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Theses and Dissertations

5-2012

Biological Strategies and Mathematical Approachesfor Limiting Bacterial Contaminants and Chemicalpollutants In Bioethanol FermentationsAlia Chahed Ep LimayemUniversity of Arkansas, Fayetteville

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Biological Strategies and Mathematical Approaches for Limiting Bacterial Contaminants and Chemical pollutants In Bioethanol Fermentations

Biological Strategies and Mathematical Approaches for Limiting Bacterial Contaminants and Chemical pollutants In Bioethanol Fermentations

A dissertation submitted in partial fulfillment of the requirements for the degree of

Doctor of Philosophy in Food Science

By

Alia (Alya) Chahed Ep Limayem

Ecole Superieur Des Industries Alimentaire de Tunis / Institut National des Recherches Agronomiques de Paris (Specialisations),

Bioengineer in Microbial and Food Industries, 1996

Laval University Master in Business Administration, 2000

May 2012 University of Arkansas

ABSTRACT

Rising concerns towards the post-petroleum paradigm, along with potential consequences from

gas emissions on climate change, have spiked interest in renewable biomass-based fuel. During

the last decade, corn-derived bioethanol has become one of the primary bioenergies used to

replace 4% of the petroleum gasoline consumed by the transport sector in the U.S. Furthermore,

the most abundant feedstock worldwide, lignocellulosic biomass is foreseen as a potential

alternative over the food-derived biofuel controversy in an effort to meet the Energy

Independence and Security Act (EISA) projections for the year 2022. However, microbial and

chemical contaminants in biofuel systems impacting ethanol productivity and the ecosystem

health have spurred researchers’ concerns to explore options to limit potential issues jeopardizing

bioethanol production. Typically, antibiotics are used to control microbial contaminants in biofuel

corn ethanol including primarily lactic acid bacteria (LAB). However, the detection of antibiotic-

resistance in large-scale bioethanol systems has led Food and Drug Administration (FDA) to

prioritize the issue. The overall objective of these research studies were to explore biological

strategies and mathematical modeling to use in biofuel systems to limit potential contaminants

from biofuel system in addition to possible indirect consequences including antibiotic-resistance

and genetically modified organisms (GMOs). The most prevalent commensal microorganisms

existing in biofuel area namely, fecal indicators (FIB) constitute an ideal vector that is able to

carry altered genetic materials and spread them throughout agriculture ecosystems. Particular

objectives of this research study were divided into 4 sections 1. To foresee an economically

feasible and broad spectrum pretreatment technique that would limit chemical pollutants from a

non-food second biofuel generation. 2. To determine natural antimicrobials over antibiotics to

limit potential microbial contaminants as well as antibiotic resistance for large-scale biofuel

systems. 3. To assess the potential adverse health effect originating from the indirect

consequences of biofuel systems (i.e., antibiotic resistance, GMOs) through mathematical

approach, microbial risk assessment (MRA). 4. To strengthen the safe-use of agricultural

biotechnology as a key-technique for improving land and water use to face the continuous

demands of increasing population. Our major outcomes proved synergistic effect of nisin, with

EDTA against the potential microbial contaminant L. casei. Reaching future prospects towards

achieving biofuel greater operational performance and public health biosafety is demonstrated to

be possible through an efficient systemic approach outputs. This scientific-based method, MRA

would help governmental agencies to set preventive measures in an attempt to protect public

health and enable industrials to counter biotechnological controversies.

This dissertation is approved for Recommendation to the Graduate Council

Dissertation Director: _______________________________________ Dr. Steven C. Ricke Dissertation Committee: _______________________________________ Dr. Philip G. Crandall _______________________________________ Dr. Ruben Morawicki _______________________________________ Dr. Michael F. Slavik

DISSERTATION DUPLICATION RELEASE

I hereby authorize the University of Arkansas Libraries to duplicate this dissertation when needed for research and/or scholarship.

Agreed _______________________________________ Alia Chahed Ep Limayem

Refused _______________________________________ Alia Chahed Ep Limayem

ACKNOWLEDGEMENTS

I would like to express my sincere gratitude to my advisor Dr. Steven Ricke for his

precious guidance and continued support that led me to reach this stage of scholarship

development. I would like to extend my deepest appreciation to my committee members: Dr.

Philip Crandall, Dr. Michael Slavik and Dr. Ruben Morawicki for inculcating me with the most

current knowledge in the fascinating world of microbiology, chemistry and with the most up-to-

date tools in food processing. Their precious help and resourceful feedback were of great benefit

to my research. I would also like to express my most sincere appreciation to Dr. Rick Ulrich who

was very helpful and offered me invaluable assistance and support.

I’m very thankful to Dr. Jean-François Meullenet to have introduced me to the outstanding

Food-Science Department at the University of Arkansas. Special thanks are also due to Dr. Betty

Martin who gave me motivation and inspiration in the field of agricultural biotechnology. My

appreciation is extended to Dr. Corliss O’ Bryan for her kind support and help. I would also like

to give my thanks to Dr. Young Min Kwon as well as Dr. Irene Hanning who have initiated me to

the molecular biology area.

I am grateful to Dr. Patrick Heist from Farm Solution Inc. who provided me with the most

recent data on bioethanol fermentation systems in the U.S. Without his input, it would have been

very difficult to conduct a viable risk analysis in the context of biofuel. The Center for Advancing

Microbial Risk assessment (CAMRA) in Michigan State University is very much thanked to have

given me the opportunity to learn the foundations of the microbial risk assessment. Particularly, I

am grateful to Dr. Joan Rose and Dr. Charles Haas for their outstanding risk analysis course.

I am especially grateful to my husband Moez for his unconditional love, considerable

support, care and patience throughout this challenging journey. To my children Sara and Karim

whose affection and smiles have given me strength to circumvent the difficult times during my

doctoral studies. I’m delighted to defend my dissertation on my mother’s birthday and I hope my

graduation will be the best gift from me to her and from me to my father.

I will never forget my true friends and particularly the persons who have been supportive

throughout my Ph.D. studies, Drs. Jo and Doug Vogel, Susan and Dr. Fred Davis and Dr. Ratul.

To all of them, I wish the best of health and success for years to come.

Dedication: To my husband Moez and two kids Karim and Sara.

TABLE OF CONTENTS

Page

CHAPTER 1 1

Introduction-Background 2

Literature Review; Publication:

Lignocellulosic Biomass for Bioethanol Production: Current Perspectives, 4

Potential Issues and Future Prospects.

Abstract 5

Introduction 6

References 45

Figures and Tables 60

CHAPTER 2 79

Alternative Antimicrobial Compounds to Control Potential lactobacillus 80

Contamination in Bioethanol Fermentations.

Abstract 81

Introduction 82

Materials and Methods 83

Results and Discussion 85

References 93

Figures and Tables 95

CHAPTER 3 100

Quantitative Risk Analysis Fundamentals in Biofuel Fermentations

Involving Antibiotic Residues and Potential Bacterial Resistance.

Abstract 102

Introduction 104

Materials and Methods 120

Results and Discussion 127

References 138

Figures and Tables 150

CHAPTER 4 165

Risk Analysis Approach for Biotechnology Applications in Biofuel Systems

Abstract 166

Introduction 167

Materials and Methods 184

Discussion and Qualitative exposure characterization 192

References 195

Figures and tables 208

OVERALL CONCLUSIONS 221

APPENDIX 223

1

Chapter One

Introduction and Literature Review

2

Introduction - Background

Little attention was focused on bioethanol production in the U.S. before 1860 when

Nicholas Otto initiated the use of ethanol as a fuel for engine combustion. As early as 1908,

Henry Ford was already aware of the promising substitute to gasoline, ethanol. This led to the

development of the Ford Model T capable of operating off of gasoline, ethanol or combinations of

both [40]. At that time, the potential for fuel ethanol received only moderate consideration due to

the dominance of low priced petroleum derived gasoline.

Interest in ethanol from biomass such as corn starch emerged in the 1970s when the price

of fossil fuel rose and methyl tertiary butyl ether (MTBE) used in gasoline was identified as an

environmental pollutant agent [41]. Moreover, the willingness of the U.S. to stay independent

from high-priced foreign oil, led the federal government to implement new research programs

directed towards the development of more sustainable alternative fuels originating from

renewable sources. Between 1980 and 1990, there was a considerable effort from the government

to boost industrial efforts towards manufacturing fuel from biomass materials by adjusting tax-

exemptions and encouraging bioethanol research and development programs. Biofuel production

grew exponentially from approximately 200 million gallons (757 million liters) in 1982 to 2.9

billion gallons (10.9 billion liters) in 2003 [42]. The PEA [1] implemented in 2005 followed by

the EISA [2] in 2007 was accompanied by a partnership between the U.S. and Brazil, the world’s

largest biofuel producer at the time.

In 2009, bioethanol-based production achieved an unprecedented increase (approximately

11 billion gallons, 41 billion liters). In the year 2010, the U.S. became the world’s leading biofuel

producer and exporter with 13.5 billion gallons (51 billion liters) nameplate capacity. Almost 200

operational corn-based ethanol plants are currently operating in 29 states [42] most of them are

3

located in the “corn belt” in the U.S. Midwest [12]. It was also reported in 2010 that despite the

global economic-burden, bioethanol production continues to expand rapidly and to contribute

significantly to the economic development of rural communities in the U.S. [42]. Although the

price of most food products has increased, corn prices have not substantially been altered.

However, the debate of whether to use plants as a fuel feedstock or as human food remains a

controversial issue. This debate has led researchers to work on more acceptable sources

containing lignocellulosic biomass that are derived mainly from agricultural residues, industrial

wastes, forest biomass and other herbaceous materials [42].

4

Literature Review

Lignocellulosic biomass for bioethanol production: current perspectives, potential issues and future prospects

Alya Limayem and Steven.C. Ricke*

*Department of Food Science and Center for Food Safety, University of Arkansas, Fayetteville,

AR 72704, USA

5

Abstract

During the most recent decades increased interest in fuel from biomass in the United States and

worldwide has emerged each time petroleum derived-gasoline registered well publicized spikes in

price. The willingness of the U.S. government to face the issues of more heavily high-priced

foreign oil and climate change has led to more investment on plant-derived sustainable biofuel

sources. Biomass derived from corn has become one of the primary feedstocks for bioethanol

production for the past several years in the U.S. However, the argument of whether to use food as

biofuel has led to a search for alternative non-food sources. Consequently, industrial research

efforts have become more focused on low-cost large-scale processes for lignocellulosic

feedstocks originating mainly from agricultural and forest residues along with herbaceous

materials and municipal wastes. Although cellulosic-derived biofuel is a promising technology,

there are some obstacles that interfere with bioconversion processes reaching optimal

performance associated with minimal capital investment. This review summarizes current

approaches on lignocellulosic-derived biofuel bioconversion and provides an overview on the

major steps involved in cellulosic-based bioethanol processes and potential issues challenging

these operations. Possible solutions and recoveries that could improve bioprocessing are also

addressed. This includes the development of genetically engineered strains and emerging

pretreatment technologies that might be more efficient and economically feasible. Future

prospects towards achieving better biofuel operational performance via systems approaches such

as risk and life cycle assessment modeling are also discussed.

Keywords: lignocellulosic feedstocks, bioethanol, fermentation, bioconversion, risk assessment.

6

1. Introduction

The agreement implemented by Policy Energy Act (PEA) [1] followed by the Energy

Independence and Security Act (EISA) [2] aims to reach 36 billion gallons (136.27 liters) of

bioethanol by the year 2022. Rising concern over depleting fossil fuel and greenhouse gas limits

has resulted in a high level of interest in non-conventional fuel originating from bio-renewable

sources including sugars, starches and lignocellulosic materials [3-8]. During the last decade, the

production of ethanol from biomass materials received more attention in the United States (U.S.)

and worldwide. In the U.S., bioethanol is primarily produced from corn starch feedstocks while

in Brazil biofuel is mainly produced from sugarcane juice and molasses. Together, these countries

account for 89% of the current global bioethanol production [9].

Several countries have initiated new alternatives for gasoline from renewable feedstocks

[10]. In the North American hemisphere, bioethanol has been extracted from starch sources such

as corn while in the South American hemisphere, biofuel has been largely provided from sugars

including sugarcane and sugar beets [11]. While European countries are deploying extensive

efforts to increase their 5% worldwide bioethanol production [12], biodiesel produced in Europe

primarily in France and Germany remains by far more substantial and accounts for

approximately 56% of the global production mainly because of the rising importance of diesel

engines and feedstock opportunity costs [13]. Although, most of the remaining countries in the

world collectively account for only 5% of the global bioethanol production, China, Thailand as

well as India are continuing to invest substantially in agricultural biotechnology and emerge as

potential biofuel producers [14-15]. In the U.S., biofuel derived from corn has emerged as one of

the primary raw materials for bioethanol production [16]. According to the renewable fuels

association [9] statistics, the production of bioethanol was historically unparalleled in the U.S. by

7

year 2009 with nameplate capacity reaching 10.9 billion gallons (41.26 billion liters) representing

55% of the worldwide production. In the year 2010 corn-based ethanol operating productions

generated a total of 12.82 billion gallons (48.52 billion liters) with the largest nameplate capacity

in Iowa (28%) followed by Nebraska (13%) [17].

Although corn-based and sugar based-ethanol are promising substitutes to gasoline

production mainly in the transportation sector, they are not sufficient to replace a considerable

portion of the one trillion gallons of fossil fuel presently consumed worldwide each year [18].

Furthermore, the ethical concerns about the use of food as fuel raw materials have encouraged

research efforts to be more focused on the potential of inedible feedstock alternatives [19-21].

Lignocellulosic biomass materials constitute a substantial renewable substrate for bioethanol

production that do not compete with food production and animal feed. These cellulosic materials

also contribute to environmental sustainability [22]. Additionally, lignocellulosic biomass can be

supplied on a large-scale basis from different low cost raw materials such as municipal and

industrial wastes, wood and agricultural residues [23]. Currently the most promising and

abundant cellulosic feedstocks derived from plant residues in the U.S., South America, Asia and

Europe are from corn stover, sugarcane bagasse, rice and wheat straws, respectively [24-27].

However, lignocellulosic-based feedstock is a recalcitrant material that requires an

intensive labor and high capital cost for processing [28]. Hence, these procedures currently are

not economically feasible. When considering enzymatic or acidic decomposition of

lignocellulosic structure, it must be taken into account that D-xylose is the second important sugar

forming the hemicellulosic portion of the plant cell wall and constituting one-third of the sugars in

the lignocellulosic feedstock [29]. However, the primary industrial yeast used in bioethanol

8

production, Saccharomyces cerevisiae converts only hexose sugars such as glucose and is not

able to co-ferment glucose and xylose [30].

There are four stages in the production of lignocellulosic-based ethanol: pretreatment,

hydrolysis, fermentation and distillation. During the past decades, there have been substantial

advances in genetic and enzymatic technologies that have helped to improve these steps of

ethanol production and expand the capability of S. cerevisiae for fermenting different sugars

simultaneously [31]. Although there is a wide range of fungal and recombinant bacteria that are

able to ferment xylose sugar, they are not all capable of adapting to fermentation-process

conditions and some of them produce only low ethanol yields. Their tolerance to ethanol and

productivity still require further refinements [32, 33]. Moreover, cellulosic materials contain

microbial contaminants that compete with the fermenting yeast for nutrients and these

contaminants can produce toxic end-products. Both of these adverse conditions can create a

considerable loss in ethanol yields [34, 35]. Additionally, pretreatment processes may result in

the formation of toxic components including primarily, acetic acid along with furfural,

hydroxymethyl furfural and phenolic components [36, 37]. However, in addition to the formation

of fermentation inhibitors during biofuel production, there is occurrence of lignin side effects on

enzymatic hydrolysis and cellulase inhibitors including primarily phenolic-derived lignin [38, 39].

Lignin and derivative effects are extensively reviewed in a later section.

This review examines what is currently known regarding recent technologies and

approaches that are used in derived-lignocellulosic biofuel production. This review also provides

a summary of the current bottlenecks and barriers that interfere with the lignocellulosic based-

ethanol pathway and places the emphasis on potential issues challenging biotechnological

conversion and bioethanol performance. Specific focus is directed towards describing current

9

solutions and possible systematic remedies that could be adopted to circumvent lignocellulosic-

derived ethanol problems and strategies for the bioethanol industry to become more economically

feasible and therefore commercially viable. Future prospects for the systematic optimization of

lignocellulosic bioconversion are also addressed.

2. Lignocellulosic sources and composition

2.1. Lignocellulosic sources

Lignocellulosic material constitutes the world’s largest bioethanol renewable resource. In

the U.S. alone the production of biomass from lignocellulosic materials is estimated to be nearly

1.4 billion dry tons per year, 30% originating from forest biomass [43]. There are several groups

of raw materials that are differentiated by their origin, composition and structure. In the U.S. most

cultivated land constitutes around 35% of the forestland, approximately 27% grazed land as well

as herbaceous and 19 % crop lands per approximately 2.25 billion acres (9.0 million km2) [44,45].

Forestland materials include mainly woody biomass namely, hardwoods and softwoods followed

by sawdust, pruning and bark thinning residues while pasture and grassland encompass primarily

agricultural residues that cover food or non-food crops and grasses such as switch grass and

alfalfa [46]. Municipal and industrial wastes are also potential recyclable cellulosic materials that

can originate either from residential or non-residential sources such as food wastes and paper mill

sludge [46, 47]. Annual total tonnage available is summarized in Table 1.

2.1.1 Forest woody feedstocks

Forest woody feedstocks account for approximately 370 million tons per year (30%) of

lignocellulosic biomass in the U.S. [43]. There are two types of woody materials that are

classified into broad categories of either softwoods or hardwoods. Softwoods originate from

10

conifers and gymnosperm trees [48] and unlike hardwoods, softwoods possess lower densities and

grow faster. Gymnosperm trees, include mostly evergreen species such as pine, cedar, spruce,

cypress, fir, hemlock and redwood [49]. Hardwoods are angiosperm trees and are mostly

deciduous [50]. They are mainly found in the Northern hemisphere and include trees such as

poplar, willow, oak, cottonwood and aspen. In the U.S., hardwood species account for over 40%

of the trees [51]. The genus Populus (cottonwood) which includes 35 species is the most

abundant fast-growing species suitable for bioethanol production. Populus deltoids species cover

most of North America from the eastern to midwestern U.S., while P. trichocarpa covers

primarily the western U.S. [52]. Unlike agricultural biomass, woody raw materials offer flexible

harvesting times and avoid long latency periods of storage [53]. Additionally, this study reported

that woody feedstock possessed more lignin than agricultural residues and less ash content (close

to zero). These unique characteristics of woody biomass including primarily high density and

minimal ash content make woody raw material very attractive to cost-effective transportation in

conjunction to its lower content in pentoses over agricultural biomass and more favorable for

greater bioethanol conversion if recalcitrance is surmounted [53]. Forestry wastes such as

sawdust from sawmills, slashes, wood chips and branches from dead trees have also been used as

bioethanol feedstocks [43].

2.1.2 Agricultural residues, herbaceous and municipal solid wastes (MSW)

Crops residues consist of an extensive variety of types. They are mostly comprised of

agricultural wastes such as corn stover, corn stalks, rice and wheat straws as well as sugarcane

bagasse [54]. There are approximately 350 to 450 million tons per year (127 million metric tons

to 317.5 million metric tons) harvested annually in the U.S. [43,55,42] with residues originating

primarily from rice and wheat straws as well as corn stalks being considered the bioethanol

11

feedstocks with the most potential. Crop residues contain more hemicellulosic material than

woody biomass (approximately 25 to 35%) [56]. Aside from being an environmentally friendly

process, agricultural residues help to avoid reliance on forest-woody biomass and thus reduce

deforestation (non-sustainable-cutting plants). Unlike trees, crop residues are characterized by a

short-harvest rotation that renders them more consistently available to bioethanol production [25,

26].

Switch grass is the primary herbaceous prairie grass and energy crop that grows in the

plains of the North American hemisphere, namely, Canada and the U.S. These perennial grasses

are of interest due to their low-cost investment as well as abundance in the U.S., their ability to

resist diseases, and their high yield of sugar substrates per acre. Moreover, switch grass is low

maintenance requiring little or no fertilization. Miscanthus giganteus is another fast-growing

grass that is a potentially optimal candidate for bioethanol production. It is native to Asia and is

grown in Europe for combustible energy use [57]. In addition to cellulosic feedstocks, municipal

and industrial solid wastes are also a potential raw material for biofuel production. Their

utilization limits environmental problems associated with the disposal of garbage household,

processing papers, food-processing by-products, black liquors and pulps [58].

Although over one billion tons of biomass per year would be potentially available to meet

the 30% replacement of petroleum-derived gasoline in 2030 [43], the high cost of biomass could

be a serious hindrance if potential lands and feedstocks are not managed and utilized efficiently

[58]. While woody biomass and agricultural residues potential was overestimated in 2005, high-

yielding energy crops including primarily Miscanthus have started to regain considerable interest

compared to woody and agricultural residues because of their potential to cover 50 to 70 % of the

total feedstock [58]. According to this study, in addition to the possible one billion tons of

12

various feedstocks that would be available, an additional cultivation of high yielding energy

crops on Conservation Reserve Program (CRP) lands that are efficiently managed would be the

key option to meet a 30% petroleum-based gasoline displacement in 2030. However, a more

recent research study concluded that bioethanol production has already reached the saturation

level just to cover the blending limit of 10% of bioethanol which could be a substantial obstacle

for further increases to reach EISA (2007) projections [59,60].

2.1.3. Marine algae

Interest in algae as a potential biofuel feedstock has existed since 1978 in the U.S. and has

recently received support by the DOE Aquatic Program [55]. Special focus was directed to assess

several aspects of algae biomass including the estimation of its productivity per acre, water

consumption and non-food feedstocks with respect to by- and co-products recovered during

biofuel production. However, improving the efficiency of algae feedstock and thus its

development as a viable and scalable source commercial enterprise remained limited during the

20th century.

More recently, marine algae biomass is regaining interest as a third generation biofuel

feedstock due to the rapid biorefineries expansion leading to a shortage on current energy crops

designated for bioethanol and biodiesel industries. Aside from being potential bioethanol

biomass, algae would also be a feedstock for other biofuels including mainly, biodiesel and fuel

for aviation in addition to other possible applications involving bio-crude oils, bio-plastics and

recovered livestock co-products [61]. Furthermore, algae feedstock with its thin cellulose layer

has a high carbohydrate composition making it capable of yielding 60 times more alcohol than

soybeans per acre of land [62]. It also provides 10 times more ethanol than corn per growing area

13

[63]. Unlike corn and sugarcane, algae biomass does not compete directly with foods and does

not require agricultural land or use of fresh water to be cultivated. It consumes a high level of

CO2 during its growth, which makes it environmentally attractive as a CO2 sink [64].

2.2. Lignocellulosic biomass composition

Lignocellulosic material can generally be divided into three main components: cellulose

(30 to 50%), hemicellulose (15 to 35%) and lignin (10 to 20%) [65-68a]. Cellulose and

hemicelluloses make up approximately 70% of the entire biomass and are tightly linked to the

lignin component through covalent and hydrogenic bonds that make the structure highly robust

and resistant to any treatment [25,67,69]. Potential lignocellulosic feedstocks and their

composition are summarized in Table 2.

2.2.1. Hemicellulose

Hemicellulose is an amorphous and variable structure formed of heteropolymers including

hexoses (D-glucose, D-galactose and D-mannose) as well as pentose (D-xylose and L-arabinose)

and may contain sugar acids (uronic acids) namely, D-glucuronic, D-galacturonic and

methylgalacturonic acids [70,71]. Its backbone chain is primarily composed of xylan β (1→4)-

linkages that include D-xylose (nearly 90%) and L-arabinose (approximately 10%) [68]. Branch

frequencies vary depending on the nature and the source of feedstocks. The hemicelluloses of

softwood are typically glucomannans while hardwood hemicellulose is more frequently

composed of xylans [70]. Although the most abundant component in hemicellulose, xylan

composition still varies in each feedstock [72]. Because of the diversity of its sugars,

hemicellulose requires a wide range of enzymes to be completely hydrolyzed into free monomers.

2.2.2. Cellulose

14

Cellulose is a structural linear component of a plant’s cell wall consisting of a long-chain

of glucose monomers linked β (1→4)-glycosidic bonds that can reach several thousand glucose

units in length. The extensive hydrogen linkages among molecules leads to a crystalline and

strong matrix structure [73]. This cross-linkage of numerous hydroxyl groups constitutes the

microfibrils which give the molecule more strength and compactness. Although starchy

materials require temperatures of only 60 to 70 °C to be converted from crystalline to amorphous

texture, cellulose requires 320 °C as well as a pressure of 25 MPa to shift from a rigid crystalline

structure to an amorphous structure in water [74]. Cellulose is the most prevalent organic

polymer and is approximately 30% of the plant composition [54]. Cotton, flax and chemical pulp

represent the purest sources of cellulose (80 to 95% and 60 to 80%, respectively) while soft and

hardwoods contain approximately 45% cellulose [56, 57, 65].

2.2.3. Lignin

Lignin is an aromatic and rigid biopolymer with a molecular weight of 10,000 Da bonded

via covalent bonds to xylans (hemicellulose portion) conferring rigidity and high level of

compactness to the plant cell wall [67]. Lignin is composed of three phenolic monomers of

phenyl propionic alcohol namely, coumaryl, coniferyl and sinapyl alcohol. Forest woody biomass

is primarily composed of cellulose and lignin polymers. Softwood barks have the highest level of

lignin (30 to 60%) followed by the hardwood barks (30 to 55%) while grasses and agricultural

residues contain the lowest level of lignin (10 to 30% and 3 to 15%, respectively) [65, 56].

Conversely, crop residues such as corn stover, rice and wheat straws are comprised mostly of a

hemicellulosic heteropolymer that includes a large number of 5- carbon pentose sugars of

primarily xylose [75]. Previously, little interest has been given to lignin chemistry potential on

hydrolysis. However, lignin components are gaining importance because of their dilution effect

15

on the process once solids are added to a fed batch hydrolytic or fermentation bioreactor in

addition to their structure and concentration effects that would affect potential hydrolysis [76].

For instance, the adsorption of lignin to cellulases requires a higher enzyme loading because this

binding generates a non-productive enzyme attachment and limits the accessibility of cellulose to

cellulase [77]. Furthermore, phenolic groups are formed from the degradation of lignin. These

components substantially deactivate cellulolytic enzymes and hence influence enzymatic

hydrolysis. This negative impact caused by lignin has led to interest in lowering the lignin

negative effect. Chen et al. (2006) [78] demonstrated that lignin modification via genetically

engineering practices targeting its biosynthetic pathways could considerably reduce lignin

formation and improve ethanol yield. However, this could be somewhat problematic as lignin

components serve as the major plant defense system to pathogen and insects and its modification

could disrupt the plants’ natural protection [79]. Retaining the lignin could have benefits as

Ladisch et al. [76] have demonstrated that lignin components, once recovered from biofuel

process may be a potential energy self-sustaining source to retain biorefineries financial solvency.

3. Pathways of bioethanol production from cellulosic feedstocks

Lignocellulosic biomass can be transformed into bioethanol via two different approaches,

(i.e. biochemical or thermochemical conversion) [80]. Both routes involve degradation of the

recalcitrant cell wall structure of lignocellulose into fragments of lignin, hemicellulose and

cellulose. Each polysaccharide is hydrolyzed into sugars that are converted into bioethanol

subsequently followed by a purification process [81, 82]. However, these conversion routes do

not fundamentally follow similar techniques or pathways. The thermochemical process includes

gasification of raw material at a high temperature of 800oC followed by a catalytic reaction.

Application of high levels of heat converts raw material into synthesis gas (syngas) such as

16

hydrogen, carbon monoxide and CO2. In the presence of catalysts, the resulting syngas can be

utilized by the microorganism Clostridium ljungdahlii to form ethanol and water can be further

separated by distillation [83].

Unlike the thermochemical route, biochemical conversion involves physical (i.e. size

reduction) or/and thermo-chemical with possible biological pretreatment [84]. Biochemical

pretreatment is mainly used to overcome recalcitrant material and increase surface area to

optimize cellulose accessibility to cellulases [53, 84, 85]. The upstream operation is followed by

enzymatic or acidic hydrolysis of cellulosic materials (cellulolysis) and conversion of

hemicellulose into monomeric free sugars (saccharification) subsequent to biological fermentation

where sugars are fermented into ethanol and then purified via distillation [81, 83]. Concurrently,

lignin, the most recalcitrant material of cell walls is combusted and converted into electricity and

heat [82]. Overall, biochemical approaches include four unit-operations namely, pretreatment,

hydrolysis, fermentation and distillation [86, 87]. Currently the biochemical route is the most

commonly used process [88]. Figure 1 adopted from Ladisch et al. [76] provides a flow diagram

illustrating the major steps involved in biochemical process with lignin co-product recovery for a

self-sufficient energy system.

3.1. Pretreatment overview

Effective pretreatment is fundamental for optimal successful hydrolysis and downstream

operations [89]. Pretreatment upstream operations include mainly physical, (i.e., biomass size-

reduction) and thermochemical processes that involve the disruption of the recalcitrant material of

the biomass. This upstream operation increases substrate porosity with lignin redistribution.

Therefore, it enables maximal exposure of cellulases to cellulose surface area to reach an effective

17

hydrolysis with minimal energy consumption and a maximal sugar recovery [53, 84, 85, 90].

Figure 2 illustrates the major outcomes from pretreatment upstream processes subsequent to

hydrolysis and fermentation operations. Zhu and Pan [53] concluded that the pretreatment

process of woody biomass differs substantially from the agricultural biomass due to differences in

their chemical composition and physical properties. Unlike woody biomass, agricultural residues

pretreatment does not require as much energy as recalcitrant woody material to reach size

reduction for further enzymatic saccharification. This study placed emphasis on the importance

of the energy consumption from the mechanical operation (size-reduction) primarily based on the

estimation of woody biomass pretreatment energy efficiency (ηPretreatment = Total sugar recovery

(kg)/Total energy consumption (MJ)). In addition to sugar recovery and ethanol yield, this energy

efficiency ratio and mass balance was deemed crucial for the complete estimation of pretreatment

efficiency [53, 91, 92, 93]. Toxic inhibitory level estimation has also been considered important

for evaluating pretreatment cost-effectiveness primarily when dilute acid is added. Costly

detoxification steps could be a major hindrance to reach high-performance pretreatment [36, 94].

Overall, the ratio including energy consumption versus sugar yield with regard to feed stock

versatility [53, 91] as well as toxic inhibitors formed per level of sugars recovered are of prime

consideration on the estimation of the pretreatment efficiency and cost effectiveness of the

operation in an effort to reach optimal conditions [95].

