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Page 1: Nondestructive Testing of Food Quality || Electronic Nose Applications in the Food Industry

Chapter 10

Electronic Nose Applications in the

Food Industry

Parameswarakumar Mallikarjunan

Introduction

In the food industry, characterizing a food product in terms of aromaor smell is a critical factor for the success of the food in the market-place. Before putting the food in the mouth, consumers will smell theproduct and any objectionable odor will prevent them from consumingthe product. To characterize this attribute by smell, many descriptiveterms have been used: smell, aroma, odor, and flavor. Of these, flavorrefers to the volatiles released during smelling and during mastication.The smell is the resultant reaction between volatile chemicals and thenose. The volatiles are often released from biological materials due tophysical and chemical reactions occurring in the material. Primarily,these volatile compounds are organic in nature and are comprised ofaldehydes, ketones, and esters. Other volatile compounds that are notorganic chemicals include sulfur, ammonia, hydrogen sulphide, etc.

During consumption or selection of a product, the human nose rec-ognizes the smell of a food product. Volatile compounds from the foodenter the olfactory area from the nose and the mouth. When the humannose sniffs, currents of air swirl up over the turbinate bones and to asheet called the olfactory epithelium. The odor from the food is alsobrought into the olfactory epithelium through the mouth. The olfactoryepithelium is very small, approximately one square centimeter per nos-tril, and yet contains an estimated 50 million primary sensory cells inhumans. Each of the sensory cells has miniscule filaments that extend

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from the surface of the epithelium into the watery mucus. Each filamentcontains a protein that is the molecular receptor (cilia) that interactswith the incoming odorant molecules. An odor molecule binds to thesecilia to trigger the neuron and causes you to perceive a smell. Humanscan distinguish more than 10,000 different smells. It has always been achallenge to correlate the human experience with analytical methods.

Conventional analytical processes mostly use gas chromatography(GC), gas chromatography mass spectrometry (GC-MS), and gas chro-matography olfactory (GCO) methods, and involve several time- andlabor-intensive steps including developing the methods, sampling, trans-porting the sample to the lab, preparing the sample for analysis, separat-ing specific volatile compounds using an appropriate chromatographiccolumn, interpreting the chromatogram, taking the decision back to thesampling site, and acting on the decision. Complex samples have to beseparated into their individual chemical components before a decisioncan be made. Even with the use of MS methods, the correlation of sen-sory smell with instrumental data has not been very successful due tothe interactions of several volatiles in forming the sensory experience.Aroma of a particular sample is a complex mixture of compounds, anda large number of statistical calculations or multiple sniff ports couldnot yield the exact smell print of the sample.

Electronic noses are gas multisensor arrays that are able to measurearoma compounds in a manner that is closer to sensory analysis thanGC. Generally, an electronic nose system is composed of several sensorsthat may be set to achieve various levels of sensitivity and selectivity.The adsorption of volatiles on the sensor surface causes a physical orchemical change of the sensor and a specific reading to be obtained forthat sample. Electronic nose systems consist of an array of chemicalsensors that respond to the volatile flavors from a sample (Bartlett et al.1997) in a unique pattern (Haugen and Kvaal 1998). Though an elec-tronic nose is not a substitute for human sensory panels, which are mostreliable and sensitive in measuring aroma, it can be used as a rapid,automated, and objective alternative to detect, measure, and monitoraroma (Mielle 1996).

In recent years, there have been major advances in sensor technologiesfor odor analysis. Electronic noses have been around for approximately35 years, and the last 15 years have seen dynamic advancements in bothsensor technology and information processing systems. Chemosensorarray-based systems using the conducting polymer technology were

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initially developed in the early 1980s (Payne 1998). Initial work on theelectronic nose with this technology stemmed from polymer develop-ment technology achieved by the U.S. Air Force. The Air Force wasattempting to use certain polymers found to conduct electricity to buildan airplane that would better evade radar. Researchers at Britain’s War-wick University in the mid-1980s used the findings from the militaryresearch to develop the first electronic nose chemosensory system basedon conducting polymer sensors (Pope 1995). Since that time, other newsensor technologies have been developed that have properties more suit-able for particular applications.

Metal oxide semiconductor gas sensors were first used in the 1960sin Japan in home gas alarms. Conducting ceramic or oxide sensors wereinvented by Taguchi and produced by the company Figaro (Schaller andBosset 1998). Electronic nose instruments have been tested successfullyfor use as a complementary tool in the discrimination of many consumerproducts.

Currently commercial electronic nose systems have been developedbased on three major technologies: metal oxide semiconductors, semi-conducting polymers, and quartz crystal microbalance sensors. A selectnumber of companies also produce systems using metal oxide semi-conductor field effect transistor technology. In addition, technologiesusing surface acoustic wave sensors (for example, zNose) provide addi-tional opportunities to convert traditional GC methods to the one similarto electronic nose systems. Determining which of the aforementionedtypes of electronic nose systems, if any, would be most suitable as adiscriminatory analysis tool to be used in quality control is of interest inthe food industry. The other major issue with the electronic nose tech-nology is it is perceived as a market pushed and not as demand driven.Many companies have sprung up but failed to sustain a market pres-ence due to the push versus pull of the technology. The electronic nosemanufacturers are trying to find applications to employ their system andtrying to convince the user base, especially the food industry to adoptthe systems. As a result, one can see rapid changes in the marketplace.A few examples follow:

� Cyrano Sciences merged with Smith Detection Systems.� AromaScan changed to Osmetech.� Nordic Sensors merged into Applied Sensors (and moved to otherareas).

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� Perkin Elmer dropped the support for their system with HKR GMBH,letting that company go out of business.� Agilent 4440A from Hewlett Packard is no longer available.

Electronic noses are comprised of (1) chemical sensors that are usedto measure aromas, (2) electronic system controls, and (3) informationprocessing systems and a pattern-recognition system that is capable ofrecognizing complex aromas. Although there are various sensor tech-nologies used among the current manufactured instruments, most sys-tems work using the same series of steps. They analyze compounds ina complex aroma and produce a simple output.

Usually, the steps in using an electronic nose system include (1) gener-ating an odor from a sample, (2) exposing the sensor array to the aroma,(3) measuring changes in an array of sensors when they are exposed tothe odor, (4) establishing a recognition pattern for the sample from theresponses of all or a number of sensors in the system, and (5) using thisinformation in statistical analyses or pattern recognition neural networksto compare to a database of other chemosensory measurements.

Each of the sensors in the electronic nose are made with a uniquematerial and, when exposed to a particular vapor mixture, each sensorreacts in a different but reproducible manner producing a “smellprint”(combination of responses from all sensors) for each volatile mixture. Adatabase of smellprints or the digital images of a chemical vapor mixtureis created by training the electronic nose system. Then using a predictionalgorithm, such as a multivariate technique (principal component anal-ysis, canonical discriminant analysis, etc.), a model can be developed.When a new unknown vapor mixture is to be identified, the electronicnose system digitizes the vapor mixture and compares this digital imagewith the previously established database (model) of smellprints in itsmemory. The unique feature of an electronic nose system is that itsresponse takes into account all of the characteristic features (chemicaland physical properties) of a sample, but does not provide informationabout the composition of the complex mixture. Thus this system canbe used when the decision about a chemical vapor of a sample is moreimportant than its contents, such as a spoiled versus nonspoiled foodsample, age of a fruit, type of cheese, and adulteration in the product,etc.

An aroma may be taken at ambient conditions to mimic what thehuman nose would experience under normal circumstances or the

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sample may be heated to intensify aroma concentrations. Aroma expo-sure to the sensor array is generally accomplished by one of twomethods: static headspace analysis and flow injection analysis. Staticheadspace analysis involves direct exposure to a saturated vapor takenfrom the headspace above a sample. Flow injection analysis involvesinjecting the aroma sample into a control gas that is constantly pumpedthrough the sensor chamber (Payne 1998).

Electronic Nose Niche

The electronic nose has both advantages and disadvantages over the useof human sensory panels as well as GC/MS analyses. Therefore, it isused as a complementary instrument to monitor odors.

The human olfaction system, which is the basis of sensory panels,is still the most sensitive device available for aroma measurement. It isalso the odor measurement method used by consumers when assessingthe odor of consumable products. Therefore, it is important that anyodor monitoring methods used in quality control or quality assurancebe capable of detecting odors that may be found to be offensive by thehuman olfactory system. This fact is also the reason that human sensorypanels are still the basis of aroma measurements in the food industry.

Although electronic noses cannot compete with the sensitivity andfinal correlation of sensory panels and replace them, they are objectiveinstruments and involve primarily a small capital investment comparedto having a standing sensory panel. They can also be used on the pro-duction floor and even can be implemented for in-line measurements.Work performed by Strassburger (1998) demonstrated that a metal oxidesemiconductor (MOS) sensor-based instrument showed great potentialin aiding in flavor analysis going from the research and developmentphase to the production floor as it produced results that were directlycorrelated to sensory and analytical results.

Sensory panels are inherently subjective and the physical conditionof panelists may vary from one day to another. This brings inherenterror into any scientific quantification of experimental results. Humanpanels require sustained training for each type of product or sample andstandardization between different panels at different sites is extremelydifficult. Sensory panels have high costs associated with training, main-taining, and testing, and they experience fatigue. Therefore, they are not

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run continuously for extended periods of time. A trained electronic noseprovides a complementary objective tool available for 24-hour complexaroma analysis (Payne 1998). Newman and others (1999) used a con-ducting polymer sensor-based electronic nose as a complementary toolto sensory analysis in the odor analysis of raw tuna quality. Electronicnose measurements were successfully correlated with sensory scoreswith correct classification rates of 88%, 82%, and 90% for raw tunastored at three temperatures.

The electronic nose is an instrument that can be either portable orconnected to an auto-sampler to reduce the need for human involvementin multiple sample testing. It is also an instrument available to potentiallytest odors that a human sensory panel would not be willing to test,although this facet is not particularly pertinent to the food industry.

Past objective odor monitoring analysis options involved the use ofanalytical GC/MS techniques. These techniques offer identification andquantification of compounds comprising an aroma. However, GC/MStechniques often find difficulties in identifying which of the compris-ing compounds contribute to the recognized odor and to what extent,particularly if they are complex odors. Electronic nose technology hasa unique advantage over GC and MS techniques because it is an ana-lytical technique that samples an entire aroma rather than identifyingit by its comprising components. It is also a faster method of aromaanalysis (Payne 1998). A portable electronic nose unit could also beused to directly sample headspace aromas from bulk raw materials orfood containers where sampling for GC/MS analysis becomes difficult(Hodgins and Conover 1995).

Electronic nose analysis is also a technique that may be nondestructiveand incurs low operational costs. Overall, it fills a number of gaps in odoranalysis not achieved by use of sensory panels and GC/MS techniquesin conjunction. Although the electronic nose has a number of weakpoints that inhibit its ability to be used exclusively, it is a powerful toolthat enhances aroma monitoring when used as a complementary tool tosensory panels and GC/MS techniques.

Electronic Nose Market

The market for electronic nose instrumentation has been developedas a result more of ‘technology push’ rather than ‘market pull’ (Payne

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1998). The technology has been continually developed without an exist-ing market demand and manufacturers have actively pushed sales andpursued applications in which electronic nose instruments would beuseful. There are numerous and expanding applications in which suchan instrument would be complementary and enhance a product or pro-cess. As a result of this market situation, manufacturers are able to offerstrong product support and aid in the implementation of their instrument.However, until the recent development of portable electronic noses, longresearch development and associated costs have resulted in high pricingof most electronic nose systems. This has in turn retarded their expan-sion into new applications. End users are hesitant to purchase theseinstruments without being fully assured that it will work as the manu-facturers claim. The production of an industrial electronic nose that isreliable is still in the development phase, and most systems currentlyavailable are most suited to a laboratory environment (Payne 1998).

Current commercial electronic nose system manufacturers that aremost involved in the market include Airsense (Germany), AlphaMOS (France), Applied Sensor (US) merged from Nordic SensorTechnologies (Sweden) and Motech GmbH (Germany), AromaScan(UK), Bloodhound Sensors (UK), HKR Sensorsystems (Germany),Lennartz electronic (Germany), Neotronics (USA, UK), RST Rostock(Germany), and OligoSense (Belgium) (Payne 1998).

Types of Chemosensory Systems

The major types of chemosensory-based electronic nose technol-ogy include MOS sensors, conducting polymer (CP) sensors, quartzmicrobalance (QMB) sensors, and metal oxide field effect transistors(MOSFET). Certain manufacturers in recent years have also been devel-oping hybrid or modular chemosensory systems that use multiple sensortypes. The MOS and MOSFET sensors are considered to be ‘hot’ sen-sors, and the remaining sensor technologies and CP and QMB sensorsare considered to be ‘cold’ sensors due to their operating temperatures(Schaller and Bosset 1998). Recently, there has been an increase in thedevelopment of nanoscale sensors (primarily using metal oxide based)with an aim to miniaturize the sensing device.

MOS sensors and CP sensors are the two technologies that have beenused the longest in commercial electronic nose systems. Conducting

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polymer sensors are easily fabricated and are fabricated with a highdegree of reproducibility. They also have the greatest range of selectivityand sensitivity. However, the MOS-based systems are less susceptibleto water vapor variations, are more robust, have a longer useful life, andare cheaper to replace.

Metal Oxide Sensors

MOS sensors consist of a ceramic substrate heated by wire and coatedby a metal oxide semiconducting film. The metal oxide coatings usedare often n-type oxides that include zinc oxide, tin dioxide, titaniumdioxide, or iron (III) oxide. P-type oxides such as nickel oxide or cobaltoxide are also used.

The main differences between sensors using the two types of oxidecoatings are the types of compounds with which they react. The sen-sors using n-type (n = negative electron) coatings respond to oxidizingcompounds because the excitation of these sensors results in an excessamount of electrons in its conduction band. The p-type (p = positivehole) sensors develop an electron deficiency when excited and thereforeare more prone to react with reducing compounds (Schaller and Bosset1998).

MOS sensors have a low sensitivity to moisture and are robust. Theytypically operate at temperatures ranging from 400◦C to 600◦C to avoidmoisture effects. These sensors are not typically sensitive to nitrogen-or sulfur-based odors, but they are sensitive to alcohols and other com-bustibles (Bartlett et al. 1997).

Conducting Polymer Sensors

Conducting polymer sensors are composed of a conducting polymer,a counter ion, and a solvent that are grown from a solution onto anelectrode bridging a 10-micrometer (μm) gap to produce a resistor.Measurements are made by measuring changes in resistance. Alteringone or more of the three comprising materials produces different sensors.The single stage fabrication technique allows the reproducibility fromthe production of one sensor to the next to be consistent.

Conducting polymer sensors are formed electrochemically onto a sil-icon or carbon substrate. This results in a polymer in an oxidized formthat has cationic sites and anions from the electrolyte. Sensors made

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from polymers based on aromatic or heteroaromatic compounds, suchas polypyrrole, polythiophene, and polyaniline, are sensitive to manyvolatile compounds and experience a reversible change in conductions(Persaud et al. 1999). Conduction is achieved in the electrically conduc-tive polymer by electron transport, not ion transport. The carriers areassociated with the cation sites (Hodgins and Simmonds 1995).

Although the conducting polymer sensors have the greatest rangeand balance between selectivity and sensitivity, they are more sensitiveto water vapor and are more expensive to produce and replace. Theycan be used at room temperatures and temperatures moderately higher.This allows for future development of handheld electronic nose instru-ments and avoids problems associated with the breakdown of volatilesat the sensor surface of systems using increased heating (Persaud et al.1999).