Several pretreatment methods, namely, mechanical, chemical or microbiological have

been used to remove the recalcitrant cell wall material of lignocellulosic biomass depending on

the raw material being extracted [95,96]. More recently, there has been considerable

advancement in development of pretreatment processes [19, 23, 96-98]. Table 3 illustrates some

of the pretreatment methods that have been examined over the years. Although most of these

18

treatments can liberate hemicellulose and cellulose from the cell wall, some of them remain

economically unfeasible due to key technical issues. Furthermore, they are not all able to

overcome the recalcitrant material found mainly in wood-based feedstocks. Typically, few

treatments are endowed with ability to overcome feedstock versatility [99,100]. Unlike

agriculture residues, forest and wood materials are high in lignin (approximately 29%) and

cellulose (approximately 44%) [56] which renders them more recalcitrant. Agricultural residues

such as corn stover, rice and wheat straws are mostly composed of hemicellulose (32%) and low

levels of lignin (3 to 13%) conferring to them a less resistant texture but a higher level of pentose

sugars rendering them less practical than woody recalcitrant material.

The most prevalent treatments include acid hydrolysis, hot water, dilute acid pretreatment

and lime [94-95,101-110]. However, the conventional methods using acidic treatments (usually

dilute sulfuric acid with concentrations below 4 wt % and temperatures greater than 160oC [111]

are always accompanied by formation of toxic inhibitors such as furfural from xylose and

hydroxymethyl furfural (HMF) from glucose in addition to phenolics and acetic acid

[36,20,95,112]. Acetic acid resulting from dilute acid pretreatment of agricultural residues as

well as herbaceous and hardwoods is pH dependent and can reach a high concentration of

approximately 10g/L [36-20] that is more difficult to separate and detoxify than HMF and

furfural. Unlike dilute acid pretreatment, ammonia fiber explosion (AFEX) treatments are

sufficient to hydrolyze primarily agricultural residues such as cornstover and have not been

associated with the formation of toxic products including HMF [99]. Given that woody feedstock

is gaining increasing attention for its attractive attributes over low-lignin materials, organosolv

along with steam explosion [113] and sulfite pretreatment to overcome recalcitrance (SPORL)

[114] have become of prime interest for their ability to degrade high-lignin forest materials [114,

19

53]. A recent study reported that steam explosion consumed the highest level of energy yielding

the lowest pretreatment energy efficiency ratio of 0.26 kg sugar/MJ when compared to organosolv

(0.31 to 0.40 kg sugar/MJ) and SPORL (0.35 to 043 kg sugar/MJ) [53]. While the organosolv

treatments degrade high-lignin woody biomass including both softwood and hardwood, they

produce considerable quantities of inhibitors namely furfural and HMF, yield a low

hemicellulosic sugar concentration and are also associated with a high capital investment [115].

Consequently, SPORL remains the most attractive candidate for its flexibility and ability to

overcome both hardwood and softwood recalcitrance with the highest sugar recovery and lowest

energy consumption [53].

3.2. Hydrolysis

The success of the hydrolysis step is essential to the effectiveness of a pretreatment

operation [82]. During this reaction, the released polymer sugars, cellulose and hemicellulose are

hydrolyzed into free monomer molecules readily available for fermentation conversion to

bioethanol [81]. There are two different types of hydrolysis processes that involve either acidic

(sulfuric acid) or enzymatic reactions [116]. The acidic reaction can be divided into dilute or

concentrated acid hydrolysis. Dilute hydrolysis (1 to 3%) requires a high temperature of 200 to

240oC to disrupt cellulose crystals [117]. It is followed by hexose and pentose degradation and

formation of high concentrations of toxic compounds including HMF and phenolics detrimental

to an effective saccharification [19]. The Madison wood-sugar process was developed in the

1940s to optimize alcohol yield and reduce inhibitory and toxic byproducts. This process uses

sulfuric acid H2SO4 (0.5 wt %) that flows continuously to the biomass at a high temperature of

150 oC to 180 oC in a short period of time allowing for a greater sugar recovery [118].

Concentrated acid hydrolysis, the more prevalent method, has been considered to be the most

20

practical approach [104]. Unlike dilute acid hydrolysis, concentrated acid hydrolysis is not

followed by high concentrations of inhibitors and produces a high yield of free sugars (90%);

however, it requires large quantities of acid as well as costly acid recycling, which makes it

commercially less attractive [119].

While acid pretreatment results in a formation of reactive substrates when acid is used as a

catalyst, acid hydrolysis causes significant chemical dehydration of the monosaccharides formed

such that aldehydes and other types of degradation products are generated [19]. This particular

issue has driven development of research to improve cellulolytic-enzymes and enzymatic

hydrolysis. Effective pretreatment is fundamental to a successful enzymatic hydrolysis [120].

During the pretreatment process, the lignocellulosic substrate enzymatic digestibility is improved

with the increased porosity of the substrate and cellulose accessibility to cellulases. Trichoderma

reesei is one of the most efficient and productive fungi used to produce industrial grade

cellulolytic enzymes. The most common cellulase groups produced by T. reesei that cleave the β

-1,4glycosidic bonds are β-glucosidase, endoglucanases and exoglucanases [115]. However,

cellulase enzymes exposed to lignin and phenolic-derived lignin are subjected to adverse effects

[121, 36, 37] and have demonstrated that phenolic-derived lignin have the most inhibitory effects

on cellulases. This study reported that a ratio of 4 mg to 1 mg peptides, reduced by half the

concentration of cellulases (i.e.β – glucosidases) from Trichoderma reesei. This strain was also

shown to be 10 to 100 fold more sensitive to phenolics than Aspergillus niger. In addition to

phenolic components effect on cellulases, lignin also has an adverse effect on cellulases. As

mentioned previously, the lignin adverse effect has two aspects including non-productive

adsorption and the limitation of the accessibility of cellulose to cellulase. Although considerable

genetic modifications (GMs) have been deployed to transform lignin effects, lignin has been

21

shown to be a potential source of self sustaining-energy and added-value components.

Consequently, several research studies have determined practical approaches in eliminating

inhibition of cellulases without involving GM approaches. Lui et al. [122] have demonstrated

that the application of metal components namely, Ca(II) and Mg(II) via lignin-metal

complexation substantially enhanced enzymatic hydrolysis. Additionally, Erickson et al. [123]

have reported the importance of additives namely, surfactants and bovine serum albumin (BSA)

in blocking lignin interaction with cellulases. Sewalt et al. [121] have reported that the adverse

effect of lignin on cellulases can be surmounted by ammoniation and various N compounds.

Moreover, the enzymatic treatment can be accomplished simultaneously with the engineered co-

fermentation microbial process known as simultaneous saccharification and fermentation (SSF)

[31, 124]. This process has been of interest since the late 1970’s for its effectiveness to minimize

cellulolytic product inhibition and subsequently increase alcohol production [124]. Typically,

separate hydrolysis and fermentation (SHF) processes involve the inhibition of the hydrolytic

enzymes (cellulases) by saccharide products such as glucose and cellobiose. Unlike SHF, the

SSF process combines hydrolysis and fermentation activities simultaneously and hence keeps the

concentration of saccharides too low to cause any considerable cellulase inhibition [111].

3.3. Fermentation

Pretreatment and hydrolysis processes are designed to optimize the fermentation process

[82]. This natural, biological pathway depending on the conditions and raw material used

requires the presence of microorganisms to ferment sugar into alcohol, lactic acid or other end

products [11, 81]. Moreover, industrial yeasts such as Saccharomyces cerevisiae have been used

in alcohol production mostly in the brewery and wine industries for thousands of years. S.

cerevisiae has also been utilized for corn-based and sugar-based biofuel industries as the primary

22

fermentative strain. Once becoming accessible for enzymatic or acidic hydrolysis, the pretreated

cellulosic slurry is subsequently converted into fermentable free sugars. The sugars are mixed

with water to form a broth. Typically, during batch fermentation S.cerevisiae ferments hexose

sugars, mainly glucose, into ethanol in a large tank via the Embden-Meyerhof pathway under

anaerobic conditions and controlled temperature. Yeast-based fermentation is always

accompanied by formation of CO2 by-products and supplemented by nitrogen to enhance the

reaction. This conventional strain is optimal at a temperature of approximately 30oC and resists a

high osmotic pressure in addition to its tolerance to low pH levels of 4.0 as well as inhibitory

products [125]. S. cerevisiae can generate a high yield of ethanol (12.0 to 17.0% w/v; 90% of the

theoretical) from hexose sugars [34, 126].

Traditionally, separate hydrolysis and fermentation (SHF) sequential steps are used in

bioethanol production. However, there is particular interest in targeting bioethanol production

that can be derived from lignocellulosic biomass materials where both hexose and pentose sugars

are available from the hemicellulose fraction. Despite its broad tolerance to stressful bioethanol

process conditions, S. cerevisiae is not able to ferment sugars other than hexose. Unfortunately,

lignocellulosic material includes a large proportion of hemicellulosic biomass that contains

mainly pentose sugars such as D-xylose [127]. Moreover, an optimal fermentative microorganism

should be tolerant to a high ethanol concentration and to chemical inhibitors formed during

pretreatment and hydrolysis process. In response to this inability of S. cerevisiae to ferment

pentose sugars, extensive efforts have been employed to develop genetically engineered

microorganisms that are capable of fermenting pentose and hexose sugars simultaneously. An

optimal fermentative microorganism should be able to utilize both hexose and pentose

simultaneously with minimal toxic end-products formation. Different techniques including SSF

23

and consolidated bioprocessing (CBP) have been developed to ensure the combination of

hydrolysis (step 3) and fermentation (step 4) in one single reactor and thus, reduce product

inhibition and operation costs. In addition to continuing downstream steps, CBP processing

integrates both fermentation and cellulase formation in one fermentative/cellulolytic

microorganism [76]. However, despite the extensive range of prokaryotic and eukaryotic

microorganisms that have been shown to be able to produce ethanol from sugars, most of them

remain limited in terms of sugars co-fermentation, ethanol yield and tolerance to chemical

inhibitors, high temperature and ethanol.

In an effort to summarize relevant advantages and major limitations of microbial fermentative

species, Table 4 compares potential microorganisms for lignocellulosic-based biofuel

fermentation including bacteria, yeasts and fungi that could be optimized and become potential

avenues to enhance alcohol yield and productivity in large-scale lignocellulosic-based ethanol

fermentation.

3.4. Separation/distillation

Bioethanol obtained from a fermentation conversion requires further separation and

purification of ethanol from water through a distillation process. Fractional distillation is a

process implemented to separate ethanol from water based on their different volatilities. This

process consists simply of boiling the ethanol-water mixture. Because the boiling point of water

(100oC) is higher than the ethanol-boiling point (78.3oC), ethanol will be converted to steam

before water. Thus, water can be separated via a condensation procedure and ethanol distillate

recaptured at a concentration of 95 % [23]. Typically, most large-scale industries and

biorefineries use a continuous distillation column system with multiple effects [128]. Liquid

24

mixtures are heated and allowed to flow continuously all along the column. At the top of the

column, volatiles are separated as a distillate and residue is recovered at the bottom of the

column.

4. Current issues and challenges of lignocellulosic bioethanol production

4.1. Overcoming recalcitrance of lignocellulosic materials

Although lignocellulosic biomass is a potential feedstock for biorefineries, its recalcitrant

structure and complexity remain a major economic and technical obstacle to lignocellulosic-based

biofuel production [129]. The resilience of lignocellulosic materials is due to their composition

and physicochemical matrix. The organization of vascular, epicuticular waxes as well as the

amount of sclerenchymatous and the complexity of matrix molecules, contribute to the

compactness and strength of the cellulosic material [89].

Furthermore, lignocellulosic materials as discussed previously are composed principally

of three components namely, cellulose, hemicellulose and lignin. Together the polysaccharides,

cellulose and hemicelluloses serve as initial substrates for subsequent saccharification and

fermentation. However, these components are encapsulated via a tight covalent and hydrogen

link to the lignin seal [98]. These tight bonds not only give the cell wall its compact structure but

limit enzyme access to the surface area. Moreover, cellulose, a polymer of glucose molecules

linked via β (1→4)-glycosidic bonds confers to cellulose a crystalline and compact structure [67].

Hemicellulose, the amorphous part of the cell wall, is composed of different hexoses and

pentose sugars including xylose and arabinose bonded through xylans β (1→4)-linkages. These

varieties of sugars polymers and linkages between molecules impose more complexities to the

cell wall and therefore the hydrolysis process necessitates numerous cost-prohibitive enzymes to

25

cleave polysaccharides entirely into fermentable sugar fragments. Additionally, components

including primarily xylo-oligosaccharides produced from hemicelluloses hydrolysis have been

shown to be inhibitory to cellulase enzymes [130]. Although xylose causes a higher level of

inhibition to cellulase enzymes than xylan, soluble xylo-oligomers are considered the most

inhibitory to cellulase and substantially influence enzymatic hydrolysis [131,132]. Hence, the

removal of these components in addition to organic acids and phenolics is desired in an attempt to

achieve an efficient cellulose conversion via enzymatic hydrolysis [76]. Thus, a successful and

low-cost ethanol bioconversion is closely related to the efficiency of the pretreatment step.

Pretreatment which is mechanical and/ or thermo-chemical, and/or a biological agent primarily

involves redistribution of lignin and improving cellulose accessibility to enzymes by increasing

the surface area that will be subjected to further hydrolysis. An effective pretreatment also

requires a reduction of energy consumption with minimum toxic inhibitory products formation

[53, 82]. However, in addition to these complexities and differences between components within

the lignocellulosic material, lignocellulose composition from each type of biomass varies

depending on the origin and geographical location. Not all types of lignocellulosic feedstocks

require the same pretreatment strategy. These heterogeneities have an important impact on the

choice of pretreatments and the downstream processes [133]. Currently, the SPORL treatment is

of interest for its broad spectrum ability on acting in both softwood and strong hardwood

materials [117, 134]. This pretreatment degrades high-lignin forest material with a limited

formation of hydrolysis inhibitors [135]. Wang et al. (2009) [134] have demonstrated that lignin

redistribution and increased porosity and surface area were achieved in only 30 minutes and was

followed by 10 hours of enzymatic hydrolysis. A small amount of 4% sodium bisulfate was

added to the solution under pH level of 2.0 to 4.5 and at a temperature of 180oC. The entire

26

conversion of cellulose to glucose sugar was accompanied by generation of low concentrations of

inhibitors (less than 20mg/g).

4.2. Potential water availability challenges for the biofuel system

Although biofuel water use is an important component to consider for the sustainability of

biorefineries, limited information is available worldwide and in the U.S. on water requirements

for the emerging agricultural practices and technologies that could impact water supplies and

quality [136]. While water availability does not pose a serious constraint in several countries

such as Brazil, Canada, Russia and some African nations, other countries including China, India,

South Africa and Turkey are already encountering scarce water issues before even considering

estimates of additional water consumption associated with biofuel production [137]. In the U.S.,

water availability could become an issue in the near future if appropriate and more effective

agricultural water sustainability practices are not implemented. To date, U.S. lignocellulosic-

based ethanol is only produced at a pilot scale level and is not yet commercially available [136].

However, this study also reported that energy corn-derived biofuel has already achieved an

exponential growth requiring an increasing availability of water in the Great Plains and other arid

regions of the country. Moreover, biofuel water availability is a very complex issue because it

varies by regions and type of crops [138]. With the increasing awareness towards the adverse

effects of biofuel system on the quality and availability of water, there has been a series of

investigations led by the U.S. National Academy of Science (NAS) to determine current

agricultural practices and their impact on water resources and quality [138]. NAS has reported

that the most important factors that cause substantial water stress due to biofuel production is the

expansion of energy crops such as corn in those areas of the U.S. Midwest that are already

susceptible to drought and hence require intensive irrigation. Although biofuel processing

27

utilizes a significant level of water, it does not consume as much water as biofuel crops.

Furthermore, biofuel crops involve a substantial use of pesticides and herbicides in addition to

fertilizers resulting in a surplus of nutrients including, nitrogen and phosphorus. This excess of

nutrients used for corn and other energy crops was demonstrated to lead to an expansion of the

“dead zone” in the Gulf of Mexico caused by oxygen depletion [139]. NAS envisions a solution

that places the emphasis on increasing irrigation-efficiency used by farmers as well as plant water

recycling. However, Huffaker [140] suggests that efforts should be directed towards improving

water quality impact rather than water recycling and irrigation efficiency. While further

expansion of cellulosic feedstock sources would be an attractive alternative within the next

decade to mitigate water supplies and reduce fertilizer use geared toward intensive crop

cultivation, a shortage of water resulting from inefficient water utilization during biofuel

processing could also jeopardize biofuel water sustainability [136].

5. Current prospects for systems approaches to biomass conversion

Current research is continuing to deploy individual and specific efforts toward achieving

optimal solutions via improving lignocellulosic-based ethanol performance with a minimum

capital investment on energy consumption and water supplies. Future prospects for the

optimization of lignocellulosic bioconversion must embrace a more systematic enhancement of

bioethanol for all four-major-steps in bioethanol production. Pretreatment as a first step is the

most costly operation and accounts for approximately 33% of the total cost [141] with respect to

the economic feasibility of each step as well as the consideration of microbial and chemical

contaminations that can potentially reduce yields. Developing genetically modified fermentative

and cellulolytic microorganisms enhanced by co-culture systems is desirable to increase ethanol

yield and productivity under the stressful conditions associated with high production bioethanol-

28

processes [142]. SSF as well as simultaneous saccharification and combined fermentation

(SSCombF) of the enzymatic hydrolysate, glucose with the hemicelluloses-derived sugars [122]

and CBP are also considered to be cost-effective and offer promise in reducing end-product

inhibition and operation numbers [124,143]. However, an overall analysis of performance would

provide a clear vision of the system conditions and allow implementation of feasible preventive

interventions aimed at enhancing biofuel production efficiency.

5.1. Overall analysis of performance: life cycle assessment (LCA) comparisons

As technologies emerge that improve various stages of biofuel production from biological

sources, there is increasing need to compare overall performance with current operational systems

to verify their validity in terms of water use and energy performance on biofuel systems as well as

the environmental impact. LCA methodologies are considered to be the analysis model of choice

for quantitatively comparing the environmental impacts of each biomass-based energy generating

system. This approach primarily focuses on the estimation of direct impacts along with indirect

and co-products credits including the carbon cycle as well as gas emission, fossil fuel

consumption, water consumption and generation of wastes involving energy utilization.

Recent studies conducted by Mu et al. [83] have analyzed and compared biochemical and

thermochemical conversion pathways based on LCA studies. They concluded that despite the

equivalent alcohol productivity and energy efficiency performance between the two routes, in the

short run biochemical conversion is considered to have a more favorable environmental

performance than the thermochemical route. LCA approaches rely on quantitative estimations of

direct (chemical pollutant agents) and indirect (greenhouse gas emissions, GHG) fossil fuel

intake, water consumption) impacts along with biomass contribution and co-product credits

29

(electricity, mixed alcohol and heat). Assessments performed by legislators on the validity of the

biomass-based energy, stipulated that a satisfactory alternative to petroleum gasoline should

achieve at least 20% reduction in GHG. Biochemical conversion of cellulosic materials was able

to achieve 50% reduction of GHG emission compared to a non-renewable fuel. The biochemical

route also saved consumption of fossil fuel resources (1.13 MJ/L) but generated chemical releases

including phosphorus and nitrogen to the atmosphere causing additional eutrophication and

acidification. While the biochemical route exhibited higher water consumption than the

thermochemical process, it did yield a better short-term environmental performance on

parameters such as GHG emissions and fossil fuel consumption. This in turn leads to a lower

impact on the environment as it uses components such as lime, sulfuric acid and nutrients that can

considerably influence LCA estimates of fossil oil, water consumption and greenhouse gas

emission. Much more detailed LCA comparisons between thermochemical and biochemical

operations have been discussed elsewhere [83].

5.2. Optimization of the biofuel process main steps

To date, various approaches have been advanced to improve the four-steps of the

bioethanol process. Pretreatment is considered the most costly operation and a major constraint

towards achieving high-yield via low-cost capital [95]. Therefore, an initial step for improvement

is crucial to the success of downstream operations. There has been considerable advancement in

pretreatment technology and several approaches are already available and successful depending

on the characteristics of the respective lignocellulose biomass source. Feedstocks richer in lignin

exhibit a high recalcitrance and resistance, thus requiring different treatment approaches from raw

materials that have a higher quantity of amorphous hemicelluloses rich in pentose sugars [144].

Hence, the inevitable feedstock versatility and variability has become a potential issue for

30

bioethanol investors. Given that ethanol is a commodity product, bioethanol plants would have

limited choices for available feedstock. This key issue has led researchers to look for a

pretreatment process able to deal with a variety of raw materials [53]. Moreover, the appropriate

treatment is also correlated to the manufacturing economics as well as lay-out and possible

investments. The selection of a suitable pretreatment relies primarily on environmental,

economical and technological factors including energy savings, wastewater, recycling issues,

substrate recovery along with a maximal solid loading yield and minimal use of chemicals [145].

Traditionally, dilute acidic pretreatment is the most commonly used method in the

bioethanol process. This upstream treatment is considered to be the most practical due to its

effectiveness at a low-cost [104,146]. However, the formation of high levels of toxic inhibitors

namely, acetic acid, HMF and phenolic components requiring an additional detoxification step

have led researchers to focus on better alternatives. Phenolic components particularly phenolic

hydroxyl groups can influence cellulase enzyme activities [53]. Consequently, it is important to

remove phenolics if enzymatic hydrolysis is to be improved. Furthermore, according to Ladisch

et al. [76], since toxic inhibitors such as aldehyde components considerably influence microbial

growth rate and volumetric productivity, selecting a fermentative culture from metabolically

modified microorganisms would improve microbial resistance to inhibitors.

Steam explosion in the presence of catalyst has gained considerable interest and

researchers are examining the potentially high correlation between catalyst concentration and

ethanol yield. Of the numerous techniques tested, Öhgren et al. [147] confirmed the effectiveness

of catalyzed steam-explosion by 3% (w/w) sulfur dioxide (SO2) pretreatment accompanied by a

cellulase and xylanase hydrolysis step at 45oC during 72 hours. These operations yielded

approximately 96% glucose and 86% xylose from residue corn stover feedstocks. The

31

Consortium for Applied Fundamentals and Innovation [148] have also demonstrated the

efficiency of SO2 steam explosion against poplar hardwoods (Populus deltoids) as it produced an

86.2% xylose yield with a final ethanol concentration of 25.9 g/L. Although SO2 could be toxic

to the environment and sulfur alone could pose potential harmful effects to some cellulolytic

enzymes and distillation, a SO2 catalyst has been demonstrated to increase enzymes accessibility

to the biomass owing to a more complete and rapid hemicellulose release [147-149].

Additionally, information is still lacking to confirm residual SO2 side effects once ethanol is used

in motor vehicles. Moreover, Hu et al. (2008) [46] reported that the acetic or uronic acid

associated to autocatalysis effects from wood pretreatment could be a better alternative to sulfuric

acid or SO2 catalysts. According to this study, despite optimal cellulases pH levels of 4.5 to 5, an

impregnation of the biomass at room temperature with an appropriate dosage of acetic acid of 1

mM corresponding to a pH level of 3.9 is feasible. This acid impregnation followed by a

pretreatment temperature at 200 OC for 10 minute would not require substantial toxic compound

removal or adverse effects to cellulolytic enzymes. Thus, acetic acid could be a potential

alternative to dissociate the biomass. However, further investigations need to be performed to

validate these assumptions.

AFEX has also been developed as another emerging economical pretreatment that limits

inhibitor formation for agricultural residues such as corn stover [19, 150,151]. Moreover,

extensive research continues to improve steam explosion with catalyst effectiveness against

recalcitrant softwood materials. Zhu et al. [114] developed a potential pretreatment SPORL to

overcome the high recalcitrance of woody biomass such as softwood material. This approach

produced readily hydrolyzed sugars and achieved excellent recovery of the hemicelluloses with

minimal generation of inhibitors. Interestingly, 87.9% of the hexose and pentose sugars were

32

recovered with the SPORL method when compared with overall saccharides recovered from

dilute acid (56.7%) [135]. The short pretreatment time period associated with this approach

permitted a low liquid-to-wood-ratio leading to a greater pretreatment energy efficiency [53].

Moreover, SPORL appears to be complementary to steam-explosion when using a catalyst and

thus improves its effectiveness against softwood biomass [135].

Different strategies including SHF, SSF as well as SSCombF have been extensively

evaluated and subsequently implemented to initiate hydrolysis of released sugar polymers. There

is some evidence that while these treatments have advantages there are disadvantages as well.

Since optimal enzymatic hydrolysis is initiated at approximately 50oC while an optimal

fermentation is enhanced at 35oC, the SHF operation appears to be more cost effective than SSF

[152]. However, the SSF pathway has the advantage of saving one step-costs in addition to its

potential to prevent cellulase inhibition by end-products such as glucose and cellobiose. From

another perspective, SSCombF improves the SSF technique by adding the co-fermentation

process as it allows saccharification along with simultaneous sugar co-fermentations in a single

reactor.

5.3. Cellulolytic/fermentative microbial ecology – Identification of indigenous candidates

Although extensive research has been devoted to lignocellulosic-based biofuel conversion

[150], less information has been provided on the microbial ecology and natural occurrence of

viable microflora in cellulosic biomaterial as well as its derived residues. Typically, an in-depth

knowledge and understanding of the ecology of the indigenous candidates could yield potential

microorganisms useful for microbially-based fermentation and cellulolytic hydrolysis in biofuel

production. However, most research efforts have focused on forestry and agricultural soil

33

microbial characteristics reflecting microbial diversity associated with these ecosystems, since

there is a mutual and close relationship between the soil-microflora and plant roots [154].

Cellulosic-containing soil consists of a wide range of microorganisms including bacteria,

filamentous fungi and wild yeasts. Synergism among these microorganisms is fundamental to the

ecological balance constituting the biomass ecosystem [155]. The nature of microorganisms as

well as the frequency and abundance vary depending on the ecological factors such as

geographical location, climate, soil and viable forms. Bacterial populations in normal fertile

agricultural soil can reach 10 to 100 million colony-forming units (CFU)/g [154]. Yeasts in soil

can range from a few to greater than a 1000 cells per gram. In southwestern Slovakia, 111 yeast

strains were isolated from 60 different agricultural soil samples. Among the wide range of

collected strains 4 genera namely, Cryptococcus, Candida, Metschnikowia and Sporobolomyces

were considered to be the most predominant [155]. This study revealed that the number of yeasts

collected from agricultural soil was ten times lower than yeasts isolated from forest soil since less

fungicide and tillage were used in the nearby forest.

Of the numerous microorganisms collected from biomass ecosystems, only a few strains

have proven to be of interest for their ethanologenic or cellulolytic abilities in bioethanol

bioconversion. In northeastern Brazil, genera such as Candida, Pichia and Dekkera were isolated

from sugarcane molasses. Despite their overall fermentative ability, these genera yielded low

ethanol concentrations in comparison to S. cerevisiae and produced acetic acid which was

inhibitory to the fermentative yeast [156]. However, some natural ethanologenic yeast species

such as Pichia stipilis, Pachysolen tannophilius, K. marxianus and Candida shehatate appeared

to have promise in replacing S. cerevisiae in lignocellulosic-based ethanol fermentation [142].

Nevertheless, these wild yeasts still require further development to survive bioethanol

34

fermentation conditions and yield an optimal ethanol concentration. The competitive exclusion as

well as repression catabolism (competitive inhibition of hexose and pentose sugar transport)

among these microorganisms in the bioethanolic ecosystem render addition of a selective agent to

not be of particular value for improving yield performance [133]. However, selective

temperatures with thermophilic yeasts including Kluyveromyces marxianus or bacteria such as

Clostridium cellulolyticum and Thermoanaerobacterium saccharolyticum may serve as

alternatives if these microorganisms are used as the major fermentative and cellulolytic agents at

high temperature operations (approximately 50°C) [157-160]. Furthermore, indigenous groups of

mesophilic and thermophilic-ethanologenic bacteria such as Zymomonas mobilis and Bacillus

stearothermophilus have proven to be promising candidates to convert sugars into ethanol [142];

however, they remain deficient as optimal ethanol producers in comparison with S. cerevisiae in

terms of resistance to high alcohol concentration and chemical inhibitors.

While a selection of indigenous bacteria and yeasts that possess fermentative abilities is

possible, fungi isolated from agricultural residues and forest woods also possess attractive

lignocellulolytic properties for initiation of the pretreatment step. In 1976, almost 14,000

cellulolytic fungi were collected from plant cell walls [161]. Only a few fungal isolates were

selected for additional research and further categorized into three groups, namely white-, soft- and

brown-rot fungi. Brown-rot fungi primarily hydrolyze the cellulose polymer, while white- and

soft-rot fungi are able to degrade most of the lignin, hemicellulose and cellulose. White rot fungi

such as Basidomycetes (e.g. Phanerochaete chrysosporium RP78) are indigenous to the northern

part of the world. P. chrysosporium is considered among the most attractive alternative fungi for

biomass processing due to their physico-chemical abilities to non- selectively break down lignin

recalcitrant material from the cell wall while liberating cellulose and hemicellulose. These fungi

35

are thermo-tolerant and can survive a temperature of 40oC [162]. Chrysosporium is also known

as a wood-decaying fungus for its unique oxidative system and has been shown to be effective on

the pre-treatment of cotton stalks [163]. Phlebia radiata, as well as P. floridensis and Daedalea

flavida belong to Basidomycetes species and are capable of selectively degrading lignin in wheat

straws and cellulosic residues [164]. Trichoderma viride, T. emersoni along with T. reesei

(Ascomyctes) and Aspergillus niger are also attractive for their cellulolytic properties, tolerance to

low pH and high temperature in addition to their ability to release large-scale cellulase enzymes

[162]. T. viride grows rapidly at a wide pH range of 2.5 to 5.0 reducing potential contamination

from other microorganisms [131,166].

Mushrooms including Volvariella species also possess hydrolytic capabilities. They have

been isolated mostly from rice straws in Asian or African countries. Lentinus edodes has also

been used in Japan and China to digest lignified residues. Aside from their ability to degrade

lignocellulosic biomaterial, some white-rot fungi belonging to the genus Pleurotus are able to

convert waste into protein for human and animal consumption [167-168].

Clostridium thermocellum, an anaerobic thermophilic microorganism, is among the rare

bacteria that possess cellulolytic properties in addition to its ability to ferment sugar polymers into

ethanol [166]. Several physiological attributes make this microorganism a promising candidate.

It has a selective growth temperature of 50°C during the fermentation process and can convert

cellulose polymer directly into ethanol yielding 0.3 g/g ethanol per converted cellulose at a high

temperature of approximately 60°C [169,170]. C. thermocellum has been considered among the

more promising thermophilic microorganisms suitable for SSF and CBP [143].

5.4. Fermentation optimization– Potential genetically modified organisms (GMO)

36

Advances in genetic engineering have been made to alter the conventional yeast, S.

cerevisiae’s capability to ferment glucose and pentose sugars simultaneously [171,172]. A S.

cerevisiae TMB3400 modified stain, designed on the basis of expressing the same gene for Pichia

stipilis xylose reductase (Ps-XR) is not only capable of co-fermenting saccharides but can also

generate less HMF products (3 times less than the initial industrial strain) [173]. As mentioned

previously, CBP is also a promising approach in combining both hydrolysis and fermentation

operations in one single vessel. Additionally, CBP bioprocessing enables genetically-modified

microorganisms that are able to produce cellulase enzyme to ferment sugars in one step and thus

prevent further investment in costly cellulolytic enzymes [143]. Furthermore, Ladisch et al. [76]

have reported that CBP could be combined with the pretreatment operation to generate lignin that

could be used as a boiler fuel and provide sufficient energy to run the process (see figure 1).