Quartz Microbalance

Quartz microbalance (QMB) sensors or quartz crystal microbalance(QCM) sensors evolved from a larger group of piezoelectric crystalsensors. These sensors use crystals that can be made to vibrate in asurface acoustic wave (SAW) mode or bulk acoustic mode (BAW).The sensors are made from thin discs composed of quartz, lithium nio-bate (LiNbO3), or lithium tantalite (LiTaO3) then coated. The coatingmaterials are usually GC stationary phases but may be any nonvolatilecompounds that are chemically and thermally stable (Schaller andBosset 1998).

The quartz microbalance sensors respond to an aroma through achange in mass. When an alternating voltage is applied at a constanttemperature, the quartz crystal vibrates at a very stable and measur-able frequency. This is dependent upon the assumption that viscoelasticeffects are negligible (Bartlett et al. 1997). Upon exposure to volatilecompounds in an aroma, the volatiles adsorb onto the GC phase coat-ing of the sensor, which causes a change in the mass of the sensor.The change in mass results in a measurable change of the oscillatingfrequency of the sensor. QMB sensors have developed as a useful elec-tronic nose technology because they produce stable responses and areformed through a simple fabrication process.

In reporting on trends and developments in quartz microbalancechemosensory systems, Nakamoto and Morrizumi in 1999 performed

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work examining QMB sensor responses with different aroma injectionsystems as well as model development for response prediction. QMBsensor technology continues to improve and Applied Sensor has releaseda handheld unit this year that is currently the least expensive electronicnose system on the market.

Metal Oxide Semiconductors Field Effect Transistors (MOSFET)

MOSFET sensors respond to aroma volatiles with a measurable changein electrostatic potential. Each sensor in a MOSFET system consists ofthree layers including a silicon semiconductor, a silicon oxide insulator,and a catalytic metal. The catalytic metal component is also called thegate and is usually palladium, platinum, iridium, or rhodium (Schallerand Bosset 1998). The standard transistor is an example of an “active”circuit component, a device that can amplify, producing an output signalwith more power than the input signal.

The field-effect transistor (FET) controls the current between twopoints but does so differently than the bipolar transistor. The FET oper-ates by the effects of an electric field on the flow of electrons througha single type of semiconductor material. Current flows within the FETin a channel, from the source terminal to the drain terminal. A gateterminal generates an electric field that controls the current.

Placing an insulating layer between the gate and the channel allowsfor a wider range of control (gate) voltages and further decreases the gatecurrent (and thus increases the device input resistance). The insulator istypically made of an oxide (such as silicon dioxide [SiO2]). This deviceis the metal oxide semiconductor FET (MOSFET). MOSFET sensorsare similar to MOS sensors in that they are also robust and have a lowsensitivity to water.

Surface Acoustic Wave (SAW)-Based Sensors

SAW sensors are piezoelectric quartz crystals that detect the mass ofchemical vapors absorbed into chemically selective coatings on the sen-sor surface. This absorption causes a change in the resonant frequencyof the sensor, similar to quartz microbalance based sensors. The inter-nal microcomputer measures these changes and uses them to determinethe presence and concentration of volatiles. The SAW sensor coatings

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have unique physical properties that allow a reversible adsorption ofchemical vapors.

The zNoseTM is a novel device that combines a GC system to SAW-based sensor that allows nondestructive aroma profiling by producing aspectrum much like the GC but operating at the speed of an electronicnose. The sensing system is based upon fast chromatography techniquesand a single high-Q acoustic sensor that simulates a virtual sensor arraycontaining hundreds of orthogonal sensors. The zNoseTM consists ofa heated inlet port, vapor preconcentrator, temperature-programmedGC column, and a solid-state SAW detector. The SAW sensor is atemperature-controlled quartz crystal, which absorbs vapors as they exitthe GC capillary column. The changes in the fundamental frequency ofthe SAW detector caused by absorbed mass of each condensed analyteproduces a GC showing retention times and total counts per second.Analysis of any odor is accomplished by serially polling this virtualsensor array or a spectrum of retention times. Any analyte can be cali-brated according to the retention times of a standard mixture of linearchain n-alkanes. The chromatogram provides a qualitative and quanti-tative analysis of specific chemicals in the headspace analyzed.

Data resulting from the zNoseTM measurements could be analyzedby either a chromatographic or a spectroscopic approach. For the chro-matogramic approach, selected peaks in the entire chromatogram couldbe considered and their relative areas could be compared. In the spec-tral analysis approach, the entire frequency plot will be comparedafter baseline and timeline correction. Polar olfactory images with fre-quency change as radial (r) and elution time in the angular (θ ) measure-ments of specific vapor mixtures can be obtained using the software(VaporPrintTM images) provided by the manufacturer (Staples 2000).

Issues or Drawbacks with Electronic Nose Technology

Major problems that exist with the use of the electronic nose systemsare sensor drift, poor repeatability and reproducibility due to systemsensitivity to changes in operational conditions or poor gas selectivity,and sensitivity (Roussel et al. 1998). To overcome these difficulties, itis necessary to develop a successful and efficient testing methodologyat optimum parameter settings, and periodic calibration or retraining ofthe nose is warranted.

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Issues such as sensor drift and the nature of the instruments’ discrim-inatory abilities are major concerns because electronic nose technologyis selected and developed for particular applications. To achieve thenecessary repeatability, it is necessary that the sensors in the electronicnose systems react reversibly with the compounds in a sample aroma.Sensor drift occurs when the sensors experience small additive changesover time and usage. The aging of sensors, or sensor drift, has been amajor issue of concern throughout the history of the development ofelectronic nose systems. However, some of the most recent advance-ments in electronic nose technology have been developed to deal withthis issue. Advances in design and manufacturing of sensors have toincrease the useful life of sensors, and calibration standards and artificialneural networks have been better developed to increase the reliabilityand longevity of measurements that unknowns are compared to.

In addition, optimization of system and experimental parameterscan establish more stable conditions and combat sensor drift. Mielleand Marquis (1998) performed work examining several parameters ordimensions of electronic nose analysis including sensor temperature,number of sensors, and sample incubation time, to stabilize systemresponse and lengthen the useful life of library patterns in the systemdatabase.

Roussel and others (1999) examined the influence of various experi-mental parameters on the multisensor array measurements using an elec-tronic nose with tin dioxide (SnO2) sensors and attempted to quantifythem. Volatile concentration in the headspace increased as the sampletemperature is increased. In screening factorial experimental designs, itis necessary that the response of the experimental parameters be mono-tonic within a studied range. Alternatively, response surface designsmust be generated to develop a model of the multisensor response.

The discriminatory power of any electronic nose chemosensory sys-tem is based upon its ability to respond measurably and repeatedly tocomponents of aromas and to respond differently to aromas with varyingcomponents. The chemical nature and concentration of the volatiles inan aroma, reaction kinetics and dynamics of those volatiles, as wellas system parameters and sample preparation affect the fundamen-tal response of each sensor. Schaak and others (1999) examined theeffects of the system parameters: injection volume, incubation time,and incubation temperature, and their effect on sensor response and dis-criminatory power for the MOS sensor-based Alpha M.O.S FOX 3000.

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Optimizing the system response of sensors in an electronic nose sys-tem through controlling system and experimental parameters is key toit being a useful analytical tool in most applications. Nakamoto andMorrizumi (1999) reported that the QMB sensor responses could bepredicted using computational chemistry. This allows optimal sensorselection for target odors. Hansen and Wiedemann (1999) performedoptimization work in using the Alpha M.O.S. (Toulouse, France) FOX4000. The experimental and system parameters were investigated tooptimize the response range of the sensors and enhance their discrim-inatory power. This work was performed using a full factorial designand examined four experimental parameters: incubation time, incuba-tion temperature, sample mass, and sample agitation rate. It was foundin this work that only the oven temperature had a major influence onvolatile generation in the sample headspace. Bazzo and others (1999)performed optimization work for the MOS sensor-based FOX 4000system to analyze high-density polyethylene (HDPE) packaging. Theoptimization work allowed the selection of discriminating sensors aswell as appropriate sample throughput conditions.

It is necessary to optimize electronic nose instrumentation to ensuresensitivity at the lowest detection thresholds. The threshold detectionlevels of 30 food aroma compounds with varying chemical structures fora MOS sensor-based electronic nose system were found to be similar toreported ortho-nasal human detection thresholds (Harper and Kleinhenz1999). Harper also found that the matrix solution used strongly influ-enced electronic nose threshold levels, and the use of a 4% ethanolmatrix solution resulted in the sensor response resistance changes abovetheir useable range. Subsequently, it is necessary to find a workablerange of sensitivity for the sensors in a chemosensory array for par-ticular samples to achieve an appropriate sensor response. It must alsobe acknowledged that although electronic nose technology continuesto improve, it still responds very differently to many compounds thandoes the human nose. For example, the human nose is not sensitive towater vapor as well as several other compounds. However, such com-pounds affect most electronic nose systems, particularly those operatingat lower temperatures. Consequently, electronic nose systems may beblinded by such compounds or not suited to discriminating others thatthe human nose is sensitive to.

The other major issue with the adaptation of the commercially avail-able system is the limitations from the software and user interface. Many

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systems allow only a limited number of samples (around 10) to developthe smell print for that particular aroma. However, when dealing withbiological products having wide variations, this becomes very limitedfor practical use. In addition, the presence of moisture in the biologicalmaterials also creates unique problems with respect to identifying thearoma.

Statistical Analysis

Problems exist with the use of electronic nose sensors such as sensordrift and poor repeatability and reproducibility caused by system sen-sitivity to changes in operational conditions or poor gas selectivity andsensitivity (Roussel et al. 1998). To overcome these difficulties, it isnecessary to develop a successful and efficient testing methodology atoptimum parameter settings. Roussel and others (1999) examined theinfluence of various experimental parameters on the multisensor arraymeasurements using an electronic nose with SnO2 sensors and attemptedto quantify them. Volatile concentration in the headspace increased asthe sample temperature was increased. In screening factorial exper-imental designs, it is necessary that the response of the experimentalparameters be monotonic within a studied range. Alternatively, responsesurface designs must be generated to develop a model of the multisensorresponse.

Response surface analysis involves the investigation of linear andquadratic effects of two or more factors. The fundamental principleof response surface methodology is to develop a simple mathematicalexpression, usually first- or second-order polynomials, that approximatethe relationship between response and the examined factors (Devineniet al. 1997). An experimental design is selected that allows a minimalnumber of experiments be used to examine a full range of values fora particular factor. Popular Box-Behnken designs are fractions of 3N

designs used to estimate a full quadratic model in N factors. They consistof all 2k possible combinations of high and low levels for differentsubsets of the factors of size k, with all other factors at their centrallevels; the subsets are chosen according to a balanced incomplete blockdesign for N treatments in blocks of size k. A number of center points,with all factors at their central levels, may also be added (Box andDraper 1987, SAS System Help 1988). The response surface analysis

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procedure uses the method of least squares to fit quadratic responsesurface regression models. The models focus on characteristics of the fitresponse function and in particular, where optimum estimated responsevalues occur.

Multivariate Factor Analysis

Statistical analysis is a key to understanding the sensor responses inan electronic nose instrument and realizing their discriminatory power.Discrimination and identification of sample recognition patterns requirethe use of multivariate factor analysis. Factor analysis is a type of multi-variate analysis that is concerned with the internal relationships of a setof variables (Lawley and Maxwell 1971). There are several multivariatestatistical methods used among electronic nose systems.

Multivariate Discriminant Analysis and Principal Component Anal-ysis (PCA) are factor analysis methods that are most common to elec-tronic nose data analysis software and are the primary discussion top-ics. Other types of factor analysis, such as cluster analysis, partial leastsquares, Soft Independent Modeling of Class Analogy, and ArtificialNeural Networks are also discussed briefly. Great length is given to dis-cussion of the descriptive statistics quantifying the amount of separationbetween sample classes and identification of unknowns, particularly theMahalanobis distance.

Principal Components Analysis

PCA allows data exploration and was initially developed and proposedby Hotelling in 1933. It is the extraction of principal factors through theuse of a component model. This analysis process does so by assessing thesimilarities between samples and the relationships between variables. Itis a linear technique and uses the assumption that response vectors arewell described in Euclidean space (Bartlett et al. 1997). The object is todetermine whether samples are similar or dissimilar and can be separatedin homogenous groups and to determine which variables are linked andthe degree to which they correlate. PCA summarizes information con-tained in a database in subspaces with the object of reducing the numberof variables and eliminating redundancy (Gorsuch 1983, Jolliffe 1986).

In PCA, the principal factor method is applied to a correlation matrixwith unit values as the diagonal elements. The factors then give the most

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suited least squares fit to full correlation matrix, with each factor rankedbased upon the amount of the total correlation matrix that it accountsfor. The principal components of the analysis are linear combinations ofthe original variables, and the discerned information from the analysisare presented in two- or three-dimensional spaces relative to the chosencomponents, which are classified based upon the level of informationthat they produce. The smaller factors are generally dropped from themodel because they carry a trivial portion of the total variance anddo not provide significant information (Gorsuch 1983, Alpha M.O.S.2000). PCA is a form of dimension reduction factor analysis where therelationships of a set of quantitative variables are examined and trans-formed into factors based on the amount of contributed variability to thesystem. Although PCA does not ignore covariances and correlations, itconcentrates on variances. The principal components are selected andranked based on the amount of total variation, not the variation that mostdiscriminates among classes of observations. This method of analysisis commonly used to reduce the number of variables used prior to per-forming discriminant analysis in order to make the calculations in thelatter more manageable (Jolliffe 1986).

Discriminant Analysis

Multivariate discriminant analysis, also known as discriminant functionanalysis, discriminant factorial analysis (DFA), Gaussian discriminantfunction (GDF), and canonical discriminant analysis (CDA), is the mostcommon analysis method used by electronic nose systems to separateclasses of observations in a database (Lawley and Maxwell 1971).

CDA is a dimension reduction technique that creates new canonicalvariables by taking special linear combinations of the original responsevariables. The canonical variables of the CDA, in some sense, are similarto principal components of the PCA. The principal advantage of CDAis its ability to allow the researcher to visualize the observations, whichare classified into the different categories, in two- or three-dimensionalspace. Another advantage of CDA is that the output from a PCA can beused as an input for the CDA, thus the data visualized. If possible, onecan attempt to interpret the canonical variables (Johnson 1998).

Canonical correlation analysis (CCA) is generally performed whenthere is a need to compare groups of variables. It helps in reducingthe dimensionality of the data. CCA can be used to summarize the

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underlying relationship between groups of variables by creating newvariables from the existing groups of variables. These new variables arecalled canonical functions. While performing the CCA, the optimumnumber of canonical functions can be known only after performing apreliminary CCA. Generally the option NCAN = 2 is used to limit thenumber of canonical functions generated to two. Interpretation of canon-ical functions is generally considered to be difficult (Johnson 1998).

CDA may be used to determine descriptive variables that predictthe divisions between groupings when information regarding samplegroupings is known ahead of time. An algorithm is used to determinelinear combinations of new descriptive variables that separate the pre-determined groups as much as possible. A set of data Nx is partitionedinto m subsets {N1

x, N2x, . . . , Nk

x, . . . , Nm−1x , Nm

x } that represent differentquality descriptors. CDA proposes to then develop an algorithm withnew variables {F1, F2, . . . , Fj, . . . , Fs} that correspond to the directionsthat separate the subsets. This method allows the classification predic-tion of an unknown as one of these groups through the computationof the distance to the centroid of each of the groups. The unknown isclassified with the closest associated group (Lawley and Maxwell 1971,Harman 1976, AlphaM.O.S. 2000).