However, fermentative microorganisms must be thermo-tolerant to survive the high

temperatures of SSF/SSCombF/CBP processes. These processes can also be accompanied by a

biological treatment step that utilizes cellulolytic fungi which require high temperature and low

pH. Furthermore, Kumar et al. [111] suggested examining thermophilic anaerobic bacteria and

yeasts such as Thermoanaerobacterium saccharolyticum, Thermoanaerobacter ethanolicus,

Clostridium thermocellum and Kluyveromyces marxianus IMB3 for their potential to utilize a

wide range of feedstocks at high temperatures above 65oC. These thermophilic bacteria are able

to ferment both hexose and pentose sugars in addition to their ability to produce cellulase

enzymes and avoid the addition of commercial enzymes. Kumar et al. [111] have also reported

that Thermoanaerobacter BG1L1 had the potential to ferment corn stove feedstocks at 70°C

within an undetoxified biomass in a continuous reactor system. This thermophilic fermentation

yielded 0.39 to 0.42 g/g (ethanol per sugar consumed) and nearly 89 to 98% xylose was utilized

37

despite the low tolerance to ethanol reported by Claassen et al. [126]. Ethanol fermentation at

high temperature continues to be an emerging technology as it allows selection for

microorganisms by temperature and does not require cooling costs and cellulase addition [174].

Recently, the thermo-tolerant yeast, Kluyveromyces marxianus has been documented as an

attractive candidate due to its ability to co-ferment both hexose and pentose sugars and survive

high incubation temperatures of 42 to 45°C [175]. Moreover, K. marxianus was genetically

modified to exhibit T. reesei and A. auleatus cellulolytic activities allowing direct conversion of

cellulosic β-glucan into ethanol at 48oC under continuous conditions, yielding 0.47g/g ethanol;

92.2% from the theoretical yield and making it an ideal GMO for CBP processing[175].

The industrial potential for S. cerevisiae fermentation has already been proven for first

generation large-scale bioethanol production. The genetic improvement of the conventional

fermentative strain is gaining increasing research interest since this strain is already the most

optimally adapted to bioethanol fermentation conditions. To date, CBP for biofuel fermentation

using genetically modified S. cerevisiae is an emerging technology that has been developed in

several studies [176-178]. These studies demonstrate that in addition to its co-fermentative

genetic flexibility, S. cerevisiae can also be genetically engineered to express cellulolytic and

hemicelluloytic heterologous enzymes. Van Zyl et al. [177] demonstrated this type of

modification of S. cerevisiae by reassembling all existing components of a minicellulosome on its

membrane surface from the thermophilic microorganism Clostridium cellulolyticum via

heterologous expression of a chimeric protein scaffold under phosphoglycerate kinase 1 (PGK 1)

regulation. The successful functionality of cohesin and dockerin from C. cellulolyticum

cellulosomein S. cerevisiae proved that this genetic modification based on a minicellulosome

model may be an attractive option to the CBP process in hydrolyzing and fermenting substrates in

38

a single step. Unlike T. reesei, recombinant S. cerevisiae is not able to simultaneously control

cellulolytic enzyme expression to effectively hydrolyze cellulose. Yamada et al. [179] reported

the effectiveness of a cocktail δ- integration approach that consists of the insertion of high

cellulase activities based cassette into the yeast chromosome to optimize its cellulase expression

ratio.

Zymomonas mobilis is also among the more attractive ethalonogenic bacteria candidates

due to its high ethanol yield production and resistance to temperatures in the range of 40oC (2.5

fold higher than S. cerevisiae) [180]. Numerous genes have been introduced and heterologous

expression has been incorporated into Z. mobilis to extend its effectiveness towards other

substrates namely, xylose and arabinose since this strain is only able to ferment glucose [181].

Furthermore, the insertion of β-glucosidase gene into Z. mobilis to also convert cellobiose can be

used in the SSF process [180,182,183]. Currently, commercial companies (DuPont Danisco

Cellulosic Ethanol (DDCE) and Butalco) have assayed genetically engineered Z. mobilis and S.

cerevisiae potential for their high ethanol yield performance and adaptability [184].

Enhancing large-scale low-cost ethanol bioprocessing by biological pretreatment

involving fungi (e.g. T. reesei and a Basidiomyctes) that exhibit lignocellulolytic properties at low

pH levels and high temperatures is also a promising added-value treatment to SSF ethanol

bioconversion. While fungi bioconversion activities have been demonstrated to be slow,

optimization of potential lignocellulolytic fungi has been demonstrated possible via mutagenesis,

heterologous gene expression and co-culturing [185].

Although some of the emerging strategies and methods have proven to be promising under

different circumstances, some of these technologies remain biomaterial-type and cost dependent.

39

For example, Talebnia et al. [145] have concluded that the most suitable pretreatment for wheat

straw material was steam explosion since it required a shorter reaction time, lower chemicals and

high solid solubilization. However, this study also demonstrated that steam explosion operation

exhibited a high level of influence on the downstream operations and its success depended on the

framework of the entire process. Thus far, Binod et al. [186] hypothesized that an

environmentally friendly biological conversion approach using thermo-tolerant stains such as

Clostridium phytofermentums and Basidomycetes in SSF/CBP processings would be the future

method of choice for rice straw feedstock if slow bioconversion is to be overcome.

Furthermore, Lau and Dale, [187] have demonstrated the effectiveness of AFEX against

corn stover feedstock via SSF process, using the 424 A (LN-ST) strain of S. cerevisiae, designed

by Ho et al. [172]. This pretreatment achieved an ethanol concentration of 40.0g/L (5.1vol/vol %)

without adding nutrients or requiring washing and detoxification steps. The Consortium for

Applied and Innovation [177] team selected by the Department of Energy (DOE) office of the

Biomass program has demonstrated a higher recalcitrance of poplar wood in comparison with

corn stover. Optimal performance was achieved by a more severe treatment involving mainly

SO2 steam explosion or lime associated with the co-fermenting yeast strain 424 A (LN-ST) of S.

cerevisiae. However, a large portion of these studies focused more on sugar yield with minimal

attention given to mass balance and energy estimates crucial for a complete evaluation of

pretreatment efficiency. Zhu and Pan [53] conducted an in depth study on the impact of the

energy consumption from woody feedstock on estimating the effectiveness of potential

pretreatments. They established the benchmark based primarily on the energy consumption for

comparing the performance of the more attractive lignocellulosic biomass pretreatments

including, SPORL, organosolv and steam explosion with catalyst. They demonstrated that

40

SPORL pretreatment overall was the most advantageous and commercially scalable to sugar

recovery along with total energy consumption (physical and thermo-chemical) in addition to the

returned lignin co-product potential from softwood. Zhu et al. [91] confirmed the effectiveness of

SPORL pretreatment prior to a disc-milling operation on Lodgepole pine softwood in terms of

pretreatment energy efficiency of 0.26 kg of sugar/MJ, an ethanol yield of 276 L/ton softwood

(using thermo-tolerant, S. cerevisiae D5A), and an energy output of 4.55GJ/ton wood correlated

to the mass balance. Recent studies published by Tian et al. [188] identified the benefits from

SPORL technique over dilute acid (DA) pretreatment used for the least resistant woody biomass,

aspen (Populus tremuloides). This study revealed that SPORL pretreatment exhibited a higher

substrate enzymatic digestibility (SED) than DA and was favorable to the high ethanol yield SSF

process. Tian et al. [188] also concluded that SPORL pretreatment with 10% higher sugar and

bioethanol yield as well as a higher ethanol and sugar production energy efficiency 395 kg/GJ

over 339 kg/GJ for DA, remained one of the most attractive alternatives for low and high

recalcitrant woody material. Olofsson et al. [133] used raw spruce material to demonstrate the

importance of adopting a controlled feeding of cellulase enzymes to prevent the competitive

inhibition of sugars transport (glucose over xylose). This study demonstrated that controlled-

cellulase addition increased the total xylose uptake from 40 to 80%. Overall, sustained efforts are

still required to improve bioconversion technology towards reaching the best performance

possible to deal with lignocellulosic feedstock variability.

Improvement in each of these prospects represents individual steps towards implementing

successful cost-effective lignocellulosic-based bioethanol operations. However, to accomplish

substantial improvement will require more of a comprehensive systems approach that

41

simultaneously accounts for all inputs and outputs during the entire operation regardless of

changes in any of these individual steps.

5.5. Microbial risk assessment (MRA) modeling

5.5.1. Concepts

The use of GMOs presents another challenge to the bioethanol industry. Introduction of

such organisms into large-scale fermentation operations opens up the possibility of environmental

dissemination and potential exposure risks to public health. Likewise, industrial operations using

antibiotics to control microbial contaminants in industrial scale fermenters or as strain markers

would generate and release antibiotic resistant organisms and offer another potential

environmental public health risk [35, 189]. MRA is a comprehensive approach that can provide

guidance for reducing potential microbial public health exposure by estimating the risk of

microbial dissemination over all steps in a microbial-based process such as bioethanol formation.

MRA is an emerging systematic and science-based method generally used to provide a qualitative

and quantitative evaluation of the probability of occurrence of adverse health effects originating

from microbial hazard contamination in food products [190]. It is based on four major steps

namely, hazard identification, hazard characterization (response-dose assessment) followed by

exposure assessment and risk characterization [190]. Currently MRA is the primary science-

based tool of Codex Alimentarius on which the World Trade Organization (WTO) uses to

describe food safety and risk estimation of food products [191].

5.5.2 Application of risk assessment in large-scale fermentation systems

Applications using MRA to certify the safety and equivalence of food products in today’s

global market are still early in development. For biofermenters, MRA would be a useful tool in

42

assessing the exposure risk of using antibiotics to control large-scale microbial contamination by

evaluating major steps from the plant source to the distillation final process for potential

generation and dissemination of antibiotic resistant organisms [192]. Figure 3 illustrates a

hypothetical model system of MRA for biomass processing based on the methodology adopted by

Food and Agriculture Organization [193] of the United Nations. In this representation, the MRA

concept was applied to the lignocellulosic-based biofuel operation from harvest-to-distillation in

an attempt to design a model describing transparently dynamic microbial contamination.

Detecting microbial problems at an early stage and suppressing microbial dissemination via

selective cost-effective control measures that does not cause damage to the ecosystem is of

primary concern [189].

Rapid development of agricultural biotechnology in the early 1980’s has led to the

emergence of GMOs. Therefore, it has increased public concern on their potential hazards

including pathogenic microbial mutations and the long-term proliferation of harmful genes in the

environment that could have a serious consequence on public health and the respective

environments [194]. The awareness of the possible impact that could originate from large scale

GMO applications has encouraged work primarily from the Toxic Substances Control Act

(TSCA) on a pragmatic science-based methods such as MRA combined to biotechnology risk

assessment (BRA) to predict the probability of occurrence of adverse outcomes in the

environment from large scale GMOs based applications [195]. Thus, greater control could be

performed to improve public health and ensure comprehensive environmental safety.

6. Conclusions-Future prospects

43

Cellulosic-based biofuel is a potential alternative over food-derived bioethanol originating

mainly from cornstarch and sugarcane provided by the world’s large producers U.S. and Brazil,

respectively. Pretreatment, the most costly step is of particular concern due to the high

recalcitrance of lignocellulosic raw materials. Given that lignocellulosic feedstock is a versatile

material and bioethanol is a commodity product, it has been deemed imperative to design a

general pretreatment combination that would be effective against a wide range of cellulosic

material and hence deal with feedstock variability. For instance, researchers have shown that

pretreatments involving steam explosion with either catalyst or lime are potential candidates to

agricultural residues, herbaceous materials and hardwoods. The inability of steam explosion

combined with catalyst to degrade softwood materials can be compensated by the low-cost and

the energy efficient SPORL pretreatment approach. Emerging technologies including SSCombF

and CBP represent potential improvements as they reduce operation steps as well as chemical

inhibitors and can be enhanced by lignin, energy-self-sustaining co-products. These processes are

typically associated with thermophilic and cellulolytic microorganisms including organisms such

as T. reesei along with P. chrysosporium, K. marxianus and C. cellulolyticum with some of them

possessing fermentative abilities in addition to their hydrolytic properties. However, some

companies such as DDCE (DuPont Danisco Cellulosic Ethanol) and Butalco prefer using

genetically engineered conventional strains, S. cerevisiae and ethanologenic Z. mobilis for their

higher alcohol tolerance and yield.

In conjunction to rapid molecular biology techniques, mathematical modeling including

MRA and biotechnology risk assessment (BRA) can be used to ensure greater predictability for

limiting antibiotic resistant microflora and GMO dissemination during operation. While

technological accomplishments and multiple research coalition efforts are still progressing, an

44

efficient combination of the most advanced systems analysis and economical techniques designed

to cope with feedstock versatility and commodity should emerge as the option of choice in an

attempt to achieve optimal second-generation biofuel performance.

Acknowledgements

This review was partially supported by grants from the South Central Sun Grant (U.S.

Department of Transportation) program, Novozyme North America, Inc., Franklinton, NC, and

the Institute of Food Science and Engineering, University of Arkansas, Fayetteville, AR.

45

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Figure Caption

Figure 1. Lignocellulose substrate conversion steps for ethanol and coproducts generation. Lignin coproducts is returned for a self-energy sufficient system (adopted from [76,115])

Figure 2. Pretreatment upstream process: Major effects.

Figure 3. Hypothetical MRA model of biofuel source-to-distillation system (FAO, 2005) [194]

GMOs: Genetically modified organisms; CTs: Contaminants including antibiotic resistance organisms

61

Figure1. Lignocellulose substrate conversion steps for ethanol and coproducts generation. Lignin coproduct is returned for a self-energy sufficient system (adopted from ([76,115])

62

Figure.2. Pretreatment upstream process: Major effects.

Lignocellulosic 

feedstock 

Major targeted 

components: 

 

- Lignin - Hemicelluloses - Cellulose

Major changes: - Lignin redistribution. - Increased porosity of the lignified cell-wall. - Size- reduction. - Increased surface area of hemicelluloses and cellulose for greater enzymes accessibility

‐ Hydrolysis ‐ Fermentation ‐ Distillation

Bioethanol

 

Pretreatment:   

 

‐ Mechanical 

       and/or 

‐Thermo‐chemical 

       and/or 

‐ Biological 

 

Energy consumption

% of toxic by‐

products 

released 

depends on 

pretreatment 

type 

63

Figure 3. Hypothetical MRA model of biofuel source-to-distillation System (FAO, 2005) [194]

GMOs: Genetically modified organisms; CTs: Contaminants including antibiotic resistance organisms.

GMOs and CTs 

GMOs and CTs 

GMOs and CTs 

GMOs and CTs 

 

Pretreatment

 

Source 

 

Fermentation 

 

Distillation 

 

Hydrolysis 

 

 

Holistic Risk   Characterization

 

 

C 

O 

N 

T 

A 

M 

I 

N 

A 

N 

T 

S 

 

A 

N 

D 

 

G 

M 

 O’ 

S 

 

 

 

 

 

 

 

C 

O 

N 

T 

A 

M 

I 

N 

A 

N 

T 

S 

 

A 

N 

D 

 

GM    

 O’ 

S 

 

P 

Potential 

releases 

Potential 

releases 

64

Table Caption

Table 1. Annual Total Tonnages of Biomass for Biofuel in the U.S. (U.S. Department of Energy Biomass Program, 2009)[55]

Table 2. Potential Lignocellulosic Biomass Source and Composition (% dry weight)

Table 3. Pretreatment Methods and Key Characteristics.

65

Table 1. Annual Total Tonnages of Biomass for Biofuel in the U.S. (U.S. Department of Energy Biomass Program, 2009)[55]

Biomass Million Dry Tons/Year

Agricultural Residues 428

Forest Resources 370

Energy Crops 377

Grains and Corn 87

Municipal and industrial

Wastes 58

Others (i.e., oilseeds) 48

Total 1368

66

Table 2. Potential Lignocellulosic Biomass Source and Composition (% dry weight)

Raw Material

Hemicelluloses Cellulose Lignin

Others

(i.e., Ash)

Reference

s

Agricultural

residues 25-50 37-50 5-15 12-16

[55,64,193

,14]

Hardwood 25-40 45-47 20-25 0.80

Softwood 25-29 40-45 30-60 0.50

Grasses 35-50 25-40 _a _

Waste papers from

chemical pulps

12-20 50-70 6-10 _

Newspaper 25-40 40-55 18-30 _

Switch grass 30-35 40-45 12 _

a Not present

67

Table 3. Pretreatment Methods and Key Characteristics.

Pretreatments

Key Characteristics

References

Dilute Acid (H2SO4, HCL (0.5 to 5% )

- Practical and simple technique. Does not

require thermal energy.

- Effective hydrolyze of hemicelluloses

with high sugar yield.

- Generates toxic inhibitors

- Requires recovery steps

[81,95,105,107,108,198,199]

Hot water

- The majority of hemicelluloses can be

dissolved.

- No chemicals and toxic

inhibitors.

- Average solid load.

- Not successful with

softwood.

[46, 94,96-98, 110, 200,201]

68

Lime

- High total sugar yield including pentose

and hexose sugars.

- Effective against hardwood and

agricultural residues.

- High pressure and temperature hinder

chemical operation.

- Commercial scalability

problem

[53,109,201]

Ammonia Fiber Expansion (AFEX)

- Effective against agricultural residues

mainly cornstover without formation of toxic end-products.

- Not suitable for high-lignin

materials.

- Ammonia recovery

- No wastewaters

[19, 120, 124, 150, 153,187]

69

Ammonia Recycle Percolation (ARP)

- High redistribution of lignin

(85%)

- Recycling ammonia

- Theoretical yield is attained

[26,204,205]

Steam Explosion with Catalyst

-Effective against agricultural residues

and hardwood.

- High hemicelluloses fractions removal

- Not really effective with

softwood

[108, 124, 206-208]

Organosolv

- High yield is enhanced by acid

combination.

- Effective against both hardwood and

softwood.

- Low hemicellulosic sugar concentration

- Formation of toxic inhibitors

- Organic solvent requires

[207,209]

70

recycling

- High capital investment

Sulfite Pretreatment Top Overcome Recalcitrance (SPORL)

- Effective against high-lignin materials,

both softwood and hardwood.

- Highest pretreatment energy efficiency

- Minimum of inhibitors

formation

- Accommodate feedstocks

versatility.

- Steam explosion combined to SPORL

in presence of catalyst becomes effective against softwood materials

- Cost-effective.

[53, 91, 92, 114, 134, 135, 188]

Ozone

- Effectively remove lignin from a wide

range of cellulosic material without

generating inhibitors.

[19]

71

- Expensive

Alkaline Wet Oxidation

- The combination of oxygen, water, high

temperature and alkali reduce toxic

inhibitors.

- High delignification and solubilisation

Of cellulosic material

- Low hydrolysis of oligomers

[99,207]

Fungal Bioconversion

- Environmentally Friendly

- Low use of energy and chemical

- Slow bioconversion

[185,211]

 

72

Table 4. Advantages and Drawbacks of Potential Organisms in Lignocellulosic-based Bioethanol Fermentation

Species Characteristics

Advantages Drawbacks References

Saccharomyces cerevisiae

Facultative anaerobic yeast

- Naturally adapted to ethanol

fermentation.

- High alcohol yield (90%).

- High tolerance to ethanol ( up to

10% v/v) and chemical

inhibitors.

- Amenability to genetic

modifications

- Not able to ferment

xylose and arabinose

sugars.

- Not able to survive

high temperature of

enzyme hydrolysis.

[70]

[145]

[212]

[82]

[125]

[213]

Candida Micro-aerophilic

- Low tolerance to [70]

73

shehatae Yeast

- Ferment xylose

Ethanol

- Low yield of ethanol.

- Require micro-

aerophilic conditions

- Does not ferment

xylose at low pH

[214]

[96]

[215]

Zymomonas

mobilis

Ethanologenic Gram-negative bacteria

- Ethanol yield surpasses S.

Cervesiae (97% of the

theoretical),

-High Ethanol tolerance ( up

to14% v/v)

- High ethanol productivity (five-

fold more than S.

- Not able to ferment

xylose sugars.

- Low tolerance to

Inhibitors

- Neutral pH range

[216]

[217]

[70]

74

cerevisiae

volumetric productivity)

- Amenability to genetic

modification.

-Does not require additional

oxygen

Pichia stiplis Facultative anaerobic yeast

- Best performance xylose

fermentation.

- Ethanol yield (82%).

- Able to ferment most of

cellulosic-material sugars

including glucose, galactose and

- Intolerant to a high

concentration of

ethanol above 40g/l

- Does not ferment

xylose at low pH

- Sensitive to

chemical

inhibitors.

- Requires micro-

aerophilic

[70]

[218]

[214]

[219]

75

cellobiose.

- Possess cellulase enzymes

favorable to SSF process.

conditions

to reach peak

performance

- Re-assimilates

formed ethanol

Pachysolen

tannophilus

Aerobic

fungus - Ferment xylose

- Low yield of

ethanol.

- Require micro-

aerophilic

conditions

- Does not ferment

xylose at low pH

[214]

[220]

Esherichia

coli

Mesophilic Gram-negative bacteria.

- Ability to use both pentose and

hexose sugars.

-Repression

catabolism interfere

to co-fermentation

-Limited ethanol

tolerance

[82]

[220]

[33]

76

-Amenability for genetic

modifications

-Narrow pH and

temperature growth

range

- Production of

organic acids

-Genetic stability

not proven yet

- Low tolerance to

inhibitors and

ethanol

Kluveromyces marxianus

Thermophilc Yeast

- Able to grow at a high

temperature above 52oC

- Suitable for SSF/CBP process

- Reduces cooling cost

- Excess of sugars

affect its alcohol

yield

- Low ethanol

tolerance

- Fermentation of

xylose is poor and

leads mainly to

[157]

[111]

[184]

77

- Reduces contamination

- Ferments a broad spectrum of

sugars.

- Amenability to genetic

modifications

the formation of xylitol

Thermophilic

Bacteria:

Thermoanaerobacterium Saccharolyticum

Thermoanaerobacter ethanolicus

Clostridium thermocellum

Extreme anaerobic bacteria

- Resistance to an extremely high

temperature of 70oC.

- Suitable for SSCombF/CBP

Processing

- Ferment a variety of sugars

- Display cellulolytic

- Low tolerance to

ethanol

[222]

[111,158,1

59]

[97]

78

activity

- Amenability to genetic modification.

79

Chapter Two Alternative antimicrobial compounds to control potential Lactobacillus contamination in bioethanol fermentations

80

Alternative antimicrobial compounds to control potential Lactobacillus contamination in bioethanol fermentations A. Limayem1, I. Hanning1, A. Muthaiyan1, J.-W. Kim2, P.G. Crandall, C. A. O’Bryan and S.C. Ricke1 1 University of Arkansas, Fayetteville, AR 2 Department of Biological and Agricultural Engineering and Institute for Nanoscale Materials Science and Engineering, University of Arkansas, Fayetteville, AR

81

ABSTRACT

Antibiotics are commonly used to control microbial contaminants in yeast-based bioethanol

fermentation. Given the increase in antibiotic-resistant bacteria, alternative natural

antimicrobials were evaluated against the potential contaminant, Lactobacillus. The effects of

nisin, -polylysine, chitosan (CS) and lysozyme were screened against 5 Lactobacillus strains.

A standard broth- microdilution method was used in 96-well plates to assess the minimal

inhibitory concentration (MIC). L. delbrueckii subsp lactis ATCC479 exhibited maximal MICs

with CS, -polylysine and nisin (1.87, 0.3125 and 0.05 mg/mL, respectively). Nisin reduced

most Lactobacillus strains by 6 log CFU/mL after 48 hours with the exception of L. casei.

Synergism occurred when ethylenediaminetetraacetic acid (EDTA) was added with nisin. An

MIC of 0.4 mg/mL of nisin combined with the EDTA at an MIC of 1 mg/ml markedly

suppressed L .casei by 6 log CFU/mL. In conclusion, alternative antimicrobials proved to be a

potential candidate for controlling bacterial contamination in the fermentation process.

Synergistic effect of nisin with EDTA successfully inhibited the nisin-resistant contaminant, L.

casei.

Keywords: Yeast fermentation; Contaminants; Lactobacillus;

Bioethanol; Antimicrobials; Nisin.

82

1. INTRODUCTION

Bioethanol production could attain 90 billion gallons (340.69 billion liters) by 2030 in

the United States,[1] although in 2009 only 10.6 billion gallons (40.2 billion liters) was

produced.[2] Current focus lies in bioethanol that is made from highly fermentable and

renewable carbohydrate sources, originating from plants and agricultural-based sources from

crops such as corn-starch or sugarcane. Industrial fermentation of carbohydrate feed stocks by

yeast is commonly used to produce ethanol. Saccharomyces cerevisiae is one of the most

common ethanol-producing organ- isms used extensively in bakery as well as in biotechnology

and most of alcohol industries.[3] Bacterial contaminants constitute a problem for bioethanol

production by competing with the yeasts for micronutrients.[4] These contaminants can also

produce end-products such as organic acids which inhibit the growth of the yeasts.[5]

Suppressing microbial contamination is one of the major challenges in yeast fermentation

processing.[6] In corn- based fuel ethanol processing plants in the United States, Lactobacillus

species have been identified as the most abundant contaminant constituting between 36 and 77%

of all isolates.[7] Skinner and Leathers[4] also reported that Lacto- bacillus species were the

most important isolates from the wet mill and dry grind originating from corn-based feed- stock

varying from 38 to 77%, respectively. Currently, antibiotics including penicillin and

virginiamycin are used in fermentation systems to control contamination problems associated

with these organisms.[7] However, the emergence of antibiotic-resistant bacteria that could serve

as a major threat to animal and human health has led researchers to examine alternative natural

antimicrobials for agricultural applications.[8] There is a need for such alternatives in the ethanol

biofuel industry because of the large-scale nature of the operations and need for continued

83

control of bacterial contamination.[6] Potent and practical antimicrobial compounds that can be

incorporated with the raw materials are needed to control microbial contamination during

industrial ethanol production.

The overall goal of the present study was to assess a selected variety of natural

antimicrobials that are known to have activity against bacteria. The specific focus of this study

was to investigate the effect of natural antimicrobials (nisin, -polylysine, chitosan and lysozyme) that

are both practical and sufficiently broad spectrum for large-scale yeast fermentation systems on

Lactobacillus species.

2. MATERIALS AND METHODS

2.1Bacterial strains and Culture Condition

A group of 5 Lactobacillus strains was chosen and used as the target bacteria for this

study. Lactobacillus strains used for this study were: L. plantarum ATCC8014, L .casei

ATCC11578, L. delbrueckii subsp. lactis ATCC4797, L. plantarum WCFS1 and L. sakei 23K.

These strains were maintained in de Man, Rogosa and Sharp (MRS; Difco Laboratories)

medium at 37◦C. The S. cerevisiae strain utilized in this study was kindly donated by Dr.

Lindquist, Department of Bacteriology at the University of Wiscon- sin in Madison. A series of

experiments were conducted to evaluate the growth of pure cultures of the S. cerevisiae and

Lactobacillus strains. This was followed by stud- ies where S. cerevisiae was co-cultured with

Lactobacillus in Potato dextrose broth (PDB; Difco Laboratories) and both populations were

enumerated on selective plates, MRS and PetrifilmTM, respectively (Becton, Dickinson and

Company, Sparks, MD; 3M Biologicals, St. Paul, MN).

84

2.2 Growth Measurement

Minimal inhibitory concentrations (MICs) for Lactobacillus strains and S. cerevisiae were

determined for the natural antimicrobials, nisin, ε-polylysine, chitosan and lysozyme (Sigma,

MO, USA), at initial concentrations of 0.4 mg/mL, 10 mg/mL, 2.5 mg/mL and 100 mg/mL

respectively. A standard broth-microdilution method utilizing 96-well microtiter plates was used

to estimate the MIC values. Each well was filled with 50 µl of MRS broth and 50 µl of the

respective antimicrobial. Successive serial 2-fold dilutions were made, then each well was

inoculated with 50 µl of Lactobacillus (approximately 6 log CFU/ mL). The microbial growth

was evaluated after 24h and 48h for Lactobacillus by visually determining the last detectable

turbid well of each antimicrobial which indicated cellular growth at the MIC.

The inhibitory effect of nisin was evaluated against S. cerevisiae cultured in PDB and L.

casei cultured in MRS. S. cerevisiae was also co-cultured with L. casei in PDB. Bacterial and

yeast viability in antimicrobial-amended me- dia were evaluated by enumeration at regular time

intervals from 0 to 48 hours, on MRS-agar and PetrifilmTM yeast and mold. Counts on

PetrifilmTM Yeast and Mold were subtracted from counts on MRS-agar to yield L. casei counts.

2.3 Chitosan-pentasodium-triphosphate (CS-TPP) nanoparticle design and preparation

The polysaccharide biopolymer chitosan was also tested as an antimicrobial not only because of

its biological and physicochemical properties but also because of its potential to serve as an

inexpensive nanoparticle platform. Chitosan was attached to antimicrobials based on ionic

gelation to form nanoparticles with chitosan-pentasodium tripolyphosphate (CS-TPP) anion.

Chitosan (CS) and pen- tasodium tripolyphosphate (TPP) were purchased without any

85

purification from Sigma-Aldrich (St. Louis, MO) with a degree of deacetylation and viscosity of

92% and 32 cps, respectively. The stock solution of CS was dissolved in 1% (v/v) of acetic acid

at 2 mg/mL. The CS solution was then adjusted to a pH of 4.8 using 10 N NaOH. The TPP

solution (pH 9.0) was prepared by dissolving TPP in distilled water at a concentration of 1.65

mg/mL. The synthesis of CS-TPP nanoparticles was made according to Cuna et al. (2006) with

minor modifications. CS solution (9 mL) was added to 3.6 mL of TPP solution and further

stirred, the gelation of CS-TPP occurred spontaneously, which resulted in a 3:1 ratio of CS to

TPP. The resultant CS-TPP nanoparticles were washed 3 times with 10 mL double dis- tilled

water after separating them by centrifugation at 8,000 g for 30 min at 4◦C. At the end of the third

centrifugal separation, the nanoparticles were re-suspended with double distilled water at the

final volume of 2 mL.

2.4 Statistical Analysis

All data were analyzed using the statistical software SAS (Statistical Analysis Systems

Institute, Cary, NC, USA). The GLM procedure and Least Significant Difference was used to

determine differences among means of survival of culturable S. cerevisiae and L. casei due to

treatment. This test was also performed to assess the differences among means of MICs of the

series of antimicrobials tested. All experiments consisted of three replicate samples within each

trial and were repeated in three independent trials.