CDA is a dimension-reduction type of factor analysis related to prin-cipal component analysis and canonical correlation. The manner inwhich the canonical coefficients are derived parallels that of a one-way MANOVA. In a CDA, linear combinations of the quantitative vari-ables are found that provide maximal separation between the classesor groups. Given a classification variable and several quantitative vari-ables, this procedure derives canonical factors, linear combinations ofthe quantitative variables that summarize between-class variation inmuch the same way that principal components summarize total vari-ation. The discriminant function procedure in the SAS Software (Cary,North Carolina), PROC DISCRIM, develops a discriminant criterion toclassify each observation into one of the groups for a set of observationscontaining one or more quantitative variables and a classification vari-able defining groups of observations. The derived discriminant criterionfrom a training or calibration data set can be applied to an unknown dataset (Harman 1976, SAS System Help 1988).

In CDA, the classification of an unknown observation involves plot-ting the unknown observation and determining whether the point fallsnear the mean point of one of the groups. If the unknown observation is

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254 Nondestructive Testing of Food Quality

close enough, the sample can be classified as being the same material.If the point is far away from all groups in a database, the sample may bea different material or have a different concentration from the sampleobservations used as the training set data. The approach is relativelystraightforward except for the concept of how being near a group isactually defined. Visual inspection of a CDA projection plot providesuseful initial information. However, it is not a viable method for realworld discriminant analysis applications. Quantification with a math-ematical equation is needed to measure the closeness of the unknownpoint to the mean point of the groups in a database.

The Euclidean distance is one such measurement technique. Theunknown response can be used in a formula to calculate the distance ofthe unknown point to the group mean point. This would be an acceptablemethod except for two facts. The Euclidean distance does not give anystatistical measurement of how well the unknown matches the trainingset, and the Euclidean distance only measures a relative distance fromthe mean point in the group. The method does not take into accountthe distribution of the points in the group, because the variation alongone axis is often greater than the variation along another axis. Thetraining set group points tend to form an elliptical shape. However,the Euclidean distance describes a circular boundary around the meanpoint. The Euclidean distance method is not an optimum discriminantanalysis algorithm, because it does not take into account the variabilityof the values in all dimensions (Jolliffe 1986, Marcus 2001).

Mahalanobis distance (D), however, does take the sample variabilityinto account. It weights the differences by the range of variability inthe direction of the sample point. Therefore, the Mahalanobis distanceconstructs a space that weights the variation in the sample along theaxis of elongation less than in the shorter axis of the group ellipse. Interms of Mahalanobis measurements, a sample point that has a greaterEuclidean distance to a group than another sample point, may have asignificantly smaller distance to the mean if it lies along the axis of thegroup that has the largest variability (Jolliffe 1986, Marcus 2001).

Mahalanobis distances examine not only variance between the sam-ples within a group, but also the covariance among groups. Anotheradvantage of using the Mahalanobis measurement for discrimination isthat the distances are calculated in units of standard deviation from thegroup mean. Therefore, the calculated circumscribing ellipse formedaround the cluster of a class of observations actually defines a preset

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number of standard deviations as the boundary of that group (Jolliffe1986). The user can then assign a statistical probability to that mea-surement. For relatively large samples and normality assumptions D/2behaves like a normal multivariate z with standard deviation 1. In the-ory, D/2 can be examined to obtain an indication of the separationbetween samples and their estimated populations, and the probabilityof incorrect assignment. A D value of 5 would correspond to aboutfive standard deviation separations that cover approximately 99% ofa population given a multivariate normal distribution. Separation ofgroups quantified with a Mahalanobis distance greater than 5 wouldindicate very little overlap. In practice, the determination of the cut-off value depends on the application and the type of samples (Marcus2001).

The Mahalanobis distance, expressed as D2 or D, is consequently thestatistic most often used in multivariate analyses to identify unknownsamples and to quantify the probability that they belong to the identifiedclass. Mahalanobis distance is the most appropriate measure of multi-variate relationships when data are normally distributed, homoscedastic,and has equality among covariance matrices. Most software give a clas-sification matrix of the Mahalanobis distances to each group centroidand identifies the sample as belonging to the group with the smallestdistance (Jolliffe 1986).

The Mahalanobis metric in a minimum-distance classifier is generallyused as follows. Let m1, m2, . . . , mc be the means for the c classes,and let C1, C2, . . . , Cc be the corresponding covariance matrices. Anunknown vector x is classified by measuring the Mahalanobis distancefrom x to each of the means, and assigning x to the class for which theMahalanobis distance is minimum (Knapp 1998).

Articles are often not specific about reporting whether D or D2 is beingused, and usually it is only discerned in context. Mahalanobis D or D2 isa descriptive measure of similarity or adjusting for pooled values withinvariance and covariance. D2 is calculated first and often preferred, asvariance to standard deviation, because of its additive and has knowndistribution. However, only the standard deviation is in original measureunits. Also, D is used as the ruler in canonical variate space or canonicalprojection plots and so is in the more useful form when examining thedata graphically (Marcus 2001).

D2 is approximately chi-square distributed with p degrees of free-dom. Therefore, an unknown is still assigned to the population with

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256 Nondestructive Testing of Food Quality

the smallest D2. Furthermore, using this idea, one can decide not toassign an unknown if all D2 values are larger than some cutoff basedon the chi-square distribution with p degrees of freedom. It is also auseful statistic for finding multivariate outliers in a sample. If the datafollow a multivariate normal distribution, then the D2 values will beapproximately chi-squared distributed with p degrees of freedom. Forstandardized principal components, D2 is the sum of squares (Jolliffe1986).

One problem with the Mahalanobis distance is that, because it isa summation of coefficient products, the number of observations andindependent variables used in the calculation affects it. As with manymultivariate quantitative methods, the Mahalanobis distance solves formultiple dimensions simultaneously. However, the Mahalanobis modeltends to become overfit very quickly as more independent variables areadded. This is similar to an increased R2 value for models when anincreased number of independent variables are used and only logicalwhen the method of calculating Mahalanobis matrix is considered. D2

cannot be zero because it is always a quantity greater than zero. There-fore, D2 is a biased estimate whether the null hypothesis is true or not.The size of the bias can be substantial when the sample sizes are smallrelative to the number of variables measured. An unbiased Mahalanobisdistance (D2

u) is given by Equation 10.1 (Marcus 2001):

D2u(1|2) = (n1 + n2 − p − 3) D2

(n1 + n2 − 2)− (n1 + n2) p

n1n2

(10.1)

where

D2 = Mahalanobis distance between classes 1 and 2, dimensionlessn1 and n2 = number of observations in class 1 and 2p = number of independent variables

The answer to combining these apparently opposing necessities intoone method for sample discrimination lies in first reducing the sensordata in electronic nose systems into its component variations with prin-cipal component analysis. A commercially available electronic nose,the Cyranose 320, uses this method to avoid over-fitting and instabilityin calculations. The PCA method indirectly performs a sensor selec-tion and reduces the number of variables used in building a canonicalmodel. It is recommended for that instrument that a breakpoint of 5 for

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Electronic Nose Applications in the Food Industry 257

the Mahalanobis distance, D, indicates well-separated groups (CyranoSciences 2000).

For cases where the number of observations or independent variablesused in the discriminant analysis differ, it is not fair to compare Maha-lanobis distances and so a standardized value must be compared. Anaverage or unbiased Mahalanobis distance calculated by using a propor-tionality constant accounting for the number of independent variablesand observations may be used or the F-value for the Mahalanobis dis-tances may also be used for comparison as well. Hotelling’s T-square,T2, equals this distance except for an included proportionality constant.A problem with both D2 and T2 is that they are based on the inverseof the variance-covariance matrix, S = X′X, with the assumption that Xhas been centered and scaled. This inverse can be calculated only whenthe number of variables, p, is small compared to the number of trainingset samples, N.

Mahalanobis D2 is also part of the formula for finding the two-sampleextension of student’s t test, testing that the centroids of two multivariatepopulations have the same mean. This is Hotelling’s T2 test, and is amaximum likelihood test. D2 can be converted to T2 and then to an Fstatistic, which has p, and n1-n2-p-1 degrees of freedom by multiplyingby appropriate constants based on the number of observations in eachclass and the number of variables used. This statistic is known to be fairlyrobust to violations of normality assumptions, but is more sensitive toequality of variance assumptions, particularly for disparate sample sizes(SAS System Help 1988).

Because the F-value incorporates the Mahalanobis distance and alsotakes into account the number of observations used, the degrees of free-dom, and the number of independent variables used, it is a useful termfor comparing discriminant analyses performed by different systems.The Wilks’ Lambda value is calculated from the inverse of the productof each of the eigenvalues incremented by one. Because Lambda is atype of inverse measure, values of Lambda that are close to zero denotehigh discrimination among groups. The F-value for the Wilks’ Lambdaprovides a quantitative value for the overall discrimination of all theclasses involved in the discriminant analysis. While it is a useful num-ber for quickly quantifying the amount of separation between classes, itdenotes total discrimination and does not indicate if the total amount ofseparation is due to a balanced separation of all the classes or a very largeseparation of some classes while having little separation between other

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258 Nondestructive Testing of Food Quality

classes. Consequently, the most useful value in comparing Mahalanobisdistances from different systems are the F-values for the Mahalanobisdistances because they give a standardized value of the separationbetween each of the three classes analyzed in the discriminant analysis.

The percent correct during cross-validation also provides additionalinformation regarding the degree of separation. After the discriminantmodel is developed, the most common method of validation is to usewhat is commonly referred to as the “leave one out” method or cross-validation. In this cross-validation, each data point is removed and testedas an unknown to the model developed with the remaining data points.A value of 100% indicates complete separation of all classes. A valueof 90% is usually considered sufficient validation for a database model.The user sets the actual required percent recognition for a training setvalidation based on the application requirements. This is also oftencalled the “leave one out” procedure, as each observation is left out inturn in the analysis and then identified using all of the remaining data(SAS System Help 1988).

Equations 10.2–10.5a, used to calculate the discussed terms, are givenas follows (Jolliffe 1986):

D2(1|2) = (x1 − x2)′COV−1(x1 − x2) (10.2)

FMahanalobis(1|2) = (n1 − 1) + (n2 − 1) + (n3 − 1) − p + 1

(n1 − 1) + (n2 − 1) + (n3 − 1)p

n1n2

n1 + n2

D2

(10.3)

� = 1

1 + λ1

1

1 + λ2

(10.4)

F� = 1 − �1/t

�1/t

[N − 1 − 0.5(p + k)] t − 0.5 [p(k − 1) − 2]

p(k − 1)1(10.5)

t =√

p2(k − 1)2 − 4

p2 + (k − 1)2 − 5(10.5a)

where

D2(1|2) = Mahalanobis distance between classes 1 and 2, dimensionless

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Electronic Nose Applications in the Food Industry 259

FMahalanobis(1|2) = F-value for Mahalanobis distance between classes 1and 2, dimensionless

x1 and x2 = the geographic means of classes 1 and 2, dimensionlessn1, n2, and n3 = number of observations in each classp = number of independent variables�� = Wilks’ Lambda, dimensionlessλ1 and λ2 = first and second eigenvalues derived from the discriminant

analysis, dimensionlessF� = F-value for Wilks Lambda, dimensionlessCOV = pooled variance matrix, dimensionlessk = Number of classesN = total number of observations in all classes

Discriminant analysis is used primarily to answer three basicquestions:

1. Is the number of sensors and the sensor data obtained from the trainingset useful for building a model to classify the apples into its maturitylevel or stage?

2. Can the model classify correctly the unknown apples of varying matu-rity levels?

3. If not, what is the miscalculated percentage?

Discriminant analysis, also known as classification analysis, is a multi-variate method for classifying observations into appropriate categories(apples into appropriate maturity levels) (Johnson 1998).

The concept of discriminant analysis is analogous to regression anal-ysis, as the goal of the latter being to predict the value of the dependentvariable, while that of the former being to predict the category of theindividual observation (Johnson 1998). The main difference is that mul-tivariate (discriminant analysis) approach is used when the variables arenot independent. This condition violates the assumption of regression.

According to Johnson (1998), there are four nearly equivalent waysto develop a discriminant rule to classify observations into categories:Likelihood Rule, Linear Discriminant Function Rule, Mahalanobis Dis-tance Rule, and Posterior Probability Rule. There are three differentmethods that can be used to verify or estimate the probability of the cor-rect classification of the observations and are described in detail below(Johnson 1998).

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260 Nondestructive Testing of Food Quality

1. Resubstitution Method: The resusbstitution method employs a dis-criminant rule to the same data that were used to develop the rule andcheck how many observations were correctly classified by the ruleinto the correct categories. This method presumes that if a rule can-not classify properly on the original data used to build the rule, thenthere is a poor chance of it doing well with a new data set. The majordrawback with this method is its overestimation of the probabilities,when it classifies correctly. In SAS, this method can be invoked usingthe DATA = option (lists).

2. Holdout Method: This method uses a holdout set or a test data set,where we know which observation belongs to which particular cate-gory, and the hold out data set is not used to develop the discriminantrule. The major drawback for this method is that one has to sacrificethe hold out data in order to build the discriminant rule, thus notbeing able to develop the best possible discriminant rule. In SAS,this method can be invoked using the DATA = option (test data).

3. Cross-Validation Method: Lachenbruch and Mickey (1968) first pro-posed the cross-validation method, also known as jackknifing. This isthe preferred method when compared to the above two discriminantrules. The first observation vector is holdout and the remaining dataare used to construct the discriminant rule, then the rule is used toclassify the first observation, and then it checks whether the obser-vation is correctly classified into the particular category. In the nextstep, the second observation vector is removed, but the first observa-tion is replaced back into the original data, and then the discriminantrule is constructed. The rule thus developed is used to classify thesecond observation and thus check whether the observation is clas-sified correctly. Thus, the same process is continued for the entiredata set and also noting down the category it is being classified. Itis claimed that this method is almost unbiased. In SAS, this methodcan be invoked using the DATA = option (cross lists).

Variable Selection ProcedureSince the number of variables involved in this study is high (32), avariable selection procedure is used to reduce the number of vari-ables, which are really necessary for effective discrimination of the data.The three types of variable selection procedures are Forward SelectionProcedure, Backward Elimination Procedure, and Stepwise SelectionProcedure. Johnson (1998) recommends the Stepwise Selection Proce-dure when the number of variables exceeds 15.

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Other statistical analyses used include Partial Least Squares (PLS),Soft Independent Modeling of Class Analogy, Cluster Analysis, andthe use of Artificial Neural Networks. Discriminant analysis shouldnot be confused with cluster analysis and principal component analysisbecause discriminant analysis requires prior knowledge of the classes.The data used in cluster analysis do not include information on classmembership. Its purpose is to construct the classification (Guertin andBailey 1970, SAS System Help 1988).

PLS is a statistical method that may be used to extract quantitativeinformation. It is an algorithm based on linear regression and can be usedto extract concentration sensory score predictions. The PLS algorithmattempts to correlate a matrix containing quantitative measurements to apredictive matrix using a matrix of sensor measurements from the elec-tronic nose instrument. After building the model, the predictive matrixis used to predict quantitative information contained in an unknownsample (Gorsuch 1983, AlphaM.O.S. 2000).