3. RESULTS AND DISCUSSION

The MIC values of the antimicrobials tested after 24 and 48 hours incubation at 37◦C are shown

in Table 1. Chitosan alone was effective against most of Lactobacillus strains with a minimum

86

MIC of 1.25 mg/mL and a maximum of 1.875 mg/mL. However, chitosan MICs were

significantly higher than the minimal MICs of -poly-L-lysine and nisin which were 0.0015 mg/mL

and 0.156 mg/mL, respectively (P 0.05). Furthermore, there was no inhibition of most of Lactobacillus

strains by the CS-TPP complex. Additionally, there was also no inhibition by lysozyme against

all Lactobacillus strains with the exception of L. delbrueckii subsp lactis after the second day

with an MIC of 100 mg/mL (Table 1). L. plantarum ATCC 8014 exhibited minimal MICs with

the -poly-L-lysine (0.156 mg/mL) and nisin (0.00156 mg/mL). Although the MIC of nisin was

lower than the -poly-L-lysine MIC against L. plantarum ATCC 8014, this difference was not

considered statistically significant because the least significant difference (LSD = 0.1977) was

higher than the difference between -poly-L- lysine and nisin MICs (0. 1544). However, L. casei

was not

affected by the nisin alone and inhibited by -poly-L-lysine only at a relatively high MIC of 1.25

mg/mL. The MIC of -poly-L-lysine for L. delbrueckii subsp lactis was significantly higher than the MIC

of nisin which were 0.3125 and 0.05 mg/mL, respectively (P 0.05). Additionally, the MICs of

nisin for L. plantarum ATCC 8014, L. plantarum WCFS1 and L. sakei (0.00156 mg/mL, 0.0125

mg/mL and 0.0125 mg/mL) were significantly lower (P 0.05) than those of -poly-L-lysine against

the same strains (0.156 mg/mL, 0. 156 mg/mL and 0.23425 mg/mL). However, L. casei was not

affected (P > 0.05) by the nisin at a high concentration of 0.4 mg/mL. S. cerevisiae also was not

affected by any of these compounds (Data not shown). Although the MICs of nisin were the

lowest and the most effective for most of Lactobacillus stains, L. casei was particularly not

inhibited by nisin alone, whereas all other target strains were completely inhibited (P 0.05).

Nisin alone did not affect L. casei. Growth responses of L. casei and S. cerevisiae mono

and co-culture in the presence of 0.4 mg/mL of nisin are shown in Table 2. After 24 hours, L.

87

casei monoculture reached a growth of 9.43 log CFU/mL. S. cerevisiae monoculture was also

able to grow and reach a growth of 9.22 log CFU/mL after 24 hours and 9.36 log CFU/mL after

48 hours. Furthermore, L. casei co-cultured with S. cerevisiae grew to 6.5 log CFU/mL after 24

hours and remained constant after 48 hours which was 3 logs lower than the growth of L. casei

cultivated alone in the MRS media that reached a growth of 9.43 log CFU/mL after 24 hours and

9.45 mg/mL after 48 hours (P 0.05). Moreover, S. cerevisiae co-cultured with L. casei enumerated in a

Petrifilm selective media was not affected by nisin under these conditions reaching 8.15 log

CFU/mL after 24 hours and 7.20 log CFU/mL after 48 hours; S. cerevisiae exhibited

significantly more growth than when it was monocultured, i.e., 9.22 log CFU/mL after 24 hours

and 9.36 log CFU/mL after 48 hours (P 0.05). The end- products such as lactic acid produced by

L. casei could be involved in growth reduction of co-cultured S. cerevisiae.

L. casei growth responses to nisin (0.4 mg/mL) and EDTA (1 mg/mL) media

amendments are shown in Table 3. L. casei growth was not affected by a high concentration of

nisin of 0.4 mg/mL alone (P > 0.05) after 24 (8.27 log CFU/mL) and it was slightly affected

after 48 hours (7.53 mg/mL). Although EDTA alone did not inhibit L. casei, no growth was

perceived for this strain after 24 and 48 hours (7.0 log CFU/mL and 7.13 log CFU/ mL,

respectively) in comparison with the control (7.0 log CFU/mL). Nisin alone did not affect L.

casei after 24 hours. However, the combination nisin-EDTA (0.4mg/mL- 1mg/mL) inhibited L.

casei after 24 and 48 hours to below detection levels (Table 3).

Growth responses of mono and co-cultured L. casei and S. cerevisiae are shown in Table

4. L. casei monoculture without the addition of antimicrobials yielded detectable growth after

24 and 48 hours in the MRS media (9.20 log CFU/mL and 9.90 log CFU/mL, respectively). S.

cerevisiae monoculture grew to 7.15 log CFU/mL after 24 hours and 8.18 log CFU/mL after 48

88

hours. However, L. casei co-cultured with S. cerevisiae was completely suppressed by the

combination of nisin-EDTA (0.4 mg/mL-1mg/mL). This combination indicated synergistic

activity between nisin and EDTA (Table 4). Furthermore, S. cerevisiae co-cultured with L. casei

was not affected by the nisin-EDTA combination after 24 and 48 hours (7.2 log CFU/mL and

7.56 log CFU/mL, respectively). Additionally, S. cerevisiae co-cultured with L. casei under the

effect of nisin-EDTA was able to grow similarly to S. cerevisiae monoculture (control) (7.15

logCFU/mL and 7.20 log CFU/mL, after 24 hours, respectively).

Biological compounds compared in this study—namely lysozyme, chitosan, polylysine,

EDTA and nisin—have all been reported to be effective against a broad spectrum of

microorganism. Lysozyme belongs to a family of enzymes also called N-acetylmuramide

glycanhydrolases and is also known as muramidase, possessing a hydro-catalytic activity against

the glycan portion of the cell wall. Chassy and Guiffrida[9] demonstrated its potential

antimicrobial activity against Gram-positive bacteria. Rogosa[10] reported that lysozyme

activity was not growth phase dependent. Gram-positive bacteria are sensitive to lysozyme

during the exponential phase, whereas they were insensitive during the stationary phase.

However, no inhibitory effects against Lactobacillus were detected during the first day for most

of the strains and only a limited reduction was detected after the second day for the L.

delbrueckii subsp lactis (Table 1). Wasserfall and Teuber[11] demonstrated that lysozyme was

effective against some Gram-positive strains including Clostridium tyrobutyricum spores found

in cheese. Gram- positive bacteria can be resistant to lysozyme because of their thicker and

denser peptidoglycan layer. Chassy and Guiffrida[9] demonstrated that lysozyme diluted in tris

(hydroxymethyl) aminomethane-hydrochloride buffer containing polyethylene glycol (PEG)

could lead to lysis of a wide range of Gram-positive bacteria including Lactobacillus,

89

Propionibacteria and Pediococcus. Additionally, it has been demonstrated that lysozyme can

become more effective when it is combined with another antimicrobial such as nisin.[12]

The ability of chitosan to serve as both an antimicrobial and anti-biofilm agent against Gram-

positive bacteria has been previously demonstrated.[13] The studies here revealed a relatively

high chitosan MIC of 1.875 mg/mL for L. delbrueckii subsp lactis when compared to nisin and

-poly-L-lysine, 0.05 and 0.31mg/mL, respectively (P 0.05). Chitosan has a unique nanoparticle character

to serve as a protein delivery system. Chitosan has also been of interest because of its high

antibacterial and antifungal activity. The effect of chitosan nanoparticles and copper-loaded

nanoparticles against E. coli, Salmonella Choleraesuis, S. Typhimurium and Staphylococcus

aureus growth has been demonstrated.[14] It has been reported that the most useful way to

create chitosan nanoparticles was through pentasodium-triphosphate (TPP), which is a small

negatively charged ion.[15] The effectiveness of these nanoparticles is highly enhanced when

they are combined with an antibiotic such as sulfamethoxazole. The synergistic effect of

chitosan and the antimicrobial sulfonamide was demonstrated against the highly antibiotic

resistant Pseudomonas aeruginosa.[16] Lifeng et al.,[14] also demonstrated that CS-TPP

nanoparticles were very effective against S. Choleraesuis at an MIC of 0.25 µl/mL and equally

effective against E. coli, S. Typhimurium and S. aureus. However, this study did not reveal any

effectiveness of chitosan nanoparticles against Lactobacillus strains.

Although -poly-L-lysine was effective against Lactobacillus strains in this study, its MIC of

0.31 mg/mL against L. delbrueckii subsp lactis remained higher than the MIC of nisin of 0.05

mg/mL. Polylysine (-poly-L-lysine) is defined as a straight chain polymer of L-lysine produced

from soy milk or milk based raw materials. It has an inhibitory effect against a wide range of

microorganisms including Gram-negatives such as E. coli and Salmonella in addition to Gram-

90

positive including Listeria.[17] It functions by electrostatic adsorption to the cell surface of

bacteria on the basis of its polycation properties, which leads to the destabilization of the outer

membrane and consequently disruption of the cytoplasm.[18]

After 48 hours of incubation, nisin was effective against all strains of Lactobacillus

except L. casei. Furthermore, the data indicates that nisin did not affect yeast growth co-cultured

with L. casei. This finding was consistent with studies that stated that although most lactic-acid

bacteria are suppressed by the nisin, some strains of L. casei can survive and remain resistant

even to a high concentration of nisin.[19] Nisin was given GRAS status in 1969 for use in food

and has received specific attention for its effect on Listeria monocytogenes.[20] The

antimicrobial mechanism of nisin has been extensively studied and well-documented for its high

activity against Gram-positive bacteria and for its qualification as a safe preservative to be used

in food.[21] Its effect is more substantial at pH levels above 4 that are more favorable for

bacterial growth[19]. The effectiveness of nisin against Gram-negative cells is generally low due

to the inability of nisin to penetrate the cell wall, which prevents access to the inner membrane.

L. casei was the only Lactobacillus strain that was not reported inhibited by nisin alone. EDTA

is a chelating agent that has been widely used to weaken the outer membrane of Gram- negative

bacteria. In these studies, EDTA alone was relatively ineffective but markedly effective against

the L. casei strain when it was combined with nisin. The effectiveness of chelators such as EDTA

used in combination with antimicrobials including nisin or lysozyme has been demonstrated

against some Gram-negative bacteria including E. coli but is found to be less effective against

Pseudomonas fluorescens and S. Enteritidis.[22] The potential for synergism of EDTA with nisin

against Gram-negative and Gram-positive bacteria including Brochothrix, Pseudomonas sp.,

Enter- obacteriaceae and lactic acid bacteria has been assessed; it was discovered that EDTA-

91

nisin (500 IU/g: 50 mM) treatments increased the shelf life of fresh chicken meat up to 20 and

24 days storage at 4◦C in a modified atmosphere by suppressing these spopilage bacteria.[23]

4. CONCLUSIONS

Nisin was the most successful of the natural antimicrobials tested against target Lactobacillus

strains in bioethanol fermentation. It was effective against most of Lactobacil- lus strains except

L. casei. EDTA alone was not effective against nisin-resistant strain, L. casei but it had a

synergistic effect with nisin against this target strain. Nisin resistance is attributed to being

caused by the L. casei physiological induction reported by Breuer and Radler.[23] The

synergistic activity of EDTA with nisin may be explained by EDTA first disrupting the

cell wall, as it allows nisin access to cytoplasmic membrane causing cell death. Hence, nisin was

shown to be a potential natural antimicrobial to prevent microbial contaminations in large-scale

bioethanol fermentation system.

92

ACKNOWLEDGMENTS

The financial support provided by a South Central Sun Grant from the US Department of

Transportation pro- gram and Novozymes, Inc (77 Perry Chapel Church Road Franklinton, NC

27525 USA) are thankfully acknowledged. The authors also thank H.-M. Moon (Department of

Biological and Agricultural Engineering, University of Arkansas, Fayetteville, AR) for his

assistance with NP synthesis and AFM imaging.

93

References

[1] Anonymous. Biofuels can provide viable, sustainable solution to reducing petroleum dependence, say Sandia researchers, 2009. https://share.sandia.gov/news/resources/news releases/biofuels- can-provide-viable-sustainable-solution-to-reducing-petroleum- dependence-say-sandia-researchers/ (Accessed November, 2010)

[2] Renewable Fuels Association. 2010 Ethanol Industry Out- look. http://www.ethanolrfa.org/page/-/objects/pdf/outlook/RFAoutlook2010 fin.pdf ?nocdn=1 (Accessed February, 2011).

[3] Walker, G.M. Yeast physiology and biotechnology; John Wiley and Sons: Toronto, Canada, 1998; 362 pp.

[4] Skinner, K.A.; Leathers, T.D. Bacterial contaminants of fuel ethanol production. J. Ind. Microbiol. Biotechnol. 2004, 31, 401-408. [5] Bayrock, D.P.; Ingledew, W.M. Inhibition of yeast by lactic acid bacteria in continuous culture: nutrient depletion and/or acid toxicity? J. Ind. Microbiol. Biotechnol. 2004, 31, 362-368. [6] Muthaiyan, A.; Ricke, S.C. Current perspectives on detection of microbial contamination in bioethanol fermentors. Bioresource. Tech- nol. 2010, 101, 5033-5042.

[7] Bishoff, K.M.; Skinner-Nemec, K.A.; Leathers, T.D. Antimicrobial susceptibility of Lactobacillus species isolated from commercial ethanol plants. J. Ind. Microbiol. Biotechnol. 2007, 34, 739-744.

[8] Cleveland, J.; Montville, T.J.; Nes, I.F.; Chick, M.L. Bacteriocins: safe, natural antimicrobials for food preservation. Int. J. Food. Mi- crobiol. 2001, 71, 1-20.

[9] Chassy, B.; Guiffrida, A. Method for the lysis of Gram-positive asporogenous bacteria with lysozyme. Appl. Environ. Microbiol. 1980, 39, 153-158. [10] Rogosa, M. Characters used in the classification of Lactobacilli. Int. J. Syst. Appl. Environ. Microbiol. 1970, 20, 519-533. [11] Wasserfall, F.; Teuber, M. Action of egg-white lysozyme on Clostridium tyrobutyricum. Appl. Environ, Microbiol. 1979, 38, 197-199.

[12] Chung, W.; Hancock, R.E.W. Action of lysozyme and nisin mixtures against lactic acid bacteria. Int. J. Food. Microbiol. 2000, 60, 25-32. [13] Pasquantonio, G.; Greco, C.; Prenna, M.; Ripa, C.; Vitali, L.A.; Petrelli, D.; Di Luca, M.C.; Ripa, S. Antibacterial activity and anti- biofilm effect of chitosan against strains of

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Streptococcus mutants isolated in dental plaque. Int. J. Immunopathol. Pharmacol. 2008, 21, 993-997. [14] Lifeng, Q.; Zirong, X.; Jiang, X.; Caihong, H.; Xiangfei, Z. Preparation and antibacterial activity of chitosan nanoparticles. Carbohy. Res. 2004, 339, 2693-2700. [15] Nasti, A.; Zaki, N. M.; de Leonardis, P.; Ungphaiboon, S.; Sansongsak, P.; Rimoli, M. G.; Tirelli, N. Chioto- san/TPP and chitosan/TPP-hyaluronic acid nanoparticles: sys- tematic optimization of the preparative process and prelimi- nary biological evaluation. Pharmaceut. Res. 2009, 26, 1918- 1920. [16] Tin, S.; Kishore, R.; Lim, C.S.; Sakharkar, M.K. Activity of chitosans in combination with antibiotics in Pseudomonas aeruginosa. Int. J. Biol. Sci. 2009, 5 (2): 153-160. [17] Geornaras, I.; Sofos, J.N. Activity of e-poly-lysine against Escherichia coli, O157:H7, Salmonella Typhimurium and Listeria monocytogenes. J. Food. Sci. 2005, 70, 404-408. [18] Yoshida, T.; Nagasawa, T. -poly-L-lysine: Microbiol produc-tion, biodegradation and application potential. Appl. Microbiol.Biotechnol. 2003, 62, 21-26. [19] Radler, F. Possible use of nisin in winemaking. Action of nisin against lactic acid bacteria and wine yeasts in solid and liquid media. Am. J. Enol. Vitic. 1990, 41, 1-6. [20] Bruno, M.E.C.; Montville, T.J. Common mechanisms of action of bacteriocins from lactic acid bacteria. Appl. Environ. Microbiol. 1993, 59, 3003-3010. [21] Winkowski, K.; Ludescher, R.D.; Montville, T.J. Physicochemical characterization of the nisin membrane interaction with liposomes derived from Listeria monocytogenes. Appl. Environ. Microbiol. 1996, 62, 323-327. [22] Branen, J.K.; Davidson, P.M. Enhancement of nisin, lysozyme, and monolaurin antimicrobial activities by ethylenediamine-tetraacetic acid and lactoferrin. Int. J. Food Microbiol. 2004, 90, 63- 74. [23] Breuer, B.; Radler, F. Inducible resistance against nisin in Lactobacillus casei. Arch. Microbiol. 1996, 165, 114-118.

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Table 1. Minimal Inhibitory Concentration (MIC) Values of Antimicrobials against 5 strains of

Lactobacillus.

Table 2. The effect of nisin (0.4 mg/mL) on L. casei and S. cerevisiae growth on MRS and

petrifilm media, respectively.

Table 3. Enumeration of L.casei on MRS medium subjected to nisin and EDTA at 37 ◦C.

Table 4. The effect of nisin and EDTA (0.4:1mg?mL%) in co-cultured S. cerevisae and L. casei.

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1Pentasodium tripolyphosphate (TPP)

Values are means ± standard errors (n = 3). Means followed by different letters in each column are significantly different (P

0.05).

Table 1. Minimal Inhibitory Concentration (MIC) Values of Antimicrobials against 5 strains of Lactobacillus . Lactobacillus strains

Antimicrobials

L. plantarum ATCC 8014

L. casei ATCC1157

L. delbrueckii subsp Lactis ATCC479

L. plantarum WCFS1

L. sakei 23k

--------------------------------------------------MIC (mg/mL)------------------------------------------------

Chitosan (CS) 1.25 ± 0.1A

1.875 ± 0.02A

1.875 ± 0.02A

1.25 ± 0.1A

1.25 ± 0.1A

Nanoparticles CS + TPP 1 day 1 >0.385 >0.385 >0.385 >0.385 >0.385 Nanoparticles: CS + TPP day 2 >0.385 >0.385 >0.385 >0.385 >0.385 Lysozyme day 1 >100 >100 >100 >100 >100 Lysozyme day 2 >100 >100 100 ± 0.1B >100 >100

Nisin 0.00156 ± 0.033B N

0.05 ± 0.029D

0.0125 ±0.00078C

0.0125 ±0.001C

ε-poly-L-lysine 0.156 ± 0.012B

1.25 ± 0.011B

0.3125 ± 0.02C

0.156 ± 0.01B

0.23425 ± 0.01BC

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Table 2. The effect of nisin (0.4 mg/mL) on L. casei and S. cerevisiae growth on MRS and petrifilm media,

respectively.

(Log CFU/mL)

Time (hours)

Microorganism Culture media 0 24 48

S. cerevisiae Petrifilm 8.21 ± 0.12A 9.22 ± 0.16B 9.36 ± 0.13B

L. casei MRS 6.10 ± 0.10A 9.43 ± 0.13B 9.45 ± 0.12B

L. casei co-cultured2 MRS 8.13 ± 0.15B 6.50 ± 0.11A 6.50 ± 0.10A

S. cerevisiae co-cultured3 Petrifilm 7.00 ± 0.20B 8.15 ± 0.10A 7.20 ± 0.14B

1Values are means ± standard errors (n = 3). Means with different letters in each row are significantly different (P 0.05).

2 L. casei in co-culture with S. cerevisiae was enumerated in the MRS culture medium.

3 S. cerevisiae in co-culture with L. casei was enumerated in petrifilm culture medium.

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Table 3. Enumeration of L. casei in MRS media subjected to nisin and EDTA at 37 OC.

L. casei (Log CFU mL-1)

Time (Hours)

Control Nisin (0.4 mg/mL)

EDTA (1 mg/mL)

Nisin-EDTA3

0 7.14 ± 0.15B

7.20 ± 0.22C

7.0 ± 0.15B

6.50 ± 0.09

24 9.15 ± 0.2A

8.27 ± 0.16A

7.0 ± 1.22B

ND 1

48 9.10 ± 0.17A

7.53 ± 0.10C

7.13 ± 0.19B

ND

1 ND: not detected 2 Values are means ± standard errors (n=3). Means followed by different letters in each column are significantly different (P≤ 0.05). 3 Nisin-EDTA concentrations (0.4 - 1mg/mL).

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Table 4.The effect of nisin and EDTA (0.4-1%) in co-cultured S. cerevisae and L. casei. LogCFU/mL Microorganisms

Antimicrobials

Time (Hours) 0 24 48

S. cerevisiae (control)

N1 6.00 ± 0.001C

7.15 ± 0.10 B

8.18 ± 0.16A

L. casei (control)

N 6.00 ± 0.0002C

9.20 ± 0.24B

9.90 ± 0.12B

S. cerevisiae 4 Nisin-EDTA 6.00 ± 0.001B

7.20 ± 0.13A

7.56 ± 0.7A

L. casei 5 Nisin-EDTA 6.00 ± 0.001C

ND 3 ND

1 No antimicrobial added. 2 Values are means ± standard errors (n=3). Means followed by different letters in each row are significantly different (P≤ 0.05). 3 ND not detected 4 S. cerevisiae in co-culture with L. casei was enumerated in petrifilm culture media 5 L. casei in co-culture with S. cerevisiae was enumerated in the MRS culture media.

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Chapter Three

Quantitative Risk Analysis Fundamentals in Biofuel Fermentations involving Antibiotic Residues and Potential Bacterial Resistance

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Quantitative Risk Analysis for potentially resistant E. coli in surface waters caused by the misuse of antibiotics in agro-industrial systems

Department of Food Science, and Center for Food Safety-Institute of Food Science & Engineering, University of Arkansas, Fayetteville, AR 72704, USA

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ABSTRACT

The food and drug administration’s detection of antibiotic residues in distillers’ dry grain

with soluble (DDGS) in 2008 from corn-based biofuel has increased public concern towards

possible emergence of antibiotic-resistance in the environment. A hypothetical microbial risk

assessment analysis was performed based on the available experts data associated to the U.S.

Great lake hazard concentrations to determine the probability of infection from the fecal

indicator bacteria (FIB) primarily Escherichia coli in water surfaces exacerbated by a misuse

of antibiotics in bio-agricultural systems. Diarrheagenic E. coli except E. coli O157:H7 were

selected for this study for their prevalence in aquatic ecosystem and high potential to acquire

resistant genes before eventually reaching human population through recreational water. A

comparative study between a typical E. coli dynamic flow scenario in the agro-industrial system

versus the worst case scenario aggravated by antibiotic misapplication and the uncontrolled

weather conditions has been performed. The estimation of the risk was performed via Crystal

Ball® software tool through a Monte-Carlo simulation statistical method. Results from

forecasting possible worst case scenario dose-response, accounted approximately 50% chance

for a 20 % of the human population to be infected from recreational water in the U.S. However,

the proportion of the infected population would decrease by approximately 10 folds in a typical

unmedicated system. The sensitivity Chart showed the high impact of the assumption E. coli

concentration in surface water for both the typical E. coli pathway and the worst case scenario

(92.1% and 90.2%, respectively) over the volume of water consumed (9.8% and 9.8%,

respectively). The scatter charts have confirmed the strong positive correlation between the

concentration of E. coli in water surfaces and the dose response forecast for the typical and

worst case scenario (0.0003 and 0.9541, respectively). In conclusion, this first step of the

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microbial risk assessment (MRA) predictive model proved that there is still a risk that requires

serious consideration and needs to be tracked from the source. This model could be refined to

provide more valuable outputs if data gaps starting from the extent of antibiotic resistance effects

in vivo to the concentration of hazard ingested were to be fulfilled.

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1. Introduction

With the exponential increase of global population, along with exhaustion of worldwide natural

resources and increasing potential for widespread human famine, agricultural biotechnology

(Ramasamy, 2007) has become of prime interest to investors and researchers in order to respond

to food scarcities and the over-population rising demands (FAO, 2004). Agricultural-derived

bioethanol production is emerging at a rapid pace in the U.S. and over the globe to compensate

partially petroleum-based gasoline depletion and contribute considerably to rural economy

development (Goldemberg, 2007; USDA, 2011). To date in the U.S., most bioethanol originates

from corn (Bothast and Schilder, 2005). While bioethanol generated from the corn wet-milling

process has experienced substantial development, biofuel from dry-grind processing remains by

far the most extensive process, which increased from 66% of the total bioethanol production in

2005 in the U.S. to approximately 90% this year (Bothast and Schlicher, 2005; RFA, 2011). In

addition to ethanol production, dry grind processing generates the most prevalent livestock co-

products in the U.S. distiller grains (DGs). Microbial contaminants have been of particular

concern to bioethanol productivity and have led investors to use antibiotics in an effort to

suppress potential bacterial contaminants (Skinner and Leathers, 2004). However, the detection

of substantial antibiotic residues (53%) in bioethanol co-products designed in large-scale to

animal feed has raised Food and Drug Administration (FDA) concerns, to prioritize this issue

and explore preventive measures to certify feed and food safety over the entire world (National

Grain and Feed Association, 2009). Consequently risk analysis has received more focus as a

possible approach due to not only to the increasing awareness of worldwide foodborne diseases

and concern about food safety, but in association with the global multilateral trading system

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development by the World Trade Organization (WTO) (FAO/WHO, 1995). During the past few

decades, considerable efforts have been made toward determining new solutions for finding

possible pragmatic remedies to circumvent worldwide food-derived public health threat (WHO

2010). Several microbiological methods and managerial techniques, such as Hazard Analysis and

Critical Control Points (HACCP), have been implemented (Buchanan and Whiting, 1997) to

ensure food safety and reduce risk contamination (Reij and van Schothorst, 2000; Bernard,

2001). However, these methods have remained insufficient to cover most of the uncertainties and

gaps that are required to estimate risk, and reduce it to a tolerable level (ICMSF, 1998). This

constraint has renewed interest in developing a complementary scientific-based method, such as

risk assessment, to develop bacterial standards and prioritize public health safety in today’s

global market (Rose and Gerba, 1991). Currently, risk assessment is among the most promising

scientific-based solutions upon which legislators rely to describe risk estimates from chemical or

microbial contaminants in food products worldwide (CAC, 1999; FAO/WHO, 1995, 1997). Risk

assessment is essentially a scientific-based approach that is systematically and transparently

analyzed to assess the probability of occurrence and severity of the disease based on four major

steps that can be either chemical or microbiological (NRC 1983; U.S. EPA, 1986; ILSI-RSI,

1996; Haas, 1983; Haas et al. 1999). Several frameworks have been provided to fit continuous

management requirements and support global development (CAC, 1999; FAO/WHO, 1995).

Overall, the major objective of the risk assessment (RA) process is to provide a qualitative and

quantitative estimate through its major cornerstones, namely, hazard identification, hazard

characterization, along with response-dose assessment, as well as exposure assessment and risk

characterization (NRC, 1983; ILSI-RSI, 1996) (Figure 1).

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Chemical risk assessment was the first implemented technique to estimate the risk of

chemical-toxic contaminants (U.S. EPA, 1986). Unlike chemical risk assessment that involves

toxicological or pollutant-agent contaminants, microbial risk assessment (MRA) is a more

complex process, as it requires consideration of a wide range of variables, including bacteria

proliferation in food often associated with a broad continuum of environmental changes (Jaykus,

1996; ICMSF, 1998) from suppliers to consumers (WHO, 2010). Variation in microbial

virulence, along with change in microbial growth, constitutes substantial difficulty towards the

implementation of microbial risk assessment (WHO, 2010). Quantitative microbial risk

assessment (QMRA) also describes microbial treatments and major physical (e.g., heat-

processing, cold shock and high pressure), chemical (e.g., preservatives, antibiotics, organic

acids and chlorine) and biological ( e.g., microbial competitive exclusion, including probiotics,

bacteriophages and peptides) hurdles followed by food ingestion and illness subsequent to the

human host immunity defense system ( Gerba, 1996; Haas, 1999; WHO, 2010).

Furthermore, unlike HACCP that continuously controls hazards of a particular product at

a critical control point, MRA represents an approach that involves all similar products in the

marketplace, and assesses primarily the exposure (Buchanan and Whiting; 1997; Reji and

Schothorst, 2000). Another advantage provided by MRA is the estimation of subsequent

manifestations including cross- and recontamination responsible for a wide range of foodborne

illnesses (Chen et al., 2001). From another angle, Cox and Popken (2007) claimed the risk that

would be generated from an excessive preventive measures and regulations. For instance, an

excessive preventive methods resulting from regulatory risk assessment itself (i.e., an entire ban

of antibiotic use) could lead to a dramatic increase of disease and illness within animal and

human population. Therefore, a practical risk analysis method that would elucidate actions to

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bring benefits rather than harm would be the key-technique to ensure healthier environment

through scientific-based approaches (Cox and Popken, 2007).

This research study summarizes what is currently known about risk assessment and its

potential implications in agricultural biotechnology. It evaluates primarily the antibiotic misuse

in farm animals and adjacent industries that would lead to the antibiotic resistance dissemination

in the nearby ecosystem. Specific focus is directed toward elucidating the first step of the risk

assessment model adopted to the agro-industrial zones from the possible propagation of

antibiotic residues and resistance through animal feeds and growth promoters. This study

provides an assumption of a potential spread of fecal indicators carrying antibiotic-resistance

(i.e., Escherichia coli) from animals via a fecal route to surface waters and would drain to the

recreational U.S. Great Lakes. The study also provides a comparison between a typical E. coli

pathway in an unmedicated agro-industrial zone and the worst case scenario intensified by the

antibiotic resistance and uncontrolled weather adverse outcomes (i.e., manure spills, untreated

run off, sewage overflows). A hypothetical MRA modeling is addressed to evaluate exposure

assessment through Monte-Carlo simulation Via Crystal Ball® software based on empirical data

and experts’ opinions. Intervention scenarios and remedies are also suggested to add an effective

point-of-care approach and provide preventive measures to ensure environmental and public

biosafety with today’s challenging modern biotechnology expansion.

2. Risk-Assessment Emergence 2.1. Chemical Risk Assessment Compared to MRA

Since the 1960s, quantitative chemical risk assessment has been implemented and defined

by Office of Pesticide Program (EPA) as a function to estimate public health effects associated

mainly with toxic and neuro-toxic components, along with environmental pollutants, pesticides

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and carcinogenic chemicals (DEQ, 2006). MRA originates from chemical risk assessments

(CRA), and both risk estimations are based on the same major steps, namely, hazard

identification along with hazard characterization, exposure assessment and risk characterization

(NRC, 1983; U.S.EPA, 1986).

There are some potential differences distinguishing a chemical risk pathway from a

microbial pathway, and these two pathways require different approaches and methods. Chemical

response-dose risk is always considered chronic, as it requires a long latency period after a

chemical exposure. The detection of chemical hazards is also difficult, since there needs to be

enough evidence for it to be considered as a potent substance, particularly as low concentration

and chemical effects can range from a negligible skin irritation to a serious illness such as cancer

(DEQ, 2006). Conversely, microbial hazards are generally acute, as they can be evaluated in a

very short period of time due to the substantial microbial databases mostly available (ILSI-RSI,

1996). Furthermore, unlike chemical exposure that can undergo a static or decline phase,

microbial exposure can change exponentially by either increasing or decreasing in a short period

(Jaykus, 1996; ICMSF, 1998). The dramatic microbial growth or death depends on different

variables. It includes the estimation of several intrinsic or extrinsic factors of pathogens in

addition to their characteristics (i.e., virulence, growth rates and antibiotic resistance temperature

resistance), as well as the specific food matrix (i.e., composition, pH, aw) and the infected hosts

or sensitive human or animal population (i.e.,immunity and susceptibility) (Gerba, 1996)

correlated with several conditions and parameters variations from farm-to -fork (i.e., processing,

temperature, storage, distribution, cross-contamination, packaging conditions and cooking)

(CAC, 1999).

2.2. Microbial risk assessment major implications

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Foodborne illnesses have always been of primary concern to animal and human health.

To date in the U.S., pathogens, including mainly the genera Salmonella, Campylobacter and

Listeria along with E. coli O157: H7, are primarily associated with foodborne diseases, and most

of these are responsible for approximately 76,000 hospitalizations and 5,000 deaths in the U.S.

each year (WHO, 1997; Tauxe 2001; USDA, 2008). Foodborne disease incidence has imposed

the implementation of major regulatory risk that primarily involves: Listeria monocytogenes in

ready to eat food (RTE), Salmonella Enteridis in eggs, along with Escherichia coli O157: H7

in ground beef, Bacillus cereus in Chinese-style rice (McElory et al., 1999) as well as

Campylobacter jejuni in broilers and Vibrio haemolyticus in oysters (Nicholson et al., 2000;

Tauxe et al., 1997).

A qualitative risk assessment conducted by Basset and McClure (2007), reported the

implication of at least 14 potential microorganisms in fruit and vegetable contaminations,

including, bacteria, virus, protozoa and nematodes. The Center for Disease Control and

Prevention (CDC) reported (2007) leafy vegetables alone being responsible for at least 590

foodborne illnesses in the U.S. During the last decades there have been several extensive

outbreaks originating from fruits and vegetables. In 2006, E. coli O157: H7 (EHEC), defined as

the hemolytic-uremic-syndrome, was identified in fresh spinach in the U.S, and caused the

illness of 183 persons (Anon, 2006). In Sweden, EHEC caused the illness of 120 people who had

consumed contaminated iceberg lettuce (Anon, 2006). In 2008, the genera Salmonella was

involved in poisoning outbreaks due to infected jalapeno peppers which subsequently led to

tomato contamination. The FDA revealed the implication of mainly Salmonella Saint-Paul in the

foodborne disease causing the hospitalization of 1,200 persons in 43 states (Prietzker Law,

2008). In an effort to find efficient interventions to ensure public health rules and improve food

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safety policies, researchers have developed a science-based risk assessment model

complementary to the HACCP technique to circumvent potential issues of foodborne and

waterborne diseases (Buchanan and Whiting, 1997). Several MRA frameworks have been

established (Jaykus, 1996; Lammerding, 1997; McKone, 1996; McNab; 1998). MRA approaches

have then been further developed to fit international standardizations (CAC, 1999; FAO/WHO,

1995, 1997). Although in most of the MRA the framework hazards are selected by risk

assessors, they are determined by managers in Codex Alimentarius framework (Reji and van

Schothorst, 2000).

Overall, hazard identification is the process that involves the collection and organization

of data to further identify and evaluate the target pathogens responsible for adverse public health

effects (ILSI-RSI, 1996). Identification of microbial pathogens is followed by hazard

characterization, mainly involving the correlation between target pathogens and the health

adverse effects via response-dose assessment. It measures the microbial dose ingested that can

cause detectable harmful effects and its severity within the host (Haas et al., 1999). Exposure

assessment provides a qualitative and quantitative estimation of foodborne intake from farm-to-

consumer (CAC, 1999; FAO/WHO, 2005). Once combined, data including response-dose and

dose assessments associated with uncertainties provide a qualitative and quantitative risk

estimate and final characterization. A general framework of QMRA approach is shown in Figure

1. For bioethanol fermentations, MRA would be a useful comprehensive approach in elucidating

major concerns and further estimating the risk of using GMOs in biofuel system from harvest-

to-consumer spectrum.

3. Current concerns in biofuel system: microbial contaminants prior to antibiotic residues and resistance. 3.1. Fermentation problems - microbial contaminants

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While most bioethanol feedstocks are composed of sugar polymers, contaminants slightly

vary and depend very closely on the type and nature of hydrolysates; the most prominent

contaminants are illustrated in Table 1. Among the extensive range of microorganisms existing

in the environment, lactic acid bacteria are the most abundant microorganisms in ethanol yeast-

based fermentations (Skinner and Leathers, 2004; Bischoff and Leathers, 2009), followed by

wild yeasts, including wild S. cerevisiae (Ingledew, 2005; de Souza et al., 2005; Abbott and

Ingledew, 2005). Although LAB are estimated to be the most prevalent contaminants to yeast-

based ethanol fermentation, sporadic wild yeasts introduced accidentally to fermentors could

disrupt the fermentation process and cause quality depletion and ethanol yield losses (Abbott and

Ingledew, 2005). For instance, genera such as Rhodotorula, Brettanomyces, Dekkera, Candida

and Pichia have been isolated sporadically in different types of sugar-based alcohol

fermentation, including wineries, breweries and biorefineries (Campbell, 1987). Furthermore,

yeasts belonging to genera Pichia, Brettanomyces and Candida have been shown to ferment the

pentose sugar, xylose, and hence find ideal environments in pentose-rich feedstocks such as

lignocellulosic materials originating from agricultural residues rich in hemicellulose ( Barnett,

1975).

Lactic acid bacteria (LAB) are anaerobic, very resistant to low acidic conditions, pH 4.0,

and are alcohol-tolerant (Thomas et al., 2001; Graves et al., 2006). They are able to survive at a

high concentration of alcohol (17% v/v) and an extensive range of temperatures between 15oC to

55oC (Thomas et al., 2002; Graves et al., 2006; Narendranath, et al., 2001; Bayrock and

Ingledew, 2004). LAB that contaminate bioethanol fermentation processes belong to the genera,

Lactobacillus, Leuconostoc, Enterococcus, Weissella and Pediococcus (Lushia and Heist, 2005).

Gram-negative bacteria, including genera, Gluconobacter and Acetobacter, grow under aerobic

112

conditions and produce acetic acid toxic to yeasts reproduction (Lushia and Heist, 2005).

However, Lactobacillus species, such as L. casei and L. fermentum, possess sugar- scavenging

abilities and organic-acid production which are both detrimental to yeast performance and

survival (Rainbow, 1971; Hough et al., 1982; Makanjuola et al., 1992).

Furthermore, sources of LAB reinfection can be amplified via formation of biofilm

composed of a community of Lactobacillus and Leuconostoc genera (Skinner and Leathers,

2004; Skinner-Nemec et al., 2007). This matrix serves as a persistent source of reinoculation that

is very resistant to antibiotic or heat treatments (Kram, 2008), and thus causes a severe infection

and a serious shut down in the fermenters. Moreover, these contaminants are typically exposed to

antibiotics for control in the fermentors. Presumably, persistent contaminants would further

develop antibiotic-resistance in the fermenters resulting from misuse or overuse of antibiotics.

Disproportionate administration of antibiotics is becoming very common in bioethanol

fermentations primarily when the required effect has been either efficient or has not been

observed. Overdosing antibiotics can decrease the effect of distillation temperature to destroy

the antibiotics used (Narendranath and Power, 2005). However, an incomplete antibiotic dosage

could lead to a lack of efficiency of the drug used or antibiotic- resistance development in the

microorganism. Kohanski et al. (2010) have demonstrated the effect of sublethal antibiotic doses

on stimulating the formation of reactive oxygen species (ROS) subsequent to mutagenisis

inductions and to the generation of multi-drug resistance. A recent study in antibiotic resistance

in Sweden reported the impact of a very low antibiotic dose on the selection of resistant bacteria.

They have also concluded that a very low dose of antibiotics could enhance the maintenance and

enrichment of bacterial-population resistance in the ecosystem due to a minimal growth rate

reduction of their susceptible counterparts (Gullberg et al., 2011). While this research study

113

places the emphasis on the antibiotic resistance that could occur in fecal indicators exposed to a

low or sublethal dose of antibiotics (Gullberg et al., 2011) originating from DDGS, an in-depth

understanding of the bacteria in bioethanol fermentors could yield information that would be

useful to the comprehension of the entire model of biofuel systems. This includes the elucidation

of the bacterial dynamic flow in conjunction with antibiotic use from farm-to-consumption

through fermenters and subsequent operations. From microbial and biochemical perspectives, it

is quite possible that a temperature of 500oF (260oC) is enough to destroy LAB contaminants

and would inhibit the biological activity of antibiotics. However, earlier studies have

demonstrated that genetic elements, including antibiotic resistance (i.e., DNA fragment), could

remain in distiller grains even after a high temperature of 500oF (Pedersen, 2004). Additionally,

based on what has been mentioned previously, a low level of antibiotic remaining in the distiller

would favor antibiotic resistance development and maintenance in biofuel systems . Further

elaboration in antibiotic resistance mechanisms is addressed in later sections.

3.2. Antibiotic use in the fermenters and direct consequences

Antibiotics have revolutionized contemporary medicine with their remarkable ability to

target pathogens (Lorian, 1986). However, evolution has allowed bacteria to become resistant to

antibiotic effects (Smith et al., 2002). Antibiotic-resistant bacteria have been detected since 1920

and have become of great concern in the public health sector (Lushia and Heist, 2005). The

emergence of antibiotic-resistance was believed to be due to an extensive use of antibiotics in

medical applications creating more chances for bacterial exposure to penicillin and derivatives.

Thus, the excessive bacterial exposure to penicillin has been associated with the increasing risk

of antibiotic resistance (Smith et al., 2002; Lorian, 1986). As researchers synthesize more

antimicrobials, bacterial cells adapt and develop the means to survive to different physical,

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biological and chemical stresses. Although antibiotic resistance occurrence is always attributed to

antibiotic overuses in medical and food industries, the fact remains that microbial cells can

usually mutate to resist stress and survive (Lorian, 1986, Gullberg, 2011).

Similarly, in yeast fermentation, antibiotics and non-biological inhibitory agents have

been implemented as control agents in yeast-based ethanol fermentation, since bacterial

contaminants have been detected at 106 bacteria/mL in corn wet milling process and 108

bacteria/mL in dry grind (Skinner and Leathers, 2004). Given this potential issue, antibiotics

have been used to limit bacterial contaminants in bioethanol fermentations (Bischoff et al., 2007).

Traditionally, numerous antibiotics, such as streptomycin, virginianycin, penicillin, ampicillin,

tetracycline and monensin, have been used in bioethanol fermentation to control bacterial

contamination (Day 1954; Aquarone, 1960; Hynes et al.,1997; Stroppa et al., 2000).

Virginiamycin, a type of streptogramin (VIR) and penicillin (PEN), has been shown to be the

most effective broad spectrum, and is practical against target contaminants, Lactobacillus species

(Dutta, 1992).

These antibiotics have been the most commonly used to reduce contamination in corn-

based bioethanol fermentation primarily because of their low minimal inhibitory concentration

(MIC) over a range of 0.1 to 0.5 mg/mL (Cocito, 1979; Allignet; Hynes et al., 1997) in

comparison to other antibiotics, as it renders them economically more viable. Furthermore,

despite its higher cost, VIR is preferred over penicillin for its higher tolerance to low pH 4.0

caused by organic acids generated during fermentation (Hynes et al.,1997). Although VIR and

PEN are commonly used to reduce bacterial contamination, their extensive use causes strains to

become less susceptible to their effects (Bischoff and Leathers, 2009). During the last decade,

antibiotic-resistance microorganisms have been isolated from bioethanol refineries. A strain of

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P. acidilactici was found to be resistant to a high dose of VIR, 100 parts per million (100 ppm)

and PEN (50 ppm) (Lushia and Heist, 2005). This study also shows that the genera Weissella

confusa and Pediococcus acidilactici were VIR and Pen-resistant. Strains such as W. confusa

resisted an elevated dose of 25 ( ppm) VIR. Furthermore, the increasing use of selected

antibiotic-resistance marker in GM crops manipulations led to a potential dissemination of

antibiotic-resistance through biofuel co-products.

3.3. Antibiotic residues in DDGS and indirect consequences

Continued growth of bioethanol production is closely related to a livestock co-products

increase, since one third of each bushel of corn can be transformed to either feed for animals or

food for humans (RFA; 2011). Hence, animal feeds offer an added-value to bioethanol

distilleries, and contribute considerably to their economic viability. These coproducts include

primarily distiller grains (DGs) of which 90% are designated for animal feeds, and approximately

80% are given to ruminants in the US. Bioethanol processes in the U.S., include wet-milling and

dry-grind (RFA, 2011). Unlike the wet-milling process, dry grind has registered greater

expansion, from 66% in 2005 to 90% in 2011 (Bothast and Schlicher, 2005; RFA, 2011). The dry

grind process (Figure 2) has experienced further development that is due primarily to the higher

capital incomes per gallon of ethanol with less operations and energy consumption.

DGs co-products, including primarily high protein distillers’ dry grain with soluble

(DDGS), along with distillers dry grains (DDG) and wet distillers grains (WDG), are the most

prevalent animal feed co-products originating from the dry grind process (RFA, 2011). Typically,

for each lot of corn gain entering the bioethanol process, almost 34% are fed to animals (i.e.,

poultry, dairy cattle, swine and even fish) and human food (Shurson et al., 2003; Rosentraters

and Muthukumarappan: RFA, 2011). These animal feeds include primarily DDGS, followed by

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corn oil, along with gluten feed and meal (Figure 2). In 2010, there was a generation of

approximately 35 million metric tons/year (mmt/year) of livestock co-products in the U.S with

31.5 mmt of DGs constituting one third of the 4.1 billion bushels of corn-grain (RFA, 2010).

Actually, DGs production is expected to reach a substantial increase of 39 mmt, with 9 mmt

being exported mainly to Asian countries (RFA, 2011).

However, in 2008, the FDA detected antibiotic residues in 53% of 60 samples from

DDGS products collected from biofuel distilleries in the U.S. These samples included mostly

erythromycin (27%), as well as virginiamycin (33%) and tylosin (11%), with some of these

exceeding the concentration of 0.5 ppm (NFGA, 2009). This incidence has raised concern over

the disproportionate use of antibiotics in the biofuel system, and created the need to set

preventive and strict measures to protect public health (NFGA, 2009).

Although the presence of antibiotics in livestock feed has been associated by some

concerned researchers and consumers to antibiotic-resistance spread in the environment (Kansas

Farm Bureau, 2011), these assumptions remain somewhat elusive for drawing conclusions.

However, if large-scale antibiotic residues remain biologically active, they could lead to the

emergence of antibiotic-resistant bacteria capable of spreading via several pathways to the

environment (Smith et al., 2002). Animals fed from DGs, including primarily beef cattle (40%

of the feed ratio), as well as swine and poultry, carry in their gastrointestinal tract (GIT)

commensal bacteria, some of which are fecal indicator bacteria (FIB), namely E. faecium and

E.coli. FIB could develop antibiotic resistance in the GIT (Sorensen, 2001; Smith et al 2002),

and hence distribute it to the environment via the fecal route (i.e., manure or sewage) to surface

or ground waters through agricultural runoff over a large area and long distances (De Roever,

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1998; Nguyen-the and Carlin, 1994). The biology and physiology of FIB are discussed in a later

section.

3.4. Antibiotic resistance mechanism of potential exposed bacteria in a biofuel ecosystem

From prior view points, there is a wide range of microorganisms that could be exposed

directly or indirectly to a disproportionate (i.e., low or high doses) level of antibiotics in the

biofuel environment. LAB, Lactobacillus strains, are primarily the most prevalent

microorganisms that are directly exposed to antibiotics in the fermenters or during DDGS

storage in the bioethanol system. This genera has proved to develop antibiotic resistance with

either spontaneous mutations or gene exchange. For instance, previous studies on the antibiotic

resistance related to the probiotic, Lactobacillus, have demonstrated that there are several strains

of Lactobacillus present in vegetables, dairy and humans that possess the potential to exhibit a

high rate of spontaneous mutation (Curragh and Collins, 1992).

Further, the detection of antibiotic-resistant Lactobacillii that exhibit resistance to

erythromycin, tetracyclin and chloromphenicol in the plasmid of some strains of Lactobacillus

has raised researchers’ concerns about the possibility of conjugately transmitting the genetic

materials via plasmid vectors to other recipient bacteria, and hence becoming a reservoir of

resistant genes inherent in biofuel distilleries and surroundings ( Salminen et al., 1998). Also,

substantial research studies have demonstrated the existence of de novo antibiotic resistance

mechanism in Lactobacillus rhamnos in addition to the existence of several antibiotic efflux

proteins that were reported in other Lactobacillus strains (Van Veen and Konings, 1998;

Tynkkynen, 1998). The acquisition of resistance by LAB in the fermenters via genetic exchange

or spontaneous mutations subsequent to a prolonged exposure to antibiotics have been

extensively reviewed by (Muthaiyan et al., 2011). Thus far, from the fermenters, the detection of

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antibiotics residues antibiotic in DDGS has led the FDA to rapidly examine the issue in an

attempt to set efficient standards and thus prevent antibiotic resistance from dissemination into

the environment.

In addition to LAB, FIB could be exposed to sublethal doses of antibiotics originating

from animal fed from DDGS carrying antibiotic residues. E. faecium is an emergent

streptogramin resistant microorganism (SREF) that has been extensively isolated from farming

animals and adjacent areas (Claycamp and Hooberman, 2004). Typically, E. faecium often

acquires resistance through a direct transfer of resistant genes from another bacteria via a

conjugation mechanism involving transposons and plasmid vectors or also through acquisition

of DNA fragments from the environment (Soltani et al., 2000; Werner et al., 2000). Overall,

gram-positive microorganisms, including E. faecium, resist streptogramins (i.e., virginiamycins,

pristinamycins or Quinupristin/dalfopristin (synercid)) via different mechanisms, involving

primarily the alteration of ribosomal sites, active efflux or inactivation of enzymes.

The most prevalent enzyme inactivation mechanism against streptogramin type A

includes O-acetyltransferase. For instance, acetyltransferase of virginiamycin is encoded by the

gene vat. Among the most common vat genes encoding to streptogramin A in E. faecium are

vat(D), also called sat A and vat(E), formerly named satG (Werner and Witte, 1999). Changes in

vat (E) gene in SERF due to single base replacement, has been demonstrated by Soltani et al.

(2001). The active efflux has been reported to extrude streptogramins via ABC porters encoded

by vga (A) or vgb (B) alleles. From another angle, the methylation of ribosomal RNA (rRNA)

encoded by erythromycin-ribosome-methylase (erm) causes site alteration in the 50S subunit

microbial ribosome, and hence could interrupt streptogramin B binding on the 50S subunit sites.

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Earlier research studies have reported that the erm(B) genes in enterococci have already been

found widespread in the environment (Werner et al., 2000).

Aside from E. faecium, E. coli is a well-known fecal indicator causative of diarrhea in

patients and also urinary tract infections in some hosts (Allen et al., 1999). Overall E. coli has

been shown to exhibit resistance to multiple antibiotics, including primarily, ampicillin, as well

as amoxicillin with moderate resistance to third-generation cephalosporins (Karlowsky et al.,

2002). Several studies have revealed resistance mediation via resistant gene acquisition (i.e.,

plasmid-encoded β-lactamase, SHV-1 or TEM1) or down regulation of the intrinsic wall porins

(OmpF) (Tenover, 2006). Typically, a large part of gram-negative bacteria are intrinsically

resistant to streptogramin drugs.

An earlier study, however, has isolated the streptogramin-resistant gene fragment from

Yersinia enterocolitica encoding for a similar gene of VIR acetyltransferase that was detected in

several gram-positive bacteria plasmids (Seoane and Garcia Lobo, 2000). This study

demonstrated that the antibiotic sensitive E. coli strain DB10 has become resistant to both

streptogramin A and B upon incorporation of the similar VIR acetyltransferase gene. In addition

to its natural ability to acquire genes via conjugation mechanism, E. coli was shown to easily

undergo transformation that occurs from an uptake of a readily available extracellular DNA

fragment in the environment or also through transduction, which is the case for E. coli K-12

(Cohen, 1972).

Furthermore, Courvalin (1994) has reported that E. coli strains are able to transfer their

genetic materials to other bacteria, and hence disseminate them easily in addition to their ability

to acquire foreigner DNA fragments from another bacteria or from the environment. It has been

demonstrated that E. coli can transmit its genetic material to a wide range of microorganisms,

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namely, Alcaligenes eutrophus and Enterococcus faecalis, along with Listeria monocytogenes,

Staphylococcus aureus and a considerable list of other microorganisms, including Citrobacter

freundii, Bacillus stearothermophilus and Streptococcus spp. Thus far, Bailey et al. (2010) have

assessed the resistance of E. coli isolated from 20 adults to 10 different antibiotics via molecular

methods. They have registered 19 subjects carrying E. coli, from which approximately a high

level of 30% of isolated strains developed resistance to a range from one to six antibiotics

including sulfamethoxazole along with ampicillin, tetracycline and trimethoprim in 15 different

combinations. It has been concluded that commensal E. coli, could form a considerable reservoir

for an extensive combination level of antibiotic resistance genes. Furthermore, Minas et al.

(2008) have assessed the level of antibiotic resistance of commensal E. coli and E. faecium

formed in the GIT of pigs and related farm workers. Bacteria resistance was evaluated via the

minimal inhibitory concentration (MIC) of each antibiotic used in the farm. They concluded that

farm workers that were directly and indirectly in contact with fattening pigs have developed

resistance to several antibiotics used in the farms (i.e., ampicillin tetracycline and erythromycin)

that were transferred from pigs carrying resistant E. coli and E. faecium. From this perspective,

serious concerns towards the harmful outcomes that antibiotic resistance can generate to the

ecosystem has driven research efforts to explore and scrutinize pragmatic options to protect the

environment from a possible upcoming risk.

5. Materials and Methods

5.1. Overview to the risk analysis

While chemical risk assessment generally examines a chronicle hazard requiring long

period latency and some static evolution (U.S. EPA, 1986), both chemical and microbial risk

assessment are based on four major similar steps. This theoretical overview encompasses

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primarily the microbial risk assessment approach and summarizes the framework described by

the Codex Alimentarius (CAC, 1999) based on the traditional QMRA fundamentals (NRC, 1983,

ILSI-RSI, 1996; Haas, 1999). Hazard identification is the first step succeeding the problem

formulation. It involves the compilation and organization of data related to each microbial hazard

that is capable of causing undesirable public health effects. Hazard characterization is estimated

through a response-dose assessment approach that assesses the level of microorganisms to which

a sensitive population is exposed, and measures the impact and severity of the microbial

concentration on this population.

A quantitative model can be subdivided into two methods, namely, deterministic (point

estimate) or stochastic (probabilistic) (Cassin et al., 1998). A point estimate approach includes

only single values, such as average or best/worst cases (Buchanan and Smith, 2000). However,

the probabilistic approach based on Bayesian method, considers all data available by involving

probability distribution techniques (Franz et al., 2004). Probabilistic techniques include the

estimation of variability (e.g., diversity in one population) and uncertainty (e.g., lack of

information or knowledge about one parameter). Based on these potential variations and

uncertainties, predictive models have been designed to meet technological requirements in terms

of quantifying the probability of microbial hazard occurrence associated to the frequency and

severity of the health adverse effects.

Mathematical modeling, including qualitative and/or quantitative evaluation, would

provide an estimate about the microbial hazard pathway from farm-to-fork (CAC, 1999) and its

subsequent health adverse effects (Haas et al., 1999). Response-dose assessment involves

mathematical equations that associate the ingested doses to the microbial specific parameters and

host susceptibility to evaluate the probability of infection (Haas, 1983; FAO, 2005). Among

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different equations assessed, exponential and beta-Poisson correlations have been shown to be

the most suitable to assess dose-responses related to MRA (Haas, 1983; 1999). These studies

have also reported that exposure assessment is either a qualitative or quantitative approach that

assesses mainly the source of hazard and route of exposure (inhalation, ingestion, skin contact),

followed by the level of microbial contamination (doses, concentrations) and its prevalence

(frequency) in food at the time of consumption. An appropriate exposure model considers the

dynamic microbial survival and inactivation correlated to the major steps that determine the risk

from the farm to consumption broad continuum (e.g., harvesting, processing, heating, storage,

consumption). Furthermore, exposure analysis considers several intrinsic and extrinsic factors

related to microorganisms, food products and the infected animal or human population. This

includes microbial aspects (virulence, antibiotic resistance, and growth) associated with food

matrix (water activity, acidity, salt contents, antimicrobials) and host factors (susceptibility,

defense system) (FAO, 2005).

The description of exposure assessment is implemented via a dynamic model (Jaykus,

1996) to assess the microbial level and contaminant prevalence with respect to variables and

uncertainties. Correlation between uncertain variables is described via a probability distribution

method subsequent to Monte-Carlo simulations that evaluate the risk through extensive

iterations based on random inputs (Vose, 1996; Lammerding and Fazil, 2000). Finally, the

integration of hazard identification and characterization provide the risk estimate, including

uncertainties and variabilities upon which decisions can be based (Haas, 1998; Cassin et al.,

1998; Thomson and Graham, 1996).

5.2. Monte-Carlo Method via Crystal Ball ® software

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The QMRA computational technique required essentially the establishment of a

deterministic, probabilistic model (without point estimate) subsequent to a stochastic model.

Once this model was established, the simulation was performed by Monte Carlo Method (MCM)

using a software tool, namely, Crystal Ball ® add-in to EXCELTM (Oracle Crystal Ball, Fusion

Edition) in an effort to analyze and quantify parameter uncertainties that entered the model. The

construction of the deterministic model subsequent to a stochastic model involved one or more

parameters called uncertain variables in the MCM framework. The Crystal Ball ® tool was used

for its ability to select randomly from a set of inputs from several probability distributions

(CAMRA, 2011). This allows for obtaining output-values, as well as to evaluate multiple

deterministic models and elucidate the uncertain variables through an extensive number of

iterations (i.e., 104 trials). The uncertain variables that were selected for this model are the

volume of water ingested from the source (recreational water) and the E. coli concentration in

the water source. A set of a standard normal probability distribution fitting each model was

performed, including two log-normal distributions, as shown in Table 3. The general equation

for the probability density function f(x) to describe variability and uncertainty is expressed as

follows:

Where µ is the mean ( location parameter) and σ is the standard deviation (the scale parameter)

define the particular normal (or Gaussian) distribution. The horizontal axis gives the values of

the uncertain variables and the vertical axis is the probability that the values of an uncertain

variable will occur. Figure 6 is the plot of the continuous lognormal distribution for both random

variables, volume of water ingested and the 37 % pathogenic E. coli concentrations picked

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randomly in water surfaces adjacent to Great Lake beaches ( 37% of E. coli are pathogenic in

Great Lakes according to Hamelin et al. (2006)). The ingested volume of water distribution was

determined based on the outcomes for adults from Dufour et al. (2006) that reported the adult

intake of 16 mL/ 45min from a recreational water while swimming. This study assumed a human

population exposure for 4 hours/ day on average. Therefore the mean become 0.085 and the

related standard deviation 0.101, f(x, µ =0.085, σ= 0.101). However, the lognormal distribution

associated to the pathogenic E. coli fluctuating concentrations in lake beaches was determined

through a series of data chosen randomly by Crystal ball software based on Table 2 available data

to fit a continuous probability distribution. The best fit for this random variable was the log

normal X = log f(x) ranked by goodness-of-fit statistic, Kolmogorov-Smirnov(K-S) and using

the empirical cumulative distribution formula of the numerical data. These results were fitted to

a mean of 3.3 x 105 and a standard deviation of σ= 2. 92 x 107 for a typical scenario, f’(x, µ

=3.3 x 105 σ= 2. 92 x 107) and f’’(x, µ =3.3 x 107 σ= 2. 92 x 109) for the worst case scenario,

respectively.

In addition to the probability density function (PDF), the cumulative density function (CDF) has

been also used to convey a greater communication of the results including the continuous random

variable. CDF function is expressed as follows, where f(t) is the PDF:

Unlike the PDF that only shows the probability of an exact value on the horizontal-axis, the CDF

provides the probability of a value that are less than the all displayed values in x-axis (Figure 7)

5.3. Biosafety problem formulation-Data set:

The detection of antibiotic residues in DDGS products in 2008 has driven FDA

legislators to set more stringent surveys to examine the probability of occurrence of an adverse

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outcomes. The transmission of sterptogramin resistant Enterococcus faecium (SREF) from

animal to humans was asserted to be biologically possible through foodborne pathways

(Claycamp and Hooberman, 2004). However, there is still some difficulty in proving this

transmission in vivo. Therefore, it appears more practical to assess the transfer of livestock

infected manure carrying already formed antibiotic resistance to surface water. Although the

Office of Surveillance and Compliance (HFV-200) suspects the presence of biologically active

antibiotic residues in DDGS, current confirmatory data remain incomplete to draw a solid

conclusion to confirm this assumption. A diagram presenting a dry-mill ethanol production mass

balance and possible antibiotic routes is shown in Figure 3. The process water (i.e., thin stillage,

condensate, CO2 scrubber water, water from distillation) eventually is recycled in the bioethanol

plant. Therefore, it does not leave the site. The distillers grains are separated by centrifugation

from the smaller particles that end up in the thin stillage, which is 7 to 8% dry matter and very

fine particles (Ferm. Solutions Inc., Danville, KY, 2011). Consequently, the level of antibiotic

residuals that would be carried with the distillers grains and with the thin stillage remains

inconsistent and debatable. Recently, fermentation experts (Ferm. Solutions Inc., Danville, KY)

have been involved in the collection of 100 DDGS samples from ethanol facilities located in the

U.S. midwest region, including primarily the states of Kansas, Nebraska and Texas, along with

the central region (i.e., Iowa, Minnesota, Wisconsin and Michigan). This large study has

revealed that in most cases antibiotic residues were barely detectable with a lower limit of

detection of 0.2 ppm. In most cases where the samples were positive, the antibiotic concentration

was slightly above 0.2 ppm. The triangular distribution of the antibiotic residues level performed

via Crystal Ball ® add-in to EXCELTM tool (Oracle Crystal Ball, Fusion Edition-2011) is shown

in Figure 4. Although the presence of antibiotic residues in DDGS samples were slightly

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detectable, a sublethal dose of antibiotic residues could possibly lead to the formation of

antibiotic-resistance within contaminated storage conditions or in animal gastrointestinal tracts

(Bailey et al., 2010; Minas et al., 2008; Gullberg, 2011). The dose would have to be high enough

to cause a population shift and it is debatable whether the low residue levels would cause

resistance formation in microbial population. Furthermore, this would require that the residues

are active, which has not been shown. According to Martinez (2009) while antibiotic residues

would be degraded after a high stress , altered genes (i.e., antibiotic resistant genes) could be

integrated into a genetic vectors or platforms and hence would replicate in bacterial population.