Soft Independent Modeling of Class Analogy (SIMCA) is a factoranalysis method that is similar to PCA and CDA. This method classifiesunknown samples using a comparison to a database composed of onegroup only. PCA is first performed on the data with the objective tofind the subspace that most precisely contains samples. Each sample isexplained in terms of its projection on the subspace and its projectionon the orthogonal subspace. This matrix composed of a set of sensorobservations induces two new matrices. The threshold identificationcriteria are set with theoretical values for the norm of the residual partof the predictive matrix and the Mahalanobis distance of the quantitativescores matrix to the centroid of the values projected in the subspace.SIMCA modeling works with as few as five observations from eachpopulation with no restriction on the number of independent variables(Jolliffe 1986, AlphaM.O.S. 2000).

Cluster analysis deals with data sets that are to be divided into classeswhen very little is known beforehand about the groupings. It providesan entry into factor analysis by establishing groupings within a data set.Within cluster analysis, principal components are calculated and used toprovide an ordination or graphical representation of the data, or either toconstruct distance measures. The majority of cluster analysis techniquesrequire the computation of similarity or dissimilarity among each pair ofobservations with the objective of clearly identifying group structures.The PCA graphical representation is often useful in verifying a clusterstructure. This method of analyses is also often used in conjunction

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262 Nondestructive Testing of Food Quality

with artificial neural networks to perform the classifications (Guertinand Bailey 1970, Jolliffe 1986).

Artificial Neural Networks

In many applications, there may be many references or combinationsof sensor data to which the unknown needs to be compared. In thesecases, an artificial neural network (ANN) is often used to analyze datafrom the sensor array. ANNs are particularly useful in analyzing datafrom hybrid electronic nose instruments where combined data must beanalyzed. They are also particularly useful when the data to be analyzedexhibits a non-Gaussian distribution. The artificial neurons carry outa summation or other simple equation using predetermined weightedfactors. The weighted factors are determined during the training of theneural network and are set arbitrarily before it is trained (Hodgins andSimmonds 1995). The training process for any ANN is a defining factorfor its success.

The training of an ANN is accomplished by inputting data from thesensor array to the artificial neurons defined by a set answer for thatdata. The neural network calculates the values at all the neurons in thehidden and output layers. A back propagation technique is then used toadjust the weighted factors until the correct output is achieved. This isrepeated for all sensor data for all samples in a training set. A commonbreakpoint value for determining if the ANN is sufficiently trained foran application is if the weighting factors vary by no more than 10%during a training run (Hodgins and Simmonds 1995).

A trained ANN can then be used to identify an unknown sample bycomparing it to all of the references in the training set. In practice, ANNsdo not always identify unknowns that are one of its references with100% confidence. However, ANNs do provide a means for performingnumerous comparison calculations quickly to provide identification.

Applications in the Food Industry

Many food industry professionals are skeptical about the claims andcapabilities of the technology, and the need for developing trainingsets for each application is also slowing down the adaptation of thistechnology in a wider scale. Schaller and Bosset (1998) reviewed the

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Electronic Nose Applications in the Food Industry 263

applications of different electronic nose systems to different foodsincluding meat, grains, coffee, beer, mushrooms, cheese, sugar, fish,blueberry, orange juice, cola, and alcoholic beverages, as well as pack-aging. They concluded that the electronic nose can be regarded as aninteresting tool for a quick “yes or no” quality test, and could occasion-ally replace sensory analysis, and even perform better, in cases wherenonodorous or irritant gases need to be detected. In the last few years,more and more electronic nose applications have been developed to beimplemented in the food industry (Table 10.1).

The technology has excellent potential for use in quality assuranceand quality control applications, and compliance of ingredients fromsuppliers. Baby and others (2005) have evaluated the discriminationability of modular sensor system (MOSES) to classify medicinal plantsand found that the discrimination between Valeriana officinalis and Vale-riana wallichii types was achieved very successfully. Classification ofmilk samples from one dairy product and based on fat content within aparticular dairy product was developed using a support vector machines(SVM) approach using metal oxide sensors (Brudzewski et al. 2004).Similarly, Collier and others (2003) attempted using metal oxide sen-sors to classify various dairy products and compared that to screen-printed electrochemical arrays. In addition, the technology can be usedto monitor quality changes, especially oxidative changes in foods hav-ing adequate lipid content and off-flavor development in food productscaused by spoilage microorganisms. Aparicio and others (2000) stud-ied the rancidity in olive oils using conduction polymer-based sensorsand found that they could detect the rancidity at very low levels. Simi-larly oxidative rancidity in milk using metal oxide semiconductor thinfilm-based sensors was done (Capone et al. 2001).

Another promising area for which this technology is findingwidespread adaptation is evaluating fruit maturity. Tin oxide-based sen-sors were used by Simon and others (1996) to monitor blueberry flavor.Benady and others (1992) related the data derived from electronic sensesto various ripeness indices such as slip pressure and classical volatilemeasurements in melons. Data from sensory panels were correlatedto the electronic nose data that registered gases from the degradationreactions in tomatoes (Simon et al. 1996). Young and others (1999)demonstrated that electronic nose technology using metal oxide sensorscould be used as a potential maturity indicator to predict the harvestdate for Royal Gala apples. According to Young and others (1999), the

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Page 29: Nondestructive Testing of Food Quality || Electronic Nose Applications in the Food Industry

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Page 30: Nondestructive Testing of Food Quality || Electronic Nose Applications in the Food Industry

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Page 31: Nondestructive Testing of Food Quality || Electronic Nose Applications in the Food Industry

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Page 32: Nondestructive Testing of Food Quality || Electronic Nose Applications in the Food Industry

268 Nondestructive Testing of Food Quality

electronic nose analysis was approximately 40 times more sensitive thanthe headspace/gas chromatography.

With higher correlation to human sensory panels, this technology alsohas great potential in the product development activities. With recentdevelopments in developing real time sensing with the gas sensors,online implementation for process control is very promising. Cimanderand others (2002) have developed a system using MOSFET-based sen-sors integrated with a near infrared sensing system for online monitor-ing of yogurt fermentation. Results showed that proposed online sensorfusion improves monitoring and quality control of yogurt fermentationwith implications for other fermentation processes.

To determine the ripening stage in Emmental cheese, a quartzmicrobalance-based sensing system was developed by Bargon and oth-ers (2003) and monitors the ripening process continuously. Similarly,the shelflife of Crescenza cheese stored at different temperatures wasmeasured by a metal oxide-based sensing system (Benedetti et al. 2005).The technology also has potential to be used to detect pathogen contam-ination in selected foods with a sufficiently larger population but withinthe risk level for human illnesses.

In addition to application in the food and bioprocess industries, elec-tronic nose technology has been explored for use in the medical field aswell. An electronic nose can examine odors from the body and identifypossible health-related problems. Odors in the breath can be indicativeof infections, diabetes, and gastrointestinal, sinus, and liver problems.Infected wounds and tissues emit distinctive odors that can be detectedby an electronic nose. Odors coming from body fluids such as blood andurine can indicate liver and bladder problems and is measuring with ablood gas analyzer. There is extensive literature available in this area.The scope of this book is limited to applications of electronic nose tech-nology in the food industry, therefore information related to medicalapplications are not included here.

Case Studies

Researchers at Virginia Tech have used electronic nose technology forvarious food-related applications including, but not limited to, detec-tion of plasticizers in packaging material (van Deventer and Mallikar-junan 2002), discrimination of oil quality (Innawong et al. 2005),

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Electronic Nose Applications in the Food Industry 269

determination of fruit maturity (Pathange et al. 2006, Athamneh et al.2006), detection of oxidation-related quality changes in meat, peanuts,and milk (Ballard et al. 2005, Mallikarjunan et al. 2006), and spoilagedetection in seafood products (Hu et al. 2005). Contrary to having onlyone type of electronic nose, the research facility at Virginia Tech hasall three major electronic nose systems, both in handheld format anddesktop format. Thus, it provided an opportunity to compare the tech-nologies for a given application and find a system that is suitable to thatapplication.