In fact, Pedersen et al. (2004) have reported that the presence of resistant genetic elements in the

system would endure abiotic factors including antibiotics and temperature. Furthermore, there

are several pathways that could transfer antibiotic-resistance from animals to humans (i.e.,

foodborne or waterborne through drinking water or occurring during water activities). From

another view point already mentioned, this antibiotic resistance would continue to be maintained

and enriched by a low dose of antibiotic residues originating from biofuel system still existing

in the ecosystem if preventive measures are not taken (Gullberg et al., 2011). The use of an

antibiotic as a growth promoter or disease treatment is also outlined. While the assessment of

antibiotic dose-response is very plausible, there are some difficulties predicting a consistent

outcome from the chemical pathway, given that the concentration of antibiotic residues and their

biological activity in DDGS determination are still under investigation and remain debatable.

Consequently, chemical risk assessment aiming to evaluate doses that are responsible for adverse

health effects, including the formation of antibiotic-resistance in vivo, remain beyond the scope

of this study

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Subsequent steps assess the risk from a concentration of E. coli that are picked randomly

from the shoreline adjacent to the beaches of the Great lakes. They encompass the problem via

a comparative study between the typical E. coli dynamic flow in an unmedicated agro-industrial

system ending in Great lakes and the assumption of a worst case scenario aggravated by farm

animals exposed to antibiotic misuse and uncontrolled meteorologist damages (i.e., sewage

overflows). Particular attention is directed towards evaluating possible outcomes of an already

formed microbial resistance in surface water. A hypothetical analysis of the adverse health

effects through QMRA systemic approach based on empirical data and experts opinions is

addressed.

6. Results and Discussion

6.1. Hazard Identification

Fecal indicators bacteria (FIB) in surface and recreational waters includes primarily E.

coli and Enterococcus faecium (U.S. EPA, 1986). Both Escherichia coli and Enterococcus

facium are commensal microorganisms, as they live in the GIT of warm-blooded animals and

also humans (U.S. EPA, 1986; Haas, 1999; WHO, 2009). In 2001, E. faecium, was identified as

the most reliable fecal indicator in recreational waters (WHO, 2001). While some strains of E.

coli and E. faecium are harmless and not considered as pathogenic, both of these strains have the

potential to develop resistance to antibiotics and to transfer resistant genes to other bacteria

(Leclerq, 1997; Murray 1990; Hunter et al., 1992). Gram-positive E. faecium is an emerging

antibiotic resistant bacteria primarily resistant to vancomycin and virginiamycin (VREF and

SREF, respectively) (Snary et al., 2004). This strain exists at high levels in most animal species

and causes hospital-acquired infection, and hence poses a serious risk to immunocompromised

hosts (Snary et al., 2004). E. faecium was not of prime concern before the detection of its

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antibiotic resistance. Streptogramin or virginiamycin resistant E. faecium (SREF) have been

isolated from livestock sources (i.e., swine and poultry) in both European and U.S farms (FDA,

2004). However, despite the rising concerns associated with SERF and VREF, there is still a lack

of data to evaluate the dose-response related to E. faecium, since dose-response models were only

elaborated for pathogens.

According to Haas et al. (1999), risk assessors will have to include fecal indicators in

addition to pathogens within the MRA approach in the near future. In contrast to E. faecium, the

existing background and information data related to Gram-negative E. coli are much more readily

available (Haas et al., 1983) for another serotype of E. coli, primarily enterovirulent types. E.

coli is not only emerging as a resistant bacteria but also as a pathogenic strain, namely, E. coli

O157:H7 (EHEC). Unlike commensal E. coli, EHEC lives generally in cattle’s intestine, but is

seldom found in surface waters (DEQ, 2007). Furthermore, E. coli has been determined as the

best indicator of potential pathogens (U.S. EPA, 2002).

In 2002, EPA recommended that E. coli become the recreational fresh water indicator.

Originating from animals or human intestinal tracts, E. coli can be spread in the manure on land.

Furthermore, E. coli is often present in turbid water, since they attach to sediment particles from

the soil (U.S. EPA, 2002). Fecal materials administrated on the land can be transported to

streams, lakes and rivers through agricultural and storm water runoff or sewage overflows (U.S.

EPA, 2002).Furthermore, Palmeeter and Huber (1985) suggested that E. coli strains were able to

survive 6 to 7 days in lake water and endured 2 months in nutrient-rich sediment despite their

half-life in lake water of 36.5 hours.

Generally, E. coli can be transmitted to humans via fecal-oral route through consuming

food or drinking water. Typically, this strain does not cause any major adverse effects, but if it

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acquires antibiotic resistance or virulent genes from the environment, the transformed E. coli can

cause very serious harmful effects. These pathogenic E. coli includes primarily,

enterohaemorrhagic (E. coli O157: H 7 or EHEC), enteropathogenic (EPEC), enterotoxigenic

(ETEC), as well as enteroaggregative (EAEC) and enetroinvasive (EIEC) (Hunter, 2003). Most

of these cause severe diarrhea through either toxin production or epithelial tissue damages. The

very young children and elderly are the most sensitive population to pathogenic E. coli (Riley et

al. 1983).

While most of enterovirulent E. coli are infective at a high dose with minimal infective

doses of approximately 108 to 109 CFU/ml and 106 CFU/ml for ETEC and EPEC, respectively,

EHEC requires only a dose of as few as 10 cells to cause infection (USDA, 2005). These

enterovirulent strains acquiring antibiotic resistance would present a very serious danger to the

exposed population. Recently, a lethal strain of E. coli responsible for a deadly outbreak in some

countries in Europe was found in sprouts in northern Germany (WHO, 2011). This strain was

resistant to multiple antibiotics and caused the death of approximately 50 people and the

hospitalization of more than 3,000 cases. This bacteria was initially associated with EHEC due to

its antibiotic-induced verotoxin characteristics and hemolytic uremic syndrome (HUS). However,

this strain was E. coli O104: H4, a novel antibiotic resistant EAEC that acquired shiga like toxin

gene (WHO, 2011). Furthermore, Hamelin et al. (2006) have reported the detection of a high

level of pathogenic E. coli (29%) along with 8% unusual virulent ones in addition to 14 %

antibiotic resistance isolated from Great lake beaches in the U.S.

Increasing concern and awareness from the real risk that pathogenic E. coli originating

from water and the agricultural field can pose to public health has led U.S. EPA to set a strict

limit to the level of E. coli originating from potential environmental sources. For instance, the

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level of E. coli in surface waters should not exceed 235 Colony Forming Units (CFU) per 100/ml

(U.S. EPA, 2002); otherwise, recreational water quality requires a stringent investigation. Given

that antibiotic-resistance is the major focus of this research study, the level or concentration of

the microbial hazard, as well as its high prevalence in water, is of great concern. Several

detection methods have been established to identify E. coli . The appropriate methods are tightly

correlated to the laboratories’ economics and possible investment. Generally, conventional

standard methods and biochemical tests are often used in addition to molecular techniques

involving primarily, real time Polymerase Chain Reaction (PCR) and pulsed field gel

electrophoresis (PFGE) that are predominately used by the Center of Disease Control and

Prevention (CDC, 2005). Parameters that have been selected for this study were related to most

strains of enterovirulent E. coli except EHEC. An extensive microbial risk assessment research

study related to waterborne EHEC has been provided elsewhere (Haas et al., 1999).

6.2. Exposure assessment

A schematic diagram illustrating the dynamic integration of the conceptual modules of

bacterial hazard pathway in the modern agricultural system is shown in Figure 5. It includes two

different scenarios including the typical fluctuating level of E. coli in an unmedicated zone and

the worst case scenario that involves a medicated system that would generate antibiotic

resistance in the animal GIT prior to its spread in water surfaces and before eventually reaching

human population in recreational water. For instance, a sublethal dose of active antibiotic

residues in DGs designated for animal consumption would lead to the formation of AR during

storage. Direct administration of antibiotics to animals to promote animal growth or treat certain

diseases could also lead to antibiotic-resistance formation in animal GIT. Animal fecal materials

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(i.e., manure, untreated sewage) carrying potential hazards could be transported to the source

(i.e., surface waters) via agricultural or stormwater runoff and also water irrigations.

The hypothetical route exposure of the model from the source, surface water-to-

consumption (i.e., lakes, rivers), is under the scope of this risk assessment, and is shown in

Figure 5. Although E. coli concentrations in water surfaces undergo a dramatic fluctuations

and changes that depend on several variables including primarily the location (i.e., beaches,

north or south shorelines), weather and sanitation coverage, the likelihood level of E. coli that is

typically detected in the shoreline adjacent to beaches along with creeks and rivers is mostly in

the range of 103 to 107 CFU/L and is shown in Table 2. These concentrations could be further

increased by 10 to 100 fold by several other factors including uncontrolled weather occurrences

(i.e., manure, spills, untreated run off, sewage overflows) in a medicated agricultural zone

along with domestic animals and wildlife high densities. Recently, a research study has been

performed at the USDA Agricultural Research Service’s National Animal Centers in Iowa has

reported that E. coli level that is initially around 109 CFU/g (1012 CFU/L) within the animal GIT

(Edberget et al., 2000) would undergo a drastic increase of E. coli by 20 to 100 fold in swine

GITs once exposed to antibiotic treatment or feed (Sohn, 2012). Furthermore, recent reseach

study implemented by the Proceedings of National Academy of Science (PNAS) have

demonstrated a considerable increase of Proteobacteria in the medicated swine feces ranging

from 1 to 11% (Looft et al., 2012)

Furthermore, Wisconsin aquatic technology 2009, has revealed the presence of 10,000 to

100, 000 CFU/100mL of E. coli in agricultural runoff. Experts have also reported the presence of

approximately 100,000 CFU/100mL of E. coli strains in stormwater runoff, and between 250,000

to 500,000CFU/100mL in sewer overflows (Great Lakes Water Institute, 2009).

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6.3. Hazard characterization-Dose-response Assessment

The probability of infection associated to E. coli in this case is initiated from the

pathogenic E. coli and related serotypes broad continuum except EHEC. Thus, based on the

available review literature, as well as epidemiological investigations of outbreaks, human feeding

trials and sporadic case series, the endpoint for the dose-response is attributed to a severe

diarrhoeal disease in addition to antibiotic resistance. From a previous study developed by Haas

et al. (1999) related to EHEC dose response, the beta-Poisson model was demonstrated to fit

animal data better than did the exponential model, and the mathematical relationship led to a

statistically improved outcome over the exponential equation. The uncertain variables (i.e.,

volume of water ingested, hazard concentration), termed assumption cells in Crystal Ball ®

software, are assigned a probability distribution. These different distributions are subsequent to

the determination of the forecast called deterministic model in Crystal Ball ® software. It

provides the dose-response outputs via MCM attributed to the best-fit equation, which is beta-

Poisson in this case. This equation is presented in Table 4, as described by Haas (1983).

Although this beta-Poisson model is considered to be the best fit dose-response equation

for this case, it is formulated for the standard maximum likelihood and conducted on healthy

adult volunteers, and thus might not reflect the most sensitive subpopulation, namely, young

children (Haas, 1983). N50 is the dose at which approximately half the population (50%) is

infected, and d presents the average dose administrated to the population. In this case, the median

infectious dose (N50) as well as alpha parameter (the slope parameter of the equation) were

selected based on Haas (1999) model adopted to non-enterohaemorrhagic E. coli.

6.4. Risk characterization

133

This section integrates all MRA components (i.e., hazard identification, exposure

modules and risk characterization: dose-response assessment) to estimate the rate of illness. The

outputs resulting from the combination of these components can be evaluated and described from

different angles using different scales or units (i.e., days, years). The most appropriate way to

describe the risk estimate would be by including data that provide a simple and comprehensive

insight clear enough to be conveyed and communicated to the concerned public, including the

exposed human population and managers. In this hypothetical case, the evaluation of the risk is

performed via the Crystal Ball Tool to quantify the outputs via MCM. Risk estimate outcomes

cover possible probability of infection estimation from a typical and the worst case scenario as

well as the sensitivity and the scatter chart of the forecast models (dose or dose-response) to the

uncertain variables (Figure 7, 8 and 9). Detailed key findings, as well as data gaps and possible

solutions with intervention scenarios, are suggested in the next section.

6.4.1. Key findings- Limitations and data gaps

The output forecasting dose-responses that indicate a risk of approximately 50% to a

population of 2% to be infected are shown in Figure 7. However, the proportion of the possible

infected population could increase by 10 fold if the agricultural field is exposed to antibiotic

misuse exacerbated by storm run-off and sewage overflows. These levels of possible infection

could be considered high, since there is no way to underestimate a minimal proportion in order to

ensure a radical public health protection. Furthermore, the sensitivity chart indicates that the

uncertain variable parameters, namely, the concentration of E. coli in water surface is

contributing the most to the dose-response model with 92.1% (typical scenario) and 90.2% (the

worst case scenario) over the volume of water ingested of 9.8% 7.9%, respectively and 28.0%

(Figure 8). This chart is among the most informative Crystal ball outputs since it shows where the

134

most variability in the dose or dose-response model is present. Therefore, the model can be

adjusted or refined to reduce the dependence on the uncertain variable or variability (CAMRA,

2011). The solid positive correlation between the dose response forecast and the level of E. coli

for both scenarios (the typical and the worst case scenario) have been verified and confirmed by

the scatter charts (0.0003 and 0.9541, respectively) is shown in Figure 9.

Although this exploratory software offers a clearer vision of the impact of the uncertain

variables on the forecast models, namely, dose and dose-responses, there are still several caveats

and data gaps to consider in an attempt to offer a more complete insight. For instance, it would be

a considerable extension of the model effort to determine antibiotic residue concentration in

DDGS as well as their biological activity after heating. Therefore, a chemical risk assessment

approach could be addressed to further clarify the minimal lethal dose of antibiotics that causes

either adverse health effect or antibiotic resistance in vivo. However, the lack of a biological

epidemiology method that would enable estimating the extent of antibiotic-resistance transfer

from zoonotic bacteria in vivo could be a serious hindrance to the extension of the risk analysis.

From the microbial hazard standpoint, there has been a wide range of information related to

pathogenic microorganisms and their concentrations in different water sources. However, there

is still a need to explore ways of incorporating fecal indicators in the MRA list, and establish a

specific dose-response model for them. In addition to E. coli, it would be useful to explore other

fecal indicators, such as E. faecium, emergent antibiotic-resistant bacteria in the agricultural field.

Further, the available beta-Poisson modeling considers only healthy adult volunteers, and does

not involve sensitive humans or even an animal population.

7. Intervention Scenario-Conclusions

135

The establishment of preventive measures in agricultural field and adjacent industries was

deemed obligatory by legislators. In an effort to clarify the real outcome from indirect

consequences and unforeseen threats from large-scale antibiotics use in agriculture and related

fields, namely, biofuel industry, several surveys were performed. While the real size of the

problem remains incomplete to-date, there are several avenues and possible outputs that could be

generated from the MRA approach to elucidate statistically the risk estimate, and offer pragmatic

insights to risk managers for taking precautionary actions.

Various intervention scenarios could be undertaken to limit antibiotic residues and

possible resistance generation from biofuel system. Among the most emergent alternatives are

bacteriophages or biological antimicrobial applications. These antimicrobials, including

components from plant extracts (i.e., hope) or peptides (i.e., nisin), represent an effective and

inexpensive alternate choice, and some have a practical broad spectrum to sufficiently ensure

limitation of bacterial contamination of large-scale yeast fermentation systems, without

compromising environment biosafety. A regular solar disinfection SODIS (Solar Water

Disinfection) procedure created by Eawag (The Swiss Federal Institute for Environmental

Science and Technology, 1991) would reduce the hazard at least by 2 logs. An installation of a

cost effective membrane filtration systems in a recreational zone (i.e., reverse osmosis) would

filter not only the E. coli strains but most of the pathogenic bacteria and viruses. Viewed from

another angle, surface waters and sewages surrounding biofuel distilleries will have to be treated

adequately with the appropriate antimicrobial (i.e., chlorine or biological antimicrobials). It is

quite possible to start tracking microbial contaminants from the source via rapid molecular

methods to keep the ecosystem and environment pristine and prevent the environment from

becoming a reservoir for resistant fecal indicators or other bacteria.

136

These biosafety initiatives could be enhanced by increasing investment in research

laboratories and public health services, as well as motivating risk assessors to elucidate safety

problems. Given that antibiotic resistance has been of prime concern during the last decade, a

biological epidemiology method becomes essential in determining the extent of antibiotic-

resistance transfer from a zoonotic bacteria in vivo and in the ecosystem in general. Additionally,

later studies by Gullberg et al. (2011) have demonstrated that a low or sub-lethal dose of

antibiotics will have to be considered, given the potential of generating antibiotic resistance. It is

quite possible that a low dose of antibiotics would maintain and enrich resistance in the microbial

population, seen from an evolutionary angle. Overall, this model is a starting-point to the risk

assessment approach in an attempt to increase public awareness towards the real threat and the

indirect consequences that could result from large-scale disproportionate use of antibiotics in

agriculture or biofuel systems. Furthermore, this study supports public awareness on the

unforeseen risk from waterborne illness if preventive measures are not seriously undertaken or

rigorous intervention is not established.

This model could be amended or built upon, based on the current available data.

However, other uncertain variables, including primarily water sanitation coverage or solar

disinfection, could possibly provide a more accurate output for this research project. The

generation of more precise and real information would reduce uncertainties and fulfill gaps to

reach a 'closer to reality' comprehensive model.

137

Acknowledgements

This research was partially supported by grants from the South Central Sun Grant (U.S.

Department of Transportation) program, Novozyme North America, Inc., Franklinton, NC, and

the Institute of Food Science and Engineering, University of Arkansas, Fayetteville, AR.

138

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Figure 1. General QMRA framework based on the Classical concept

                

Problem Formulation 

     Hazard Identification 

   Exposure Assessment    Hazard Characterization: 

        Risk Characterization 

                

     Risk Estimation 

151

Table 1. Predominant Acute Contaminants in Major Hydrolysates in Yeast-based Ethanol Fermentaion

Raw Materials Major Contaminants

Bacteria Yeast references

Corn:

- Wet-mill

- Dry-grind

1. Frequent:

1.Gram-positive

Lactobacillus (55%)

L. casei

L. fermentum

L. salivarius

Leuconostoc

Pediococcus

Enterococcus

Weisella

1.2. Gram-negative

Acetobacter

Gluconobacter

Infrequent

Propionobacterium

Fusobacterium

Clostridium

Streptococcus

Lactococcus

Bifidobacterium

Wild S. Cerevisiae

Wild yeasts:

Brettanomyces

Candida

Debaryomyces

Hanseniaspora

Hansenula

Klockera

Pichia

Rhodotorula

Torulopsis

(Skinner, K.A.

2004; Bischoff,

K.M. 2009; )

(de Souza, A.T.

2005; Abbott,

D.A. 2005;

Back, W. 1987)

(Geros, H. 2000;

Ciani, M. 2003).

(Lushia, W.

2005)

(Elstein, C.

2008)

152

Bacterioides

Sugarcane:

- Molasses

- Juice

1. Frequent:

Gram-positive

Lactobacillus (55%)

Leuconostoc

Pediococcus

Enterococcus

Weisella

1.2. Gram-negative

Acetobacter

Gluconobacter

Infrequent

Propionobacterium

Fusobacterium

Clostridium

Streptococcus

Lactococcus

Bifidobacterium

Bacterioides

1. Wild S.

cerevisiae

2. Wild yeast:

2.1 Juice acute

contaminants

Dekkera

bruxellensis

Clavis

poralusitaniae

Pichia galeiformis

Candida tropicalis

Zygoascu

shellenicus

Candida fermentati

2.2 Molasses acute

contmainants

Dekkera

bruxellensis

Clavispora

lusitaniae

( Basilio, A.C.M.

2008; Campbell, I.

1987; Kleyn, J.

1971)

(Lushia, W. 2005)

(Elstein, C. 2008)

Lignocellulosic 1. Frequent: Wild S. cerevisiae (McMillan,

153

Biomass:

-Woody materials

- Agricultural

residues

1.1. Gram-positive

Lactobacillus (55%)

Leuconostoc

Pediococcus

Enterococcus

Weisella

1.2. Gram-negative

Acetobacter

Gluconobacter

1. Unfrequent

Propionobacterium

Fusobacterium

Clostridium

Streptococcus

Lactococcus

Bifidobacterium

Bacterioides

Rhodotorula

Brettanomyces

Dekkera

Candida

Pichia

J.D.1993, Bernett,

J.A. 1975).

(Lushia, W. 2005)

( Basilio, A.C.M.

2008; Campbell, I.

1987; Kleyn, J.

1971)

154

Corn based dry- Grind Processing

Figure 2. Bioethanol and major Co-products Recovery from Corn-Based Dry-grind

               Grinding 

      

           Cooking 

              Hydrolysis 

        Fermentation 

Distillation/Dehydration 

Cleaning 

     Enzymes 

    

Bioethanol 

  

CO2 

      Yeast 

        Centrifugation          Evaporation 

  Distillers 

Solubles Distillers Dry Grains

  Distillers Dry Grains with  

Solubles (DDGS) 

              Drying 

155

Figure 3. Corn Dry Grind-based Biofuel Mass Balance based on literature review (Mei et al.,

2005)

      

          

        Bioethanol Distillery 

                    

Corn (1 tons) 

Water (2.68 

tons) 

 

Ethano

l (0.32 

tons) 

DGs (0.33 

tons) 

And 

Virginiamycin 

X ppm? (see 

Figure 4)  

Major Recycled 

components: 

‐ Wastewater‐thin 

sillage 

‐ Others (i.e., 

condensate, CO2 

scrubber).

CO2 

(0.31 

Virginiamycin 

0.5 ppm‐2ppm 

156

Figure 4. Triangular Distribution of Antibiotic Residues (min=0ppm; Likeliest=0.20ppm;

Maximum=0.28ppm)

157

 

Normal flora, E. coli in 

farm animal (i.e., cattle, 

swine). Animal GIT 

exposed to a low or a 

high dose of antibiotic 

Fecal material carriers 

(.e., sewage, manure, 

irrigation waters) 

Surface waters: recreational water 

(i.e., Milwaukee Lake) 

Large‐scale medicated 

system exacerbated by 

runoffs and overflows

 

Animal exposed to 

antibiotics via feed or 

other practices (i.e., 

growth promoter or 

disease therapy)

Antibiotic resistance formation in animal guts . E. coli and 

antibiotic resistance number increasing in animal GIT (Looft 

et al., 2012)

  Fecal route 

 Runoff (i.e., 

agricultural or 

storm runoff and 

sewage overflow) 

 

  unmediated animal 

farms within a 

typical conditions 

Normal flora E. coli in 

the GIT of farm 

animals  (i.e., cattle, 

swine)  

Fecal materials 

carrying 

microorganisms 

including E. coli. (.e., 

Surface waters: recreational 

water (i.e., rivers and lakes) 

Human consumption from 

recreational water 

Figure 5. Hypothetical Model Comparaison from Harvest-to-consumption Dynamic Flow : (1) unmedicated farm animals;

(2) Worst case scenario: farm animals exposed to antibiotic usage and aggravated by runoffs and sewage overflows

 

                 

(1) 

 

                 (2) 

Antibiotic usage (i.e., 

virginiamycin, penicillin) 

158

Table 2.The most likely range of  E. coli  level that is located  in the areas  adjacent to the beaches of    

the U.S recreational Great lakes. 

Major recreational lakes in the 

U.S. 

 E. coli level detected  References 

Lake Michigan (i.e., Wisconsin 

harbor) 

From  10 3 up to 2.7 x 105 CFU/L  ( Great Lakes Water 

Institute, 2009) 

Indiana Lake  From 2 CFU/L  to 8 x 106 CFU/L  The Indiana Department 

of Environmental 

Management, 1994‐1995) 

Lake Erie (i.e., Ohio)  From 6500 to 7.1 x 105CFU/L in 

east end of the beach 

(Sigler and Esseili, 2008) 

Lake Huron  up to 1.6 x 107 CFU/L   (Kon et al., 2007; 

Palmateer and Huber, 

1984) 

Lake Ontario (i.e., Hamilton)   up to  1.14 x 105  CFU/g dry sand 

in wet foreshore. 

 

 

(Edge and Hill, 2007) 

Milwaukee Harbor  Average E. coli 8.16 x 105CFU/L 

Range was <103 to 3.9 x 

106CFU/L 

(McLellan, 2004; 2009)  

( Great Lakes Water 

Institute, 2009) 

 

 

159

Table 3. Probability Distribution Assignment to Uncertain Variables

Parameters Distribution Units References

Resistant-E. coli in surface water Recreational pathway Volume ingested Log Normal

Mean 0.085 Liters/cap/day (Dufour et al., 2006)

Std. dev. 0.101 (Haas et al. 1999)

Typica E. coli CONC in lakes Log Normal

(U.S. EPA, 2002; Greater Milwaukee Watersheds Pathogen Sources Identification, 2009)

(37% pathogens)

3.30E+05 2.92E+07

CFU/liter Assumption:

(Hamelin et al., 2006)

Mean Std.dev.

(100 fold increase for the worst case scenario)

160

Figure 6. The uncertain variable lognormal distribution; (1) volume of water ingested; Typical E.

coli concentration in recreational lakes

(1) 

(2) 

161

Table 4. Best fit dose-response model adopted to non-enterohaemorrhagic strains of E. coli

Dose-response Model

beta-Poisson

References

Equation

a

1 1

(Haas,

1999)

Alpha ( α)

0.175

d

d1*d2 b

N50

2.55 106

a: P(d) is the probability of illness or infection; d is the average dose administrated to population; α is the slope parameter of the equation adopted from Haas (1983). b: d1: Ingested volume of water; d2: Hazard concentration in the source (i.e., surface waters);

162

Figure 7. Dose response simulation outputs via cumulative function; (1) Unmedicated water

surfaces; (2) The worst case scenario

(1) 

(2) 

163

Figure 8. Chart indicative of the unceratin variables contribution; (1) Unmedicated system; (2) The worst case scenario

(1) 

(2) 

164

Figure 9. The Scatter Chart verification; (1) The typical unmedicated scenario; (2) The worst case scenario

(1) 

(2) 

165

Chapter four

Qualitative Exposure Assessment for Biotechnology Applications in agricultural Systems

\

166

ABSTRACT

Development of agricultural biotechnology has opened up new avenues to bio-based economies

and is beginning to benefit the biofuel industry. Bioethanol production from bio-renewable

feedstocks is emerging as one of the biofuel sources for replacing at least some of the petroleum-

derived gasoline currently used in automobiles. Genetic tailoring and modification applied to

crops and microorganisms are of prime interest to agro-based biofuel industries that aim to

increase their yield with less water and better land use. This study provides a qualitative risk

assessment approach on the exposure of the human population to genetically modified organisms

(GMOs) carried by a bacterial vector originating from biofuel system environment. Particular

focus in this paper has been directed towards comparing the most prevalent biofuel industries in

the U.S. namely, corn- and lignocellulosic-based biofuel on the release of altered genes through a

microbial vector to the environment. A descriptive dynamic tree flow modelings have been

established to assess particularly the risk of exposure to the bacterial-mediated GMOS from

harvest-to-consumption in bioethanol system. These qualitative risk approaches adopted to

biofuel systems, range from transgenic crops along with genetically engineered microorganisms

to human consumption. A generalized Agrobacterium -vector of the altered genetic materials

involved in agricultural biotechnology has been constructed to exhibit the sources of the genes

involved in biofuel systems. This study has elucidated sources and different pathways of the

microbial hazard carrying altered genes. To further estimate the probability of outcomes from the

direct and indirect consequences namely, altered genes namely, allergens and toxins namely, Bt

toxins (.i.e., Crystal protein, Cry) and also antibiotic resistance that would reach human

population through commensal bacterial vectors if scientifically strong preventive measures are

not undertaken

167

1. Introduction

Owing to the awareness of the post-petroleum based-fuel paradigm, agricultural-based

biofuel has regained considerable attention from politicians and scientists in the U.S. and

worldwide (Youngquist, 1999). The finite gasoline reserves and serious concern towards

greenhouse gas emissions and climate change have given rise to what has been referred to as a

green revolution (Caledria et al., 2003; Dahlberg, 1979). Increased interest in renewable

bioenergy has initiated farmers and industrial entities of under- developed countries to invest in

ethanol-convertible crops and foresee cellulosic materials as sustainable biofuel feedstocks that,

unlike current corn-based biofuel, do not directly compete for food and feed (Demirbas,1998).

With the passage of the Policy Energy Act (PEA 2005) and Independence Security Act (2007),

there has been a notable development in corn-based bioethanol in the U.S. increasing from: less

than 200 million gallons per year (757 million liters) in 1980 to approximately 12.8 billion

gallons (48.5 billion liters) in the year 2010 in order to partially (4.5%) displace fossil fuel oil in

the transportation sector (Congressional Research Service, 2010). Lately, there has been

substantial competitive grant support from the United States Department of Agriculture (USDA)

and the government for sustainable bioenergy production in an attempt to meet oil imports

independence and offset petroleum-derived gasoline with respect to rural economic development

and expansion (USDA, 2011).

The continuous worldwide expansion of biofuel industries at an exponential pace has led

to renewed substantial growth in modern biotechnology (James, 2002; Sanchez and Cardona,

2008). To date, agro-biotechnology involves mainly the genetically modified organisms (GMOs),

including both trans-genic crops and genetically transformed microorganisms (Holmes, 2010).

GMO manipulations offer economic advantages to investors in terms of a maximal product yield

168

based on limited land and water use (Ramasamy, 2007). During the last decade, the U.S. has

become the world leader in the cultivation of genetically modified (GM) crops involving

primarily corn and soybeans. At the beginning of 2010, Brazil achieved the second world’s

largest increase in soybean cultivation with a total area of approximately 23 million hectors

( Cerdeira, 2011). It has also been reported that GM crops in the U.S. account for approximately

46% of the world acreage followed by Brazil, Argentina and Canada, as well as some Asian

countries. In European countries, regulations imposing labeling and guidelines have substantially

mitigated GMs investments, and thus there is less transgenic crops cultivation in European

countries (RPT, 2002). Currently, China is emerging as one of the world’s leaders in

biotechnology, particularly in the cultivation of GM's rice and cotton, followed by India in

development of GM fiber primarily cotton (Huang et al., 2001; 2002)

However, as the investment in biofuel industry and agricultural biotechnology expands,

the risk of adverse health effects from GMOs increases (Krimsky, 1991; Moreau and Jordan

2005). Lignocellulosic-based biofuel involves several potential uses of GM from the farm (GM

crops) to the biofuel processing (GM fermentative microorganisms) over a broad continuum

(Phillips, 2008). However, accidental releases of GMOs carried by the most prevalent bacterial

vectors in biofuel system present a potential risk, and could generate hazardous unforeseen

outcomes to the environment that would pose a serious danger to the ecosystem and human

health (Krimsky et al., 1989; Bassabara et al., 1999). Unlike lignocellulosic-derived bioethanol

corn-based biofuel, processing could include GM crops as a feedstock (transgenic crops), but

does not necessarily include GM microorganisms during processing.

Biotechnological applications have displayed several economic benefits, primarily from

pest and herbicide-resistant transgenic crops, as well as engineered strains (Brookes, 2007;

169

Brookes and Barfoot, 2009a). Although Collier (2009) has concluded that novel biotechnology is

imperative to face the exponential growth of the human population, GMOs remain highly

controversial in the public and political forum. Within the last decade, there has been a number of

different groups calling for a natural preservation of the ecosystem and biodiversity. Researchers

have also concluded that 95% of genes in the biosphere are unknown, and the adverse outcomes

of GMOs could present a real risk to the environment from an irreversible unforeseen

dissemination (Epstein, 1998; Ho et al., 1999). However, to date, direct proof of adverse effects

from GM practices is lacking to support cessation of biological improvements and economic

gains from contemporary biotechnology (Miller, 1998; Brookes and Barfoot, 2009b).

This serious debate has led researchers and governmental regulators to explore ways for

preventive and pragmatic solutions to counter biotechnological constraints (Krimsky, 1992).

Among options, the scientific-based approach or risk analysis has received considerable attention

for its value in providing transparent data and a comprehensive model. Microbial risk

assessment (MRA) is an emerging systematic tool derived from the risk analysis concept (NRC,

1983; U.S. EPA, 1986; Haas, 1983; ILSI-RSI, 1996). It provides a quantitative or qualitative

estimation of the likelihood, as well as the severity, of infections that could result from

exposure of susceptible human populations to, for example, pathogens (Haas, 1983; Miller,

1998; FAO/WHO, 2003) . Several MRA frameworks have been modified and improved to fit

the continued economic evolution over the years, thus adding risk management and

communication to the MRA structure (NRC, 1983, Haas, 1983, U.S.EPA, 1982; ILSI-RSI,.

1996; FAO/WHO, 1995, CAC, 1999). The basic risk assessment components remain the same,

including primarily, hazard identification, hazard characterization, along with dose-response

assessment and exposure assessment, followed by the risk characterization (Figure 1). This

170

approach involves a qualitative exposure estimation that aims to elucidate major steps for

determining the risk and thus exploring ways to optimize operations and mitigate the risk to a

more tolerant level for the ecosystem and human population. According to Snary et al. (2004) ,

the MRA model, if it is efficiently used, would fullfil certain gaps and limit uncertainties for

more scientific-based regulations and control for risk. Hence, it would ensure environmental

safety and reduce animal and human health exposure to microbial hazards. Consequently, this

systematic approach would enable GM technology proponents to overcome uncertainties and

increase the public’s GMs acceptance (Snary, 2004).

Overall the current paper framework will be of a qualitative and descriptive nature that is

not directly related to a more quantified evaluation of risk. A qualitative risk analysis are

typically used for elucidating the risks to determine whether they deserve further examination

based on the probability of outcomes, and can be valuable in the first step of risk management

initiatives described in FAO/WHO (2002). This research also encompasses the possible MRA

implications in biofuel industry to face modern biotechnology controversial issues. It provides

what is currently known on GMOs applications in agricultural-based biofuel from transgenic

plants to genetically engineered microorganisms. It examines the possible adverse health effects

resulting from the use of recombinant DNA in biofuel systems. Particular attention is given to a

comparative study between corn-based and lignocellulosic-derived biofuel on the accidental

dissemination of altered genes into the environment through potential bacterial vectors namely

commensal microorganisms. This paper determines the first steps of qualitative risk assessment,

namely, hazard identification and exposure assessment of the most up-to-date scientific-based

preventive method, MRA that could be involved thoroughly in the biofuel system to ensure

171

public health before adverse health effects from altered-genes- bacterial mediated vector become

a reality rather than a concern.

2. Risk assessment background and current trends

In the late 1950s, food safety quality worldwide received considerable attention by

World Health Organization (WHO) members, along with the United Nations of Food and

Agriculture Organization (FAO) (Lupine, 2000). Their major concerns involved the extensive

use of food additives, as well as novel pesticide agents, incorporated mainly during food and

agricultural storage. Debates between FAO and WHO about food safety and fair standards

implementation led researchers to look for preventive methods, including science-based

systematic information such as risk assessment (FAO, 1999). Between the 1950s and 1960s,

there was the establishment of the joint FAO/WHO of Expert Committee of Food Additives

(JECFA), followed by Meeting on Pesticides Residues (JMPR) in 1960. At that time, the risk

assessment tool was primarily designed to assess chemical hazards, namely, carcinogenic and

toxic components (NRC, 1983; U.S. EPA, 1982). In 1962, WHO and FAO came to a decision to

create a joint FAO/WHO Food Standards to serve Codex Alimentarius (Codex) by providing it

science-based international guidelines. Codex signed an agreement with WTO aiming to create

standard measures to protect public health and ensure equivalent food product quality in

international trade. Unlike JMPR and JECFA, involving independent experts on evaluating risk

estimates, Codex involves international standards and national governments committee

members.

The Codex Joint on global science-based norms was created to reach a consensus and

reduce international trade conflicts among all Codex member countries (Lupien, 2000). In early

1995, the Uruguay Round decided to replace the General Agreement on the Tariffs and Trade

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treaty (GATT) by WTO, involving permanent member commitment of 123 countries. This

replacement was accompanied by an agreement on the implication of Sanitary and Phytosanitary

rules (SPS) that involve animals and plants, as well as food safety regulations (WHO, 1998). As

basically described and improved to fit international trade and contemporary managers (NRC,

1983, U.S. EPA, 1982, Haas, 1983; ILSI-RSI , 1996; FAO/WHO, 1995; CAC, 1999), MRA is an

emerging scientific approach, which provides a holistic risk characterization that enables greater

hazard control and equivalent food-safety products in the current worldwide market. A

quantitative basis and systematic guidelines directed towards increasing investors awareness of

implementing action permits monitoring of microbial risk and improvement of food safety by

minimizing the impact of contaminations and foodborne diseases. Furthermore, Codex member

countries recognize that an appropriate installation of food legislation requires a solid

quantitative science–based document. It further ensures consumers' health and helps to prevent

conflicts by reaching a consensus among Codex members all over the globe.

2.2. Science-based regulatory tool

Given the increase of international food trade and the willingness of several nations to

establish a safe and fair international trade associated with sufficient justifications to policy

makers, consumers and suppliers, MRA has become an option of interest to food safety

regulators to adjust and align equivalent food standards worldwide. MRA has gained more

interest since the WTO, as well as Sanitary and Phyto-Sanitary (SPS), agreements have been

implemented to fit international trade food safety requirements. As mentioned previously, WTO

relies on the Codex Alimentarius to evaluate food safety and hygiene based on the risk

assessment approach (Reij and van Schothorst, 2000). In the U.S., the regulatory process from

farm-to-consumption task is distributed among three separate major governmental agencies,

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primarily including the Environmental Protection Agency (EPA) under the scope of Toxic

Substances Control Act (TSCA) reporting regulations, United State Department of Agriculture

(USDA) and the Food and Drug Administration (FDA). The EPA ensures the regulation of GM

crops for the environment safety, while the USDA evaluates growth safety of GM crops, and the

FDA evaluates whether GM plants-derived food is safe for consumption (Whitman, 2000).

3. Emerging trends: bioethanol production in the U.S

3.1 Corn-based biofuel

The dramatic depletion in petroleum-derived fuel, along with environmental safety

concerns, has sparked interest in a bio-renewable feedstock derived fuel. Although several

countries in Europe and Asia have already invested in biofuel industries, the U.S. remains the

world leader in bioethanol production with approximately 57.7% of the total world production,

followed by Brazil (RFA, 2010). In the U.S., corn crops have emerged as one of the primary

feedstocks for starch and bioethanol production leading to the generation of approximately 13.

23 billion gallons (50.08 billion liters) of ethanol at the end of 2010 (RFA, 2011nts being

distributed in 29 states in the U.S. Midwest “corn belt” with nine more ethanol distilleries under

expansion). Current RFA statistics indicate that most of the 204 operational corn-plants in the

U.S. originate from either wet-milling (33%) or dry grind (67%) (Bothast and Schlicher, 2005).

The major steps involved in dry grind and wet mill corn-based bioethanol production

accompanied with co-products generation are illustrated in Figures 2 and 3. Unlike the corn wet

mill process that uses intensive energy to separate corn components, such as germs, gluten starch

and fibers, to generate livestock co-products, including corn gluten feed/meal along with crude

oil, the dry grind process is considered to be more economically feasible, since it requires less

capital investment per gallon of ethanol produced (Bothast and Schlicher, 2005). Moreover, the

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dry grind process involves fewer steps than wet milling. During the dry grind process, corn is

ground and turned into a mash. This mash is cooked before the addition of enzymes, subsequent

to saccharification and yeast-based fermentation. The mixtures, including solid loading and

bioethanol, are separated via distillation to form ethanol and then centrifugation, along with a

drying processes to form distiller’s dry grain with solubles (DDGS) designated to animal feed.

According to RFA (2010), the increase of biofuel production is strongly related to livestock by-

products because they provide a substantial financial margin to retain ethanol distillery financial

solvency. Typically, for every single bushel of grain, one third is transformed into animal feed

primarily as DDGS. The RFA study (2011) has also reported that the U.S. produces

approximately 35 million metric tons (mmt), and most of the DDGS (approximately 90%) was

fed to animals, including swine, beef cattle and poultry.

Although the corn ethanol industries and derivative co-products have fulfilled a major

proportion of the economical requirements, the increasing ethical and economical concerns

regarding food-based biofuel has shifted researchers' focus towards looking for a non-food

massive biomass, such as cellulosic materials, that could contribute to offset the 140 billion

gallons of petroleum derived gasoline used each year for the automobile sector in the U.S

(Corredor et al. 2007). The EISA agreement has been mandated to set the limit for ethanol corn

production to 15 billion gallons (56.78 billion liters) for the year 2015, and projects the

generation of 36 billion gallons (136.27 billion liters) of bioethhanol by the year 2022, with 21

billion gallons (79.49 billion liters) produced from lignocellulosic feedstocks (EISA, 2007;

DOE, 2010). Furthermore, the study envisages the possibility of displacing 30% ( 60 billion

gallons) of petroleum derived gasoline from cellulosic materials by the year 2030 (Perlack,

2005). These optimistic projections have motivated more research on approaches to make

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lignocellulosic-derived ethanol cost effective over corn-based ethanol and equalize petroleum-

derived gasoline price.

3.2. Lignocellulosic-based biofuel

Lignocellulosic feedstock is the most prevalent renewable source worldwide. It primarily

includes agricultural and woody- forest residues and energy crops, including primarily

Miscanthus and switchgrass, along with industrial and municipal solid wastes (MSW)

(Pettersen, 1986). Although lignocellulosic-based biofuel production has only been performed

at a pilot scale level, the EISA 2007 has already projected the production of 3 billion gallons

(11.35 billion liters) of cellulosic-drived biofuel for 2015 and 16 billion gallons (60.56 billion

liters) for the year 2022, respectively. Additionally, Perlack et al. (2005) have envisioned that in

the U.S. there is the potential of approximately one billion tons of cellulosic biomass available

each year, along with subsequent conversion to ethanol that could replace 30% of petroleum-

derived gasoline in 2030. Another study has demonstrated that this estimation would be

achievable if lands were well managed and exploited by cultivating 70% of the energy crops in

reserved lands, in addition to agricultural and forest residues, to face feedstocks' high-cost

(Khanna, 2011).

Noteworthy is that the EISA (2007) projections were anticipated by politicians and

scientists despite some technical and economic issues interrupting cellulosic-based biofuel

production. The major steps involved in cellulosic-based bioethanol and potential co-products

are illustrated in Figure 3. Pretreatment upstream operation accounts for one third of the total

operation costs, and presents the most important technical issues due to the highly lignin-

recalcitrant material. Additionally, the high level of non-conventional yeast convertible pentose

sugar in cellulosic material constitutes a considerable barrier to ethanol productivity.

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Furthermore, the addition of high-cost enzymes to the hydrolysis operation is of particular

financial concern to the cellulosic biofuel production (Lynd, 2005). In an effort to determine

feasible solutions to meet EISA (2007) perspectives, several research projects have been

implemented that aim to circumvent the key technical and financial issues to enable cellulosic-

based biofuel availability in the market to become reality within the next decade.

Currently, low-cost pretreatment, including the combination of sulfite pretreatment to

overcome recalcitrance (SPORL) and steam explosion with catalysts, is of prime interest due to

its potential to cope with cellulosic feedstock commodity product and versatility (Zhu and Pan,

2010). Furthermore, the emergence of GMOs technology has given rise to newly modified

thermo-tolerant microorganisms integrating both cellulolytic and fermentative ability in one

single strain, thus enabling the simultaneous saccharification and combined-fermentation

(SSCombF) processes, as well as consolidated bioprocessing (CBP) to take place in one single

vessel for a more cost-effective operation. Ladisch et al. (2010) have also demonstrated that

lignin co-products generated from the cellulosic-based biofuel process would be a potential self-

energy-sustaining system. Additionally, inedible cellulosic biomass, a lignin co-product, has the

potential for a subsequent conversion via fungal microorganisms, including primarily white rot

fungi, Basidiomycetes, to form fungal proteins that can be used for animal or human

consumption (Zadrazel, 2010). However, GMOs, could also pose a serious threat to

environmental safety and to biomass-based bioethanol production sustainability, and these

potential issues should thus be addressed carefully.

4. Genetically modified organisms (GMOs)

4.1. Background overview

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Although the public has been unaware of the genetic alteration concept until over recent

times, modifying genomes in organisms via breeding approaches has been carried out over a long

period of time (Phillips, 2008). At the end of nineteenth century, physiological genetics started

to emerge over the classical theory of chromosome heredity, along with the segregation and

inheritance law of Mendel (Burian and Gayon, 1999). At that time, several disciplines, namely

physics, biology and virology, started to interact in an effort to achieve a scientific understanding

of living organisms. From this perspective, the molecular explanation of life arose in the

Rockfeller Institute of New York in the late 1930s as a novel discipline named by Warren

Weaver, molecular biology (Abir-Am, 1985; Sakar; 1991). The extensive bibliography of

molecular biology history will only be summarized in this brief overview. Between 1926 and

1960 there were considerable advances in physiological genetics that were devoted to explaining

the genes and proteins relationship from the Drosophila fly model (Morgan, 1926), along with

Neurospora fungus (Beadle and Tatum, 1941) to the new model organism, namely,

bacteriophage by Delbrück and his group in the beginning of 1940 (Delbrück, 1949; Stent,

1968; Stent and Calendar, 1978; Luria, 1984). In 1944 Oswald Avery et al. (1944) demonstrated

that genes were composed of Deoxyribonucleic Acid (DNA). Avery’s findings were then

confirmed by the prominent experiment that explored chemical components of phage (Hershy

and Chase, 1952). The DNA discovery was subsequently followed by the elucidation of the

double helical structure of the DNA (Watson and Crick, 1953), followed by the eminent virus life

cycle and phage replication revelations of Lwoff (1954). In the earlier 1960s Jacob and Monod

won the Nobel Prize for their considerable accomplishment in the regulatory genetics, primarily

in the description of the relationship between the DNA and its related proteins involving

primarily Ribo Nucleic Acid (RNA) messenger intermediary (i.e., gene expression).

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More modern genetically engineered approaches that involve precise manipulation of

genomic vectors (i.e., plasmid construct and recombinant DNA) in bacteria and mice began

between 1972 and 1974, respectively (Cohen et al., 1972, 1973; Jaenisch and Mintz 1974;

Arnold, 2009). In the early 1980s the development of insulin-producing microorganisms

(Johnson, 1983; Crea et al. 1978) became commercially available in the medical field ( Goeddel,

1979; Time 1982). The rapid evolution of agricultural biotechnology by the end of the twentieth

century then gave rise to genetically modified crops (James, 1996; 1997).

4.2 . Global agricultural biotechnology sources and applications

Emerging agricultural biotechnology approaches have been widely used to ensure

biological and economical benefits from the extensive cultivation of potential GM crops

(Brookes, 2007). Increasing yield associated with less land erosion and water use is among the

most desired benefits from transgenic plants (Ramasamy, 2007). In 2006 pest and herbicide-

resistant GM plant cultivations, including mainly soybeans and corn, reached approximately

101.17 million hectares across 22 countries (James, 2008). Currently, there are almost 150

million hectares of GM crops planted in 25 countries in the world. The U.S. alone has almost

50% of the world’s transgenic plants (i.e., soybeans and corn) with approximately 66.7 million

hectares, followed by Brazil and Argentina that account for approximately 25.4 million hectares

and 22.9 million hectares, respectively (EDP, 2011). Although India and China are emerging as

potential sources of GM plant cultivators primarily with non-food fiber crops (i.e., cotton), their

GM plants account for only approximately 9.4 million hectares and 3.5 million hectares,

respectively (Compass GMO, 2009). The worldwide most prevalent transgenic plants, along with

their related major GMOs techniques and sources used, is summarized in Table 1.

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Although agricultural biotechnology achievements are substantial, this research study

places the emphasis on the most important accomplishments that have been achieved so far.

Currently, both herbicide tolerance and pest-resistance are among the most prevalent GM traits

that are used in agronomical biotechnology (James, 2008; EDP, 2011). The most common

herbicide tolerance is achieved by the insertion of glucophosate and glycofosinate resistant gene,

namely, 5-enolpyuruvylshikimate-3-phosphate synthetase (EPSPS) to the target plant (OECD,

1999; Padgette et al., 1996). However, insect resistance is enabled by genes that originate from

the well-known Bacillus thuringiensis strain (Bt) that inserts its toxic crystal protein into insect

stomachs and causes host cell death (van Frankenhuyzen, 1993). Hence, plants transformed by

Bacillus thuringiensis genes for pest resistance are called Bt crops. The Bt toxin can be

incorporated to the plant cell via different techniques. There are substantial transformation

techniques available, ranging from indirect delivery-system-based Agrobacterium to the most

commonly used direct transfer Agrobacterium microprojectile bombardment (biolistic) method

( Klein et al., 1987; Koziel et al., 1993; FAO, 2009).

4.3. Gene transfer mechanisms

Basically, the delivery-system-based Agrobacterium method has been widely used to

form a transgenic plant. This technique, also called binary-vector Agrobacterium, is summarized

in Figure 5. It requires first the isolation of the gene of interest for its “desired trait” before its

insertion into a delivery vector to form the recombinant DNA (rDNA) (Jackson et al. 1972;

Kiermer, 2007; Berg and Metz, 2010). The most common transfer and cloning vector used in

medical or agricultural biotechnology are bacterial plasmids from Escherichia coli, primarily for

their ability to generate extensive copies of the desired gene (James, 2008). The desired “trait”

including herbicide tolerance gene, EPSPS (Marketed name, Roundup Ready®) (Padgette et al.,

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1996) or pest resistance, Bt genes (i.e., Cry1A), are most commonly isolated from soil

microorganisms or plants (Agrobacterium tumefaciens and B. thuringiensis).

Modification of these target genes is crucial, however, to enable them to be translated into

protein once they reach the plant. Typically, E. coli plasmid constitutes the ideal platform and

cloning vector (Dyer, 1996) for translator genes (i.e., promoters, terminators) (Padgette, 1996;

Harison, 2001). The target gene requires a link with other translator genes, most of them

originating from plants and microorganisms, including promoters and translators (.i.e.,

cauliflower mosaic virus (CamV35S), nopaline synthase (nos) from Arabidopsis plant) that

enable the target gene transcription and stop signals (Nida etl., 1996; James, 2008). In addition, a

selection gene marker has been shown to be essential to the DNA delivery package to aid in the

detection or tracking of the transformed cells. Only the transformed cells carrying the selective

marker will be regenerated and transferred to the mediated transformation strain. The desired trait

is then transferred to the modified deactivated tumor-inducing plasmid Ti- plasmid of the plant

mediated-transformation strain , Agrobacterium tumefaciens, through the DNA recombination

technique (USDA-APHIS, 2000; Gelvin, 2003; Graham: 2009).

A. tumefaciens strain causative of crown gall disease upon its insertion of its Ti plasmid

has been used for its ability to infect plants and transfer genes into callus embryonic plant tissue,

callus (USDA-APHIS 2000; Chilton, 2001). However, the gene is transferred from the delivery

system into Ti plasmid through DNA recombination. This mechanism is enabled by the cleavage

of the target gene at specific sites, and ensured by endonuclease restriction enzyme (Arber et al.,

1979). The same enzyme is also used to cleave the host cell DNA before ligation via DNA-

joining enzyme, ligase (Zimmerman et al., 1967). Therefore, the transformed Ti plasmid

(deactivated from tumor-inducing mechanism) is subsequently injected into the tissue culture or

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plant embryos (callus), prior to a successful regeneration. A successful transfer of the desired

gene to the tissue culture chromosome is detected via the selective marker incorporated in the

plasmid vector. Thus, plant regeneration is ensured via only one cell successfully injected to the

plant chromosome before its multiplication and the propagation in the plant system. Molecular

techniques including Polymearse Chain Reaction (PCR), Southern hybridization and DNA

sequencing to confirm gene translocation and its inheritance by the plant (FAO, 2009). These

molecular methods enable tracing the inserted gene in the plant and to make amplification of

the target gene via an extensive number of copies, and its amplification further confirms its stable

inheritance by the plant. While Agrobacterium- binary vector requires an intermediary

transporter to transfer the desired allele, direct gene transfer methods do not require a delivery

system to enable gene transfer to the recipient organism. Currently, there are several direct gene

transfer methods that include primarily Agrobacterium microprojectile bombardment (biolistic),

along with chemical mediation and electroporation, as well as microinjunction (Klein et al.,

1987; Biotechnology 4u, 2009). However, Agrobacterium mediated recombination is preferred to

direct methods since it leads to lower damage to the plant tissue (Koziel et al., 1993). Direct

techniques with the most widely used biolistic method have been extensively covered by Food

and Agriculture Organization (FAO, 2009).

4.4. GMOs practices-Current applications and future prospects

Although gene transfer technology is continuing to deploy efforts to achieve optimal

transformation, achievements in genetic trait isolation has also reached considerable advances to

ensure pesticide and herbicide greater resistance (Carpenter et al., 2002). Basically, the Bt gene

is variably pathogenic to insects and does not have the same effect in different pests (Van

Frankenhuyzen, 1993). For instance, Bt thuringiensis that includes Cry1A and Cry1B delta-

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toxins is known for its effect against Lepidoptera and Diptera insects, while Bt tenebrionos is

only harmful to the Coleoptera insect host via Cry3A toxin. To date, the genetically modified

E. coli vector is engineered in a way to contain a wide range of Bt toxin and bring them together

in the same strains. This technique enables making a broad spectrum of Bt pesticide (Van

Frankenhuyzen, 1993). Moreover, the transfer of Cry3A endotoxin to Bt israelensis added to

this strain allows the synergetic ability to affect both Coleoptera and the initial host, Diptera

(Gebhard and Smalla, 1998). In addition to Bt corn and soybeans, cotton and others, Bt rice has

been improved for rice via Agrobacterium-engineered rice plants technique expressing CryIA (b)

and CryIA(c) synthetic genes (Cheng et al., 1998). These genes have been demonstrated to be

highly pathogenic to yellow and striped stem borer.

Furthermore, Ely (1993) has demonstrated that the mediated-transformation strain,

Agrobacterium, was able to become a vector system for Bt delta-endotoxins, in addition to its

ability to transfer the gene into callus plant tissue. During the last decade, Bt utility has been

improved to convey greater stability, delivery and versatility to cope with different hosts and to

control a wide range of insects (Carpenter et al., 2002). Biolistic injection of construct

containing Bt, as well as EPSP gene, is now the most used herbicide technique to protect plants

from insects (FAO, 2009). In addition to its herbicide tolerance ability, EPSPS gene is also used

as a selection marker in the plant. It thus confers both resistance and selection to the target plant.

In addition to insect- and herbicide-resistance traits that are used to increase plant yield

performance, there is an increasing interest in enhancing nutritional values, as well as adding

cold and drought tolerance variants to the crops. Perhaps the best known example is the drought

resistance gene Bet A from E coli (EcBetA) and Rhizobium meliloti (RmBetA) that has been

used thus far have been demonstrated to be beneficial to the plant without adverse effects

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(Kemphen and Jung, 2010). Seen from another perspective, future clean fuel producers and

biodegradion microorganisms (Phillips, 2008), as well as strains integrating both co-

fermentative and cellulolytic abilities for CBP (consolidating bioprocessing) processes for the

optimization of lignocellulosic based biofuel, are still in development (Lynd et al., 2005).

Microorganisms integrating both cellulolytic and fermentative, including primarily modified

Saccharomyces cerevisiae TMB 3400, along with P. chrysosporium,, K. marxianus and C.

cellulolyticum, are thoroughly possible for the second biofuel generation. The promising

microorganisms for genetical manipulation in biofuel system have been extensively reviewed by

Limayem and Ricke (2012).

Overall, genetic manipulations in plants and microorganisms hold promise for the

increasing population that requires considerable resources from the ecosystem. However, rising

concern for the potential risk from large-scale irreversible harmful mutations has urged

researchers to explore ways to counter possible outcomes from an extensive use of newly altered

genes. A scientific-based approach, such as MRA, offering greater vision of all inputs and

outputs was deemed essential to further protect the public health from unpredictable danger. The

emergence of agricultural technology has opened up the possibility for GMOs appearing in the

environment before ultimately reaching humans. MRA statistical technique could be either

quantitative or qualitative and would provide a pragmatic and systemic insight by describing

transparently all potential steps that include the risk (Reji and Van Schothorst, 2000). While

several MRA frameworks have been developed and improved , this qualitative exposure

assessment provides a systemic analysis based on Codex Alimentarius Commission (CAC,

1999) framework to fit international standardization and to respond to managers' requirements

for ensuring greater biosafety.

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5. Material and methods

5. 1.Theoretical overview of MRA method

5.1. 2 Hazard Identification

Once the food and water safety problem is formulated, the hazard identification step

follows to further identify the causative agents of the adverse health effects. Unlike the

traditional framework established by NRC (1983), CAC(1999) suggests that managers select the

hazard instead of the risk assessors. Hazard identification is a qualitative process aiming to

identify the pathogenic microbial hazard present in a specific food which can be detrimental to

human health. An effective collection and organization of data is crucial to the success of

subsequent assessments and risk estimation. Hazards can be categorized from several relevant

libraries, namely, USDA-FSIS Microbial Laboratory Guidebook or Bacteriological Analytical

Manual, along with governmental and international regulatory agencies and experts. These

sources provide epidemiological and in-depth clinical studies, including microbial agent data.

They also consider sensitive population information, in addition to endpoints of concern (CDC,

2007). Food processing operations and information credibility are also necessary to downstream

assessments. The classic Koch’s Postulates would also be used to prove that a particular

pathogen is the cause of adverse health effects.

Microbial agent data require greater attention paid to the hazard identification steps,

involving primarily the pathogenic hazard characteristics such as, microbial growth rate, heat or

cold resistance and ecology, along with intrinsic factors, such as pH, water activity (aw),

oxidation-reduction potential, and antimicrobials micronutrients associated with extrinsic factors

such as, gas, temperature, relative humidity. While The MRA approach can be easily adopted to

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assess microbial hazard consumed from food by human population, the GMOs hazard is a newly

emerging concept but is closely associated to bacterial hazard since altered genes are often linked

to bacterial plasmid. Therefore, GMOs that would cause allergy or toxicity reactions to human

population in addition to antibiotic resistance emergent disease would also be associated to

microbial hazards.

5.1.3 Exposure Assessment

Exposure assessment can either be qualitative by describing the threat or quantitative,

including probability distribution in each involved step (Jaykus, 1996). The major objective of

exposure assessment is to estimate the microbial hazard source associated with route, level and

frequency in food during consumption (Haas, 1999). Exposure analysis is a dynamic approach,

as it should consider microbial rapid growth or death associated to different conditions and

variations (Jaykus, 1996; ICMSF, 1998). The estimation of microbial hazard carrying altered

genes at the time of consumption requires the consideration of several prior steps from farm-to-

fork broad continuum (CAC, 1999). An efficient and pragmatic exposure assessment model

requires consideration of all steps that determine the risk, from the source of the raw materials to

consumption.

The exposure analysis process first states the unit and size of sample that is of concern.

Concurrently, it places emphasis on the route of contamination (ingestion, inhalation or skin

contact), level of contamination (concentrations or doses), along with the prevalence, including

all major steps over the time. However, exposure models vary from one type of hazard carrier to

another. For instance, the design of farm-to-consumption modules for products that are

consumed raw or fresh requires different exposure pathways cooked products. Crop pathways

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would include harvesting in addition to processing and storage temperature, along with handling

and packaging followed by retail and consumption. During these steps microbial contaminants

might undergo either growth or inactivation (i.e., die off) (Jaykus, 1996; ICMSF, 1998). The

correlation of all these factors, in addition to the host defense system, is estimated via the

probabilistic model, as it includes all available data. It considers population uncertainty and

population diversity involving probability distribution modeling from which a set of random

inputs are selected and evaluated several times via Mont-Carlo Simulations Method (MCM)

(Vose, 1996). MCM could be performed via several emergent softwares, namely, Crystal Ball®

and @ Risk (Oracle Crystal, Fusion Edition, 2011; Palisade Corporation)

5.1.4. Hazard Characterization -Dose-response Analysis

Hazard characterization describes, either qualitatively or quantitatively, the intensity and

severity of the adverse health effects caused by the exposure (ingestion, inhalation, and contact)

of the host to pathogenic microorganisms or toxins. Hazard characterization primarily involves

dose-response aspects (Haas, 1983). Dose-response evaluation assesses the concentration or

level of ingested pathogenic microorganisms or toxins that are susceptible to cause detectable

health adverse effects (probability of illness). Mathematical equations have been used to assess

the relation between the level of the ingested dose and the probability of infection associated to

the severity of illness. A threshold level of microbial exposure, called minimum infectious dose,

is determined as the lowest level at which a detectable disease response is detected. Exponential

and Beta-Poisson equations are the most suited to cope with MRA quantification. They include

the relationship between the ingested doses and the hazard specific parameters to determine the

probability of illness.

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The establishment of hazard characterization depends very closely on the interaction of

several aspects, primarily including the sensitive animal or human population ( i.e., immuno-

compromised person, pregnancy, elderly, children) (Gerba, 1996), and how it interacts with

intrinsic characteristic of microbial hazard (Coleman and Marks, 1998). The concept of adverse

health effects or a disease from GMOs practices is relatively young and is still under

development. However, altered gene carried by bacterial vector proved to be plausible through

resistant genes transported by microbial delivery system. The adoption of microbial risk

assessment approach to gene-mediated bacteria is deemed possible to assess adverse health

effects in human population.

5.1.5. Risk Characterization

The integration of the data collected and validated through hazard identification combined

with exposure and response dose assessments provides sufficient information to yield insights on

the probability of occurrence, including the likelihood and severity of the disease, along with the

uncertainties that could happen in one given population. Hence, this integration characterizes the

risk and provides a pragmatic science-based risk estimation. A qualitative and quantitative

estimate of the risk credibility and level of confidence is closely related to the level of

uncertainty, as well as variability and previous assumptions. Uncertainty depends on the model

designed and data provided, while variability is mostly related to the level of diversity of one

population (CAC, 1999). An efficient systematic characterization of the risk is crucial to the risk

manager and decision makers to further establish an appropriate large-scale microbial control

system.