Detection of Retained Solvent Levels in Printed Packaging MaterialThe packaging suppliers use plasticizers to make the printing stay onthe packaging material. Some of the plasticizer could transfer into thefood product and alter the taste and flavor. In addition, at higher lev-els, these plasticizers pose health risks and the industry wants to limitthe level of plasticizer in printed packaging materials. Currently, theindustry uses a human sensory panel-based sniff test and found that thechromatographic techniques did not correlate with sensory results. Itwas decided to explore the feasibility of using electronic nose technol-ogy for this application. Three different types of electronic nose systemswere tested for their ability to discriminate the packaging based on thecontamination level, ease of use for training and prediction, and repeata-bility. (See Figure 10.1.)

First and foremost, each system was optimized for its performanceto obtain maximum sensor response. The results of the optimization aredescribed by van Deventer and Mallikarjunan (2002).

Performance analyses of these systems, which use three leading sen-sor technologies, showed that the conducting polymer sensor technol-ogy demonstrated the most discriminatory power. All three technologiesproved able to discriminate among different levels of retained solvents.Each complete electronic nose system was also able to discriminatebetween assorted packaging having either conforming or nonconform-ing levels of retained solvents. Each system correctly identified 100%of unknown samples. Sensor technology had a greater effect on per-formance than the number of sensors used. Based on discriminatorypower and practical features, the FOX 3000 and the Cyranose 320 weresuperior (van Deventer and Mallikarjunan 2002).

Page 34: Nondestructive Testing of Food Quality || Electronic Nose Applications in the Food Industry

Fig

ure

10.1

.C

om

par

iso

no

fth

ree

typ

eso

fel

ectr

on

icn

ose

syst

ems

ind

iscr

imin

atin

gp

ack

agin

gm

ater

ialb

ased

on

the

level

of

pla

stic

izer

s.

270

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Electronic Nose Applications in the Food Industry 271

Discrimination of Frying Oil Quality Based on the Usage LevelVarious criteria are being used to judge when frying oils needs to be dis-carded. In restaurants and food services, changes in physical attributesof frying oils, such as oil color, odor, and foam level have been usedas an indicator of oil quality. In the food industry, not only physicaltests but also chemical tests are used to measure oil quality, includ-ing acidity, polymer content, and/or total polar content. Many of themethods do not correlate with oil quality effectively, and many timesthey are time consuming and expensive. Previous monitoring methodsused to analyze volatile compounds and aroma in food needed eithera highly trained sensory panel or GC/MS techniques. Thus, there hasbeen a genuine need for a quick, simple, and powerful objective test forindicating deterioration of oil.

This study was conducted to determine the possibility of using achemosensory system to differentiate among varying intensities ofoil rancidity and investigate discrimination between good, marginal,and unacceptable frying oils. Fresh, 1-day, 2-day used, and discardedfrying oils were obtained from a fast food restaurant in each fry-ing cycle for 4 weeks. The oil samples were analyzed using a quartzmicrobalance-based chemosensory system. The discrimination betweengood, marginal, and unacceptable frying oils with regard to ranciditywas examined, and the results were compared to their physicochemicalproperties such as dielectric constant, peroxide value, and free fatty acidcontent. The different qualities of frying oils were successfully evaluatedand discriminated using the chemosensory system. Good correlations(0.87 to 0.96) were found between changes in physicochemical proper-ties of oil and the sensor signals (Innawong et al. 2005). Based on theresults, oils from two different restaurants were obtained with differentusage levels to discriminate between the usage levels (Bengtson et al.2005). See Figure 10.2.

Evaluating Apple MaturitySometimes harvested apples are a mixture of mature, immature, andovermature fruits, and the quality of an apple fruit depends primarilyon its level of maturity at the time of harvest. Even though the externalappearance of an immature fruit may look perfect to harvest, store, andsell, due to their preclimacteric physiological condition, these apples donot ripen normally, and thus their taste is strongly impaired due to lackof full-flavor compounds. On the other hand, overmature fruits have

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Goo

d

Fres

hFr

esh

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Mar

gina

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ant B

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ant A

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ure

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iscr

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ose

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rysy

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.

272

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Electronic Nose Applications in the Food Industry 273

59

8

8

6

4

4

44

44

33

19

9

99

3

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56

84

0

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1

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Figure 10.3. Evaluation of apple maturity using a conducting polymer-based sensing

system.

shorter storage life, soften rapidly, develop storage disorders such as offflavor, lack firm texture, and are unattractive in appearance.

Currently, random and destructive sampling techniques are used toevaluate apple quality. Thus, there exists a need to develop a nonde-structive technique to assess apple quality. Gala and York apples wereharvested at different times to obtain different maturity groups (imma-ture, mature, and ripe). Headspace evaluation was performed first, andmaturity indices were measured within 24 hours after harvest. Individ-ual apples were placed in a 1.5-liter glass bottle, and the headspace gasfrom the glass bottle was exposed to the electronic nose. A conductingpolymer-based sensing system was used for apple maturity evaluation.Maturity indices such as starch index, puncture strength, total solublesolids, and titratable acidity were used to categorize apples into threematurity groups referred to as immature, mature, and overmature fruits.See Figure 10.3.

Multivariate analysis of variance (MANOVA) of the electronic nosesensor data indicated that there were different maturity groups (Wilks’Lambda F = 3.7, P < 0.0001). From the discriminant analysis (DA),the electronic nose could effectively categorize Gala apples into the

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274 Nondestructive Testing of Food Quality

three maturity groups with the correct classification percentage of 83%(Pathange et al. 2006).

Detection of Spoilage and Discrimination of Raw Oyster QualityThe effectiveness of two handheld electronic nose systems to assess thequality of raw oysters was studied on live oysters stored at 4 and 7◦Cfor 14 days. Electronic nose data were correlated with a trained sensorypanel evaluation by Quantitive Description Analysis (QDA) and withmicrobial enumeration. Oysters stored at both temperatures exhibitedvarying degrees of microbial spoilage, with bacterial load reaching 107

colony-forming units per gram (CFU/g) at day 7 for 7◦C storage. SeeFigure 10.4.

The Cyranose 320 electronic nose system was capable of generat-ing characterized smell prints to differentiate oyster qualities of varyingage (100% separation). The validation results showed that Cyranose320 can identify the quality of oysters in terms of storage time with93% accuracy. Comparatively, the correct classification rate for theVOCChek electronic nose was only 22%. Correlation of electronic nosedata with microbial counts suggested Cyranose 320 was able to predictthe microbial quality of oysters. Correlation of sensory panel scores withelectronic nose data revealed that the electronic nose has demonstratedpotential as a quality assessment tool by mapping varying degrees ofoyster quality.

Summary

Electronic nose technology is an emerging analytical tool that has excel-lent potential to be implemented in the food industry for quality control,quality assurance, product safety evaluation, new product development,and process control. Many systems are available in the marketplaceusing multitude of sensing technologies, software capabilities, and hard-ware configurations. The cost of the system also ranges from $5,000 to$120,000 with varying capabilities and sensitivities. The technology hasnot been recognized by the market mainly because of the lack of confi-dence in the technology and limited available applications for immedi-ate adaptation in the industry. In addition, the technology is perceivedas a technology push from the instrument manufacturers without clearimplementation strategies in the food industry. Successful development

Page 39: Nondestructive Testing of Food Quality || Electronic Nose Applications in the Food Industry

Fig

ure

10.4

.D

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ose

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275

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276 Nondestructive Testing of Food Quality

and implementation of this technology to a wide range of applicationsand subsequent research publications in the near future will provideenough support from the food industry.

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