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Although MRA is still in development, it is primarily used by the Codex Alimentarius

Commission (CAC) and several international organizations to ensure greater hazard control in

food worldwide and hence to manage public health risk (Lammerding and Fazil, 2000). While

the MRA tool has been used to evaluate risk from microbial contaminants in food industries, it

has never been to our knowledge used for biofuel applications distillery using transgenic that

generates large-scale animal feed and food products. To date, transgenic-crops based biorefinery

that foresees the use of modified microorganisms for the near future is emerging exponentially in

the U.S and worldwide. However, increasing public concern for the potential adverse health

effects that could be generated from large-scale altered genes have initiated researchers’ efforts

to establish a practical and scientific based response. It requires a comprehensive starting-point

modeling of the GMOs exposure that considers the dynamic gene flow from harvesting-to-

consumption through bacterial vector in an effort to reach a pragmatic risk estimation once data

become fully available.

6. Application of a qualitative MRA in a large -scale biofuel systems

6.1.1 Hazard identification

According to the extensive study on the environmental risk (ERA) that has been assessed

by The Council for Agricultural Science and Energy (Carpenter et al., 2002), GMOs

manipulation could impact considerably the environment by reducing biodiversity via cross-

pollination or destroying beneficial organisms in addition to other unforeseen outcomes.

However, this study places the emphasis on the adverse effects in human health from GMOs

through the MRA approach. Although there is no a clear evidence of adverse health effects from

GMOs practices, hazards from GMOs manipulations has been always associated with new

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allergens, toxins and antibiotic resistance in addition to unknown harm to health. Furthermore,

according to Weil (2005), crops that are inoculated with altered genes could develop tolerance to

abiotic hurdles and conditions. Bt corn could bring toxins or allergens, as well as antibiotic

resistance gene that are used as a “selectable marker” in the plant cells, and hence disseminate it

through several pathways into animal or human population. The case of Bt toxin traits, the

Cry9C protein (commercialized, Starlink) used in corn that was alleged for allergy reactions has

raised concerns among epidemiologists and regulators (USEPA, 2001). However, the study that

was established by the Centers for Diseases and Control (CDC) have shown the risk of allergy

reactions that could result from Cry9C for was very low. However, according to FAO (2010) the

limitation of current techniques to fully understand the genetic physiology, provide incomplete

data and evidence to set a quantitative risk analysis to a gene fragment in the ecosystem . Hence,

we can still not confirm adverse health effects to human population from genetic practices.

However, it is still possible to track the gene flow through a microbial vector (.i.e., altered-gene

microorganism -mediated). For instance, an altered genes resisting abiotic stressors would be

transferred to microorganisms and hence transform the bacteria into altered-genes vector.

Basically, in a biofuel system, the altered genes could be transmitted to potential contaminants,

such as Lactobacillus strains, potential contaminants in bioethanol fermentations. The main issue

would be the integration of the altered gene into the delivery platform or transfer vector (i.e.,

plasmid or virus ) before being conjugately transmitted to other bacteria (Cattoir et al., 2008).

For instance, several Lactobacillus strains have been detected in vegetables and animals that

possess antibiotic high spontaneous mutations (Salminen et al., 1998; Curragh and Collins;

1992)

190

In addition to the direct consequences from lactic acid bacteria (LAB) that acts as a

potential biorefinery contaminants inside the biofuel industry itself, there are indirect

consequences that would also arise from commensal and fecal bacteria (fecal indicator bacteria,

FIB). Typically, FIB primarily, Escherichia. coli and Enterococcus faecium live in the

gastrointestinal tract (GIT) of animals and humans and could acquire altered genes antibiotics

originating from the biofuel system. Typically, both E. coli and E. faecium have been shown to

acquire resistant gene through plasmid conjugation mechanisms from another bacterium or also

the acquisition of DNA fragment from the ecosystem. These FIB strains has been demonstrated

to acquire or disseminate other genes easily through conjugately transmitting or acquiring them

from other bacteria. Couvalin (1994) has demonstrated the ability of E. coli to easily acquire

DNA fragments from the environment in addition to their potential to transmit it to a wide range

of pathogens.

6.1.2. Exposure assessment through biofuel system models comparison

The hypothetical exposure model comparison of the biofuel system, including corn-wet

milling along with dry grind biofuel and biochemical cellulosic-derived bioethanol from harvest-

to-consumption, is outlined in Figure 6. It includes the dynamic gene flow and its related

prevalence and concentration through the different steps that determine the risk. It is quite

possible that genetic constructs containing altered genes would include an antibiotic selective

marker that would alter the gene itself but also help scientists track consequence from other

altered genes being in the same vector.

Accidental releases from steps that determine the risk, namely, hydrolysis with

approximately 50oC temperature or fermentation with approximately 30 oC in the different cases

191

of the biofuel system (i.e., wet-milling, dry-grind, and cellulosic ethanol) (Kaddar et al., 2004),

could present a potential reservoir for resistant or altered genes that come from transgenic

plants (i.e., corn) (Figure 6). While lignocellulosic-based biofuel is not commercially available

yet, there are extensive modified microorganisms that are foreseen for usage in the promising

CBP and SComF cost-effective processing. Furthermore, fungal-protein for animal feed

produced by the degradation of co-products (i.e., lignin) via white rot fungi (i.e., Basidomycetes)

would be a potential carrier of new resistant genes to the environment if it carries altered genes.

Importantly, mishandling, that is, including cross-contamination aggravated by improper

cooling without subsequent heating, could be a serious factor favoring bacterial regeneration

through mutation and resistant gene formation.

Furthermore, livestock feed co-products that are probably carrying altered genes could

be transferred to animal pathogens or commensal strains (FIB) before eventually reaching

humans through several pathways, including primarily the fecal-oral route. For instance,

agricultural runoff would carry manure and untreated sewage to surface and recreational

waters, including lakes and rivers. This particular dynamic of a mediated gene flow from distiller

grains to possible human consumption is illustrated in Figure 7.

Additionally, accidental releases of by-products or mixing components could create

another source of resistant gene dissemination. For instance, in 1994 there were approximately

700 feedlot cattle fatalities from toxicosis. These livestock had been DDGS from a dry-grind

based biofuel. The Kansas State Veterinary Diagnostic Laboratory associated this incident to an

accidental mixing of solid wastes to pharmaceutical components. Overall, most of these

mentioned factors would favor microbial restoration along with mutations associated with the

persistence of altered genes and their propagation into the environment. Although altered gene

192

FIB-mediated could be transmitted directly from animal consumption, the estimation of the

hazard via water consumption is more practical. The hypothetical uncertain variable models as

well as the forecast modeling attributed to the model described in Figure 7 were performed in

excel and are shown in Table 2. In addition, the lognormal distribution of the uncertain variables

of the volume of water ingested followed by a triangular distributions displaying the altered-gene

FIB-mediated concentration in the source and the inactivation are illustrated in Figure 8.

6.1.3. Qualitative exposure characterization- Conclusion

The qualitative evaluation of the exposure is essential for setting an efficient quantitative

risk analysis when more data are available. A probabilistic method would be the likely choice to

estimate the risk of contamination, including uncertain variable parameters. While this research

study is of descriptive and hypothetical nature , the outcomes could be performed by Monte-

Carlo simulations that randomly sample probability distribution inputs and involve an extensive

number of iterations based on the dose-response model with the ultimate goal of the realistic

overall risk estimate of the likelihood and the health adverse effects severity outputs. This also

includes a holistic characterization of the risk originating from the biofuel fermentation operation

and potential impacts on the environmental dissemination into neighboring ecosystems and

public health venues. The risk estimate would result from the integration of the different outputs

generated from hazard identification along with dose-response model and exposure assessment.

Possible dose-response outputs from FIB mediated genes would be performed via Monte-Carlo-

simulation through Crystal Ball software to elucidate the probability of infection. Furthermore, a

dose-response sensitivity chart could be performed by the Crystal Ball tool to estimate the

contribution and the impact of the uncertain variables on the establishment of the model.

193

Extensive development of agricultural biotechnology in 1980 has given rise to the ensuing

genetic revolution. The biofuel industry comprises emergent and rising biotechnology opting for

GMOs practices to increase the yields and economic benefits via either transplants or engineered

microorganisms. Corn-derived biofuel has exhibited a substantial growth subsequent to

government incentives and extensive cultivation of transgenic plants. Lignocellulosic-based

ethanol holds promise for the next decade through novel GM microorganisms related to CBP

processing. Hence, GMOs development has increased public concern towards possible harmful

large-scale dissemination from artificial gene manipulations. Although to date there is no a clear

evidence or direct proof of GMOs side effects, preventive measures need to be taken to mitigate

the risk from GMOs practices and subside public apprehensions. A clear vision of the dynamic of

GMOs flow through bacterial mediator in biofuel process would allow implementation of

feasible and preventive interventions; thus, greater control could be performed to meet with the

main regulatory agencies requirements in improving public health and ensuring the entire

environmental safety. The QMRA approach is a novel scientific -based tool and mathematical

model that could provide pragmatic insights to managers and decision-makers to take greater

protective standards. It is quite possible that similar exposure assessment modeling could be

generated for the biofuel industry when GMOs are introduced on a more large scale basis. This

preliminary insight would enable the industry to be prepared and proactive in response to public

forum issues, political debates and other concerns.

194

Acknowledgements

This study was partially supported by grants from the South Central Sun Grant (U.S.

Department of Transportation) program, Novozyme North America, Inc., Franklinton, NC, and

the Institute of Food Science and Engineering, University of Arkansas, Fayetteville, AR.

195

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Figure 1. General QMRA Lay-out based on the classical available literature (NRC, 1983, ILSI-

RSI, 1996).

     Hazard Identification 

   Exposure Assessment   Hazard Characterization: 

Dose‐response Assessment 

        Risk Characterization 

                

     Risk Estimation 

                

Problem Formulation

209

Corn Kernels (Wet-milling Process)

Figure 2. Bioethanol and Major Co-products Generation from Corn-based wet-milling Process

   Steeping (Degermination) 

     Milling  

   Cyclone Separation 

Starch/ Gluten Separation 

Centrifugation 

                 Starch 

Liquification/Saccharification 

        Fermentation 

Distillation/Dehydration 

             Cleaning 

 Grinding  

     Fibers 

     Gluten   Corn 

Gluten  

 Washing/Filtering    

     Enzymes

 

   

Bioethanol 

       Yeast   

     Germs 

Corn Oil 

  Evaporation 

Corn 

Gluten 

Feed

HCL/Cooking  Dextrins 

210

Corn Kernels (Dry- Grind Process)

Figure 3. Bioethanol and major Co-products Recovery from Corn-Based Dry-grind Process

                      Grinding 

      

               Cooking 

Liquification/Saccharification 

              Fermentation 

Distillation/Dehydration 

Cleaning 

     Enzymes

 

    

Bioethanol 

  

      Yeast   

        Centrifugation          Evaporation 

  Distillers 

Solubles  Distillers Dry Grains 

  Distillers Dry 

Grains with  

Solubles (DDGS) 

              Drying 

211

Lignocellulosic Biomass (Biochemical Pathway)

Enzymes

Yeast

Figure 4. Bioethanol and Major Co-products Generation from Biochemical Lignocellulosic Process.

Feedstocks Preparation 

Pretreatment: 

 

‐  Mechanical 

and/or 

‐ Thermo‐chemical 

Hydrolysis/Saccharificationn 

Fermentation

Distillation/Dehydration 

Co‐Products (i.e., lignin) 

Bioethanol

  

Separation and Drying 

Fungal Proteins 

             White‐rot fungi 

212

Figure 5. Generalised transgenic plants generation method involving A. tumefaciens- based delivery

system; (1) sources for genes; (2) Delivery system (i.e., E. coli plasmid); (3) mediated-transformation, A.

tumefaciens.

Desired “traits”: (i.e., EPSPS gene isolated from A. tumefaciens; Cry 1Ab gene from Bacillus thuringiensis)

Amp: ampicillin marker gene for selecting the cloning vector bacteria (i.e., E. coli)

Ori T:transfer origin for conjugal transfer of the plasmid to recipient cell

oriV: origin of replication

Promoter : Promoter gene (i.e., gene derived from cauliflower mosaic virus (CamV 35S) used for soybeans vector

(Nida,1996),

Sel. Marker: selection marker (i.e., kanamycin resistancer or bla for beta-lactamase, nptII (/neomycin/ (kanamycin

phosphotransferase) for selecting transformation in plants

TT: termination of transcription (i.e., nopaline synthase (nos) from Arabidopsis plant)

    Amp

Oriv OriT 

Promoter 

 

Desired   Sel. Marker TT 

 Transgenic plants 

 

 

Agrobacterium 

Gene sources (i.e., soil, 

microorganisms, plants 

providing trait, promoter 

and terminator genes) 

 

                 (1) 

 

                 (2) 

 

                 (3) 

213

Table. 1. Most prevalent transgenic plants used for biofuel production and their related major

GMOs techniques

Plants

Desired “traits”

Desired “traits”

sources

Constructs major

components and

sources

References

Corn

- Pest resistance

(i.e., Bt corn)

- Resistance to

herbicides ( i.e.,

glycophosate,

glycofosinate)

-Cry genes from

B.

thuringiensis(i.e.,

Cry 1A.105, Cry

2Ab2, Cry 1F for

aerial pests and

Cry 3Bb1, Cry

34Ab1 and Cry

35 Ab1 for

subsoil pest

resistance)

For insect

resistance

- EPSPS genes

isolated from A.

tumefaciens CP4

(resistance to

- Promoter from rice

- Terminator from A.

tumefaciens

- Antibiotic resistance

marker (ARM), beta-

lactamase (bla)

- CTP peptide (EPSPS

transporter) from corn

itself and sunflower

(Carpentar

et al., 2002)

(Phillips,

2008; Nida

1996)

(FAO,

2009)

(Kempken

and Jung;

2010)

(Ye et al.,

2010)

214

glucophosphate)

Soybeans

Resistance to

herbicides

(i.e., glycophosate,

glycofosinate)

- EPSPS genes

isolated from A.

tumefaciens CP4

for herbicide

resistance

- Promoter from

cauliflower mosaic

virus, CamV 35S

- Terminator from

Arabidopsis plant

- ARM, neomycin

phosphotranspherase II

- CTP (EPSPS

transporter peptide from

petunia plant

Sugar

beet

- Promoter from

cauliflower mosaic

virus, CamV 35S

3'nos terminator A.

grobacterium

tumefaciens

Marker genes NPTII

(/neomycin/ (kanamycin

phosphotransferase from

microbial transposon, Tn

5

215

terminator from bar

from Streptomyces

hygroscopicus

along with 3'ocs and

3'g7 controlled by

bidirectional TR1/2

promoter from A.

tumefaciens

Canola

- Herbicide

resistance

- Increased content

of laurate

- EPSPS from A.

tumefaciens CP4

- GOX from

Ochrobactrum

anthorpi strain

LBAA

- ACP thiosterase

genes from

California tree

- Promoter from figwort

mosaic virus

- Terminator from pea

- ARM, streptomyin

Sugar

cane

- Resistance to

some insecticides

- Cry genes from

B. thuringiensis

- Gene Bet A

- Promoter from

cauliflower mosaic

virus, CamV 35S

216

- Increased sugar

content in the plant

- Drought tolerant

from Escherichia

coli (EcBetA)or

Rhizobium

meliloti

(RmBetaA)

- Terminator, nopaline

synthase (nos) from A.

tumafaciens

- Selective markers,

kanamycin (Kan) or

hygromycin (Hyg)

Rice

- Resistance to

insects (i.e., yellow

and stem borers)

- “Golden rice”

Vitamin A-added

(beta-carotene)

- Cry IA (b) and

CryIA (c) from

B. thuringiens.

- Phytoene

synthase gene

(psy) from

daffodil

(Narcissus

pseudonarcissus)

- Phytoene

desaturase (crtl)

from Erwinia

uredovora.

- Promoter from

cauliflower mosaic

virus, CamV 35S

- Nopaline synthase

promoter (Pnos)

- NT, 3’ terminator

ARM, neomycin

phosphotranspherase II

(NPTII) , hygromycin

(Hyg)

217

Figure. 6. Simplified Hypothetical Model Comparaison from Harvest-to-consumption in Biofuel System; (1) Corn-

wet-mill process; (2) Corn dry-grind process; (3) Biochemical cellulosic-process.

 

G

M

O

  

 

 

P

r

e

v

a

l

e

n

c

e

s 

Harvesting:

GM Crops 

Processing: 

‐ Steeping 

‐ Separation 

Co‐products: 

(i.e. Corn gluten 

feed/meal, corn 

Animals or 

humans 

Human 

consumption 

G

M

O

   

C

o

n

c

e

n

r

t

a

t

i

o

n

s

 

 

G

M

O

  

P

r

e

v

a

l

e

n

c

e

s 

 

G

M

O

  

  

P

r

e

v

a

l

e

n

c

e

s

Harvesting:

GM crops 

Processing:

‐ Grinding 

‐ Cooking 

‐ Liquification 

‐ Saccharification 

      DDGS

    Animals

 

G

M

O 

C

o

n

c

e

n

t

r

a

t

i

o

n

s 

Human 

consumption 

Cellulosic feedstocks

Processing:

‐ Pretreatment 

‐ Hydrolysis

Separation/ Drying

Inedible Co‐products

Fungal protein

 

G

M

O

 

C

o

n

c

e

n

t

r

a

t

i

o

n

s 

Animals or human 

consumption 

        

(1) 

        

(2) 

        (3) 

ACR

ACR to the 

environment 

Faecal route

i.e., manure, 

sewage) runoff 

to surface  

water 

GMOs use for 

CBP or 

SSCombF 

ACR

AU

AU 

AU

218

Altered genes

Or antibiotic residues (AR)

Fecal route

Runoff (i.e., agricultural runoff)

Direct animal consumption

Figure 7. Hypothetical model of exposure to GMOs and AR from distiller grains originating from biofuel

production ; cycle formation.

DDGS  including altered 

genes or antibiotic  

residues  

Animal intestinal tracts 

including commensal  

Faecal material carriers 

(.e., sewage, manure, 

irrigation waters)

 Ecosystem: 

‐ Water sources (i.e, 

surface waters, ground 

waters) 

‐ Wild animals 

Human’s consumption 

(i.e, antibiotic resistance, 

allergens, toxic prteins) 

219

 

 

Table. 2. Excel presentation adopted to the uncertain variable and forecast modelings performed  via Crystal Ball® 

software; beta‐Poisson best fit;  FIB parameters alpha and N50 (i.e., E. coli O111 and O55 adopted from Haas et al. 

(1999)) 

 

 

 

 

 

220

 

 

 

Figure 8. Hypotehtical distributions of the uncertain variable models performed by Crystal Ball ®; 

(1)Ingested volume of water; (2) Altered‐gene FIB‐mediated; (3) Inactivati 

(1) 

(2) 

   (3) 

221

Overall Conclusions

This research project explored biological strategies and mathematical approaches to limit

bacterial contaminants along with antibiotic resistance concerns from current corn-derived fuel

in conjunction with chemical pollutants interfering with the yield performance of cellulosic-

derived biofuel. The threat from exposure to genetically modified organisms involved in biofuel

systems was also qualitatively assessed. The first step of this study was to determine an

alternative biological antimicrobial over antibiotics and to examine the effect of natural

antimicrobials (i.e., nisin, chitosan ε-polylysine, and lysozyme) with eventually EDTA on

Lactobacillus species that are broadly efficient to inhibit potential contaminants in a large-scale

biofuel system. Nisin was demonstrated to be the best candidate against Lactobacillus strains,

except with the strain L. casei. It was concluded that the synergistic effect of nisin, with EDTA,

successfully inhibited the nisin-induced L. casei. Thus, this combination was effective against all

LAB species. In conjunction with microbial contaminants in bioethanol production, chemical

pollutants, namely, furfural and hydroxymethylfurfural (HMF) were demonstrated to be of

primary concern from prior research. It was concluded from the literature search that the

combination of the energy efficient SPORL and steam explosion by catalysts pretreatment would

cope with feedstock versatility and commodity in an effort to achieve optimal next generation

bioethanol performance. This included the integration of cost-effective SSCombF and CBP

processes that require engineered microorganisms with both cellulolytic and thermophilic

abilities. Hence, the two last steps comprised of developing a mathematical modeling, to

potentially foresee possible adverse public health effects from possible FIB antibiotic resistance

resulting from farming animal fed with DDGS carrying antibiotics or altered gene fragments. A

dynamic flow tree model of the exposure route is illustrated in both corn- and lignocellulosic

222

derived biofuel. A hypothetical quantitative evaluation of enterovirulent E. coli carrying

resistance with the exception of EHEC was performed via Crystal ball® software

through Monte-Carlo simulation. This simulation demonstrated that for typical E. coli dynamic

flow in agro-industrial system there is a risk of 50% to a human population exposed to a

recreational water to be infected. The most variability in the forecast model, namely, volume of

water consumed, was determined through a sensitivity chart. It was concluded that the E. coli

concentration contributed the most to the dose-response model over the volume of water

ingested. From this viewpoint, antibiotic resistance would present a potential danger if stringent

precautions are not undertaken from the source-to-consumption. The last study in this research

qualitatively assessed the exposure to GMOs originating from biofuel, and thoroughly

investigated essentials to the establishment of dose-response modeling when more pragmatic

empirical data would become available. It is quite plausible that selective marker genes carrying

antibiotic resistance would help researchers track other altered genes in the same delivery

system. Hence, a comprehensive approach would be an important tool in an attempt to subside

public concern before it becomes a reality.

223

APPENDIX I: Glossary

Biological antimicrobial

It is a natural component derived primarily from plant extracts or peptides (i.e., nisin, chitosan, hope) that has either bactericidal or bacteriostatic effects ( similar to antibiotics ) but without the adverse side effects.

Biotechnology

The techniques that are used to transform biological system via bioliological components to produce different bio-materials or improve yield performance (i.e., transgenic plants, insulin- producing microorganisms).

Bt toxins

It is a protein and crystalline component (i.e., gene encoding Bt toxin, Cry) that is toxic to insects including primarily diptera, lepidoptera and coleoptera. The Cry gene proved not harmful for human has been transferred to plants to protect them from pests.

Cellulolytic microorganisms

Microorganisms that contains enzymes that are able to hydrolyse cellulose in lignocellulosic materials.

Crystal ball® tool

It is software used to perform Monte-Carlo simulation for risk analysis.

Dose-response assessment

Dose-response evaluation or risk characterization n assesses the concentration or level of ingested pathogenic microorganisms or toxins that are susceptible to cause detectable health adverse effects (probability of illness). Mathematical equations have been used to assess the relation between the level of the ingested dose and the probability of infection associated to the severity of illness

224

1 1

P(response ) or P (infection) is the probability of illness or infection ; d is the average dose administrated to one human population; α is the slope parameter of the equation adopted from Haas (1983).

Exposure Assessment

Exposure assessment can either be qualitative by describing threat or quantitative, including probability distribution in each involved step. The major objective of exposure assessment is to estimate the microbial hazard source associated with route, level and frequency in food during consumption. Exposure analysis is a dynamic approach, as it should consider microbial rapid growth or death associated to different conditions and variations.

Hazard identification                 

Hazard identification is a qualitative process aiming to identify the pathogenic microbial hazard present in a specific food which can be detrimental to human health. An effective collection and organization of data is crucial to the success of subsequent assessments and risk estimation. Hazards can be categorized from several relevant libraries, namely, USDA-FSIS Microbial Laboratory Guidebook or Bacteriological Analytical Manual, along with governmental and international regulatory agencies and experts. These sources provide epidemiological and in-depth clinical studies, including microbial agent data.

Lignocellulosic feedstock

It is the most prevalent renewable source worldwide. It primarily includes agricultural and woody- forest residues and energy crops, including primarily Miscanthus and switchgrass, along with industrial and municipal solid wastes.

Mediated-transformation Agrobacterium

Agrobacterium mediates the desired gene to the plants and cause transformation in the host cells. A. tumefaciens strain causative of crown gall disease upon its insertion of its Ti plasmid has been used for its ability to infect plants and transfer genes into callus embryonic plant tissue, callus. The gene is transferred from the delivery system into Ti plasmid through DNA

225

recombination mechanism. This mechanism is enabled by the cleavage of the target gene at specific sites, and ensured by endonuclease restriction enzyme . The same enzyme is also used to cleave the host cell DNA before ligation via DNA-joining enzyme, ligase. Therefore, the transformed Ti plasmid (deactivated from tumor-inducing mechanism) is subsequently injected into the tissue culture or plant embryos (callus), prior to a successful regeneration. A successful transfer of the desired gene to the tissue culture chromosome is detected via the selective marker incorporated in the plasmid vector.

MIC- Lactobacillus growth measurement method used

Minimal inhibitory concentrations (MICs) for Lactobacillus strains and S. cerevisiae were determined for the natural antimicrobials, nisin, ε-polylysine, chitosan and lysozyme (Sigma, MO, USA), at initial concentrations of 0.4 mg/mL, 10 mg/mL, 2.5 mg/mL and 100 mg/mL respectively. A standard broth-microdilution method utilizing 96-well microliter plates was used to estimate the MIC values. Each well was filled with 50 µl of MRS broth and 50 µl of the respective antimicrobial. Successive serial 2-fold dilutions were made, then each well was inoculated with 50 µl of Lactobacillus (approximately 6 log CFU/ mL). The microbial growth was evaluated after 24h and 48h for Lactobacillus by visually determining the last detectable turbid well of each antimicrobial which indicated cellular growth at the MIC.

Microbial risk assessment   

It is essentially a scientific-based approach that is systematically and transparently analyzed to assess the probability of occurrence and severity of the disease based on four major. Several frameworks have been provided to fit continuous management requirements and support global development. Overall, the major objective of the risk assessment (RA) process is to provide a qualitative and quantitative estimate through its major cornerstones, namely, hazard identification, hazard characterization, along with response-dose assessment, as well as exposure assessment and risk characterization.

Monte-Carlo Method via Crystal Ball ® software

The QMRA computational technique required essentially the establishment of a deterministic, probabilistic model (without point estimate). Once this model was established, the simulation was performed by Monte Carlo Method (MCM) using a software tool, namely, Crystal Ball ® add-in to EXCELTM (Oracle Crystal Ball, Fusion Edition) in an effort to analyze and quantify parameter uncertainties that entered the model. The construction of the deterministic model involved one or more parameters called uncertain variables in MCM framework. Crystal Ball ® tool was used for its ability to select randomly from a set of inputs from several probability

226

distributions (CAMRA, 2011). This allows for obtaining output-values, as well as for evaluating multiple deterministic models and elucidate the uncertain variables through an extensive number of iterations (i.e., 103 trials). The uncertain variables that were selected for this model are the volume of water ingested from the source (recreational water), the resistant E. coli concentration in the water source and the chemical inactivation (i.e., chlorination).

Probabilistic model

The probabilistic model is based on Bayesian method, considers all data available by involving probability distribution techniques. Probabilistic t approach includes the evaluation of variability (e.g., diversity in one population) and uncertainty (e.g., lack of information or knowledge about one parameter).

Nanoparticle design and preparation (i.e., Chitosan-pentasodium-triphosphate (CS-TPP)

The polysaccharide biopolymer chitosan was also tested as an antimicrobial not only because of its biological and physicochemical properties but also because of its potential to serve as an inexpensive nanoparticle platform. Chitosan was attached to antimicrobials based on ionic gelation to form nanoparticles with chitosan-pentasodium tripolyphosphate (CS-TPP) anion. Chitosan (CS) and pen- tasodium tripolyphosphate (TPP) were purchased without any purification from Sigma-Aldrich (St. Louis, MO) with a degree of deacetylation and viscosity of 92% and 32 cps, respectively. The stock solution of CS was dissolved in 1% (v/v) of acetic acid at 2 mg/mL. The CS solution was then adjusted to a pH of 4.8 using 10 N NaOH. The TPP solution (pH 9.0) was prepared by dissolving TPP in distilled water at a concentration of 1.65 mg/mL. The synthesis of CS-TPP nanoparticles was made according to Cuna et al. (2006) with minor modifications. CS solution (9 mL) was added to 3.6 mL of TPP solution and further stirred, the gelation of CS-TPP occurred spontaneously, which resulted in a 3:1 ratio of CS to TPP. The resultant CS-TPP nanoparticles were washed 3 times with 10 mL double dis- tilled

water after separating them by centrifugation at 8,000 g for 30 min at 4◦C. At the end of the third centrifugal separation, the nanoparticles were re-suspended with double distilled water at the final volume of 2 mL.

Risk characterization

The integration of the data collected and validated through hazard identification combined with exposure and response dose assessments provides sufficient information to yield insights on the probability of occurrence, including the likelihood and severity of the disease, along with the uncertainties that could happen in one given population.

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Yeast-based fermentation

Typically, during batch fermentation S.cerevisiae ferments hexose sugars, mainly glucose, into ethanol in a large tank via the Embden-Meyerhof pathway under anaerobic conditions and controlled temperature. Yeast-based fermentation is always accompanied by formation of CO2 by-products and supplemented by nitrogen to enhance the reaction. This conventional strain is optimal at a temperature of approximately 30oC and resists a high osmotic pressure in addition to its tolerance to low pH levels of 4.0 as well as inhibitory products. S. cerevisiae can generate a high yield of ethanol (12.0 to 17.0% w/v; 90% of the theoretical) from hexose sugars.

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APPENDIX II: Acronyms, Abbreviations and symbols

AFEX Ammonia Fiber Explosion

APHIS USDA Animal and Plant Health Inspection Sevice

BRA Biotechnology Risk Assessment

Bt Bacillus thuringiensis

CaMV Cauliflower mosaic virus

CAC Codex Alimentarius Commission

CBP Consolidated Bioprocessing

Cry Crystalline

DDCE DuPont Danisco Cellulosic Ethanol

EDTA Ethylenediaminetetraacetic

EISA Energy and Independence Security Act

EPA or USEPA U.S. Environmental Protection Agency

EPSPS 5-enolpyruvylshikimate-3-phosphate synthetase

FAO Food and Agriculture Organization of the United Nations

FDA U.S. Food and Drug Administration

FIB Fecal Indicator Bacteria

LAB Lactic Acid Bacteria

MIC Minimal Inhibitory Concentration

MRA Microbial Risk Assessment

NOS Nopaline synthase

NPTII Neomycyin phosphotransferase II

OECD Organization for Economic Cooperation and development

PEA Policy Energy Act

QMRA Quantitative Microbial Risk Assessment

rDNA recombinant DNA

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SHF Separate Hydrolysis and Fermentation

SPORL Sulfite Pretreatment to Overcome Recalcitrance

SERF Streptogramin Resistant Enterococcus faecium

SSCombF Simultaneous Saccharification and Combined Fermentatiomn

TSCA  Toxic Substances Control Act

USDA U.S. Department of Agriculture

WHO World Health Organization of the United Nations

WTO World Trade Organization