thesis proposal sample chapters 1,2,3

CASA DEL NIŇO SCIENCE HIGH SCHOOL PAGE

CHAPTER 1

INTRODUCTION

1.1 Background of the Study

Over recent years, there has been a drastic increase in the distribution of

herbal medicines in the market worldwide [1 – 4]. The global trade has an annual

growth rate of 10-15% on the average, and has an estimated share of 20% to total

pharmaceuticals market [5]. These can be attributed to the wider availability,

lesser side effects, and lower price of herbal medicines compared to synthetic

drugs [6-7].

The increase in the popularity of herbal medicines has caused growing

demands for quality standards. This is to test their authenticities and efficacies

[8-9], and also to trace possible adulteration and substitution [10-11]. As one way

of quality control of herbal medicines, most countries have pharmacopeia made to

provide information on the morphological description of medicinal plants,

including the monographs of quality standards [12-19].

The test methods for quality control of medicinal plants involve sensory

inspection (macroscopic and microscopic examinations) as the first step toward

establishing the identity and degree of purity of herbal medicines [20-21],

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followed by chemical profiling [23]. Macroscopic inspection involves

examination of the organoleptic and morphological characteristics of plants [3].

Microscopic evaluation, on the other hand, is performed to validate what plant

parts (roots, leaves, fruit, bark) the sample contains, to determine the presence of

foreign matter, and to identify the plant by characteristic tissue [11, 22]. However,

with the two sensory inspections, the parameters are judged subjectively and

substitutes or adulterants may closely resemble the genuine material [23]. Also,

the numbers of replicate samples which can be evaluated at any time is limited by

the onset of sensory fatigue in the part of analyst.

To have a credible authentication of plant material, chemical profiling

should follow. It establishes a characteristic chemical pattern for identification

[23].This analytical identification uses instrumental techniques such as thin-layer

chromatography (TLC), high performance liquid chromatography (HPLC) [24],

mass spectrometry (MS), infrared spectrometry (IR) [25], ultraviolet/visible

spectrometry (UV-VIS), and capillary electrophoresis (CE) [26], which can be

used alone or in combination. However, these methods may either imply a long

and complicated task or would demand for high cost operation [7, 27]. Examining

and identifying the constituents of a sample would even involve collecting

samples and analyzing them in complex, bulky, and expensive laboratory

analytical instruments [28-30].

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The potential of electronic nose has been recognized in eliminating the

complexity and expensiveness in the quality control of medicinal plants [8, 31-

33]. It offers low cost and rapid technique for authentication, and it allows

qualitative identification of odiferous compounds without the need for a solvent

[5]. Electronic nose is essentially an instrument to mimic the human sense of

smell and consists of chemical imaging [28] and multiparameter [34] sensing

systems. It has advantages over human olfactory system since it depends neither

on people’s condition nor on detection capacity, it generates invariable rapid

response with time, and it can detect gases which are odorless or hazardous for

the human nose [32]. The sensor arrays for an electronic nose can be based on

metal oxides semiconductor (MOS), surface acoustic wave (SAW), bulk acoustic

wave (BAW) or quartz microbalance (QMB) and conducting polymers (CP) [28].

It has wide applications ranging from food technology, environmental,

automobile, perfume, forensics, medical diagnosis and pharmaceutical testing.

In this study, an array of quartz crystal microbalance (QCM) sensors will

be employed to differentiate the non-flowering Yerba Buena (mentha cordifolia

opiz.) from the flowering Yerba Buena (mentha arvensis linn), in crude and

powdered form. The sensing films to be used in coating QCM sensors are trioctyl

methylammonium chloride (TOMA), polyethylene glycol 1000 (PEG-1000),

polypropylene glycol 1200 (PPG-1200), polyaniline (PA), polypyrrole (PP),

polythiopene (PT), and ethyl cellulose (EC).

3


The Philippine hybrid (Mentha cordifolia opiz) is usually not flowering

and has various medicinal values compared to the flowering hybrid [35]. It yields

a volatile oil containing piperitone oxide as the principal constituent together with

other 3-oxygenated monoterpenes [36]. Tablets from the unextracted and

unpurified leaves of this mentha are available in market, which have proven to

contain analgesic properties [37] and were found to be antigenotoxic [38]. The

plant is a good source of menthol, with various commercial uses such as analgesic

balms, cough drops, nasal inhalers, dental product, confections, liquors, cigarettes,

and perfumes [35]. The leaves are used by traditional healers to cure asthma,

stomachache, fever, toothache, insect bites, dizziness, headache, and arthritis [38].

1.2 Objectives of the Study

This study generally aims to develop an array of non-selective sensor

elements (electronic nose) based on quartz crystal microbalance to discriminate

mentha plants (M. arvensis and M. cordifolia opiz). The samples will comprise

the crude and powdered leaves of each variety.

The study will also take into consideration the following phases:

1) Assembly of instrumentation system for headspace sampling, vapor

sensing, and pattern recognition.

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2) Assessment of the responses of the samples’ headspace volatiles and

evaluation of the discrimination-potential of the electronic nose.

3) Test for the authenticities and purities of samples of each variety.

4) Optimization and characterization of the performance of electronic nose

sensor system.

1.3 Significance of the Study

Authentication of crude drugs is important to assure their qualities and

efficacies. Since the three varieties of mentha differ in volatile oil constituents and

medicinal values, discrimination should be employed to avoid possible

adulteration and substitution. With this, the use of electronic nose, among the

other analytical techniques, would lead to rapid and inexpensive discrimination

for quality control.

1.4 Scope and Limitations

This study will concern mainly in the following: (1) development of

sensor’s array based on quartz crystal microbalance with the following sensing

films: trioctyl methylammonium chloride (TOMA), polyethylene glycol 1000

(PEG-1000), polypropylene glycol 1200 (PPG-1200), polyaniline (PA),

polypyrrole (PP), polythiopene (PT) and ethyl cellulose (EC) (2) consecutive

qualitative analysis of headspace samples of the three mentha (from cultivated

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and wild-collected source) using the sensor’s array (3) integration of the

responses of the entire sensors to create a pattern for authentication.

Only the matured leaves of mentha plants will be utilized in this study.

The volatile oil components (VOCs) will not be identified and quantified.

Authentication and discrimination will just be based on the created pattern.

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CHAPTER 2

REVIEW OF RELATED LITERATURE

2.1 Electronic Nose

Electronic nose is a non-selective chemical sensor array system for odor

classification. The term first appeared in the literature around the late 1980s [28].

It has two main components, the sensing system and the automated pattern

recognition system. The sensing system can be a single sensor device from which

withdrawal of transient parameters can be made, or an array of several different

sensing elements, where each element measures a different property of the sensed

chemical [39]. The chemical is carried by an inert gas to the sensor array, as

thermally equilibrated vapor, and the sensor property is altered to produce a time

dependent response [40]. A chemical compound is identified by the response

outputs interpreted by pattern recognition system.

(see Fig. 2.1) [41].

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Fig. 2.1 General Structure of Electronic Nose [41]Electronic nose is designed based on the mechanisms involved in human

olfaction. The sense of smell is able to recognize and discriminate extraneous

volatile compounds of diverse molecular structure with high sensitivity and

accuracy [42]. However, its sensitivity to odorants varies widely both with the

nature of the odor and its classification. Also, the health and endocrinological

conditions of individuals differ which cause variation in the sensitivity potential

of their noses [28]. This subjective sensation of human nose can be eliminated

using electronic nose. It offers rapid, low costs, real-time detection of volatiles.

Comparisons of human nose with electronic nose are shown in Table 2.1,

involving the systems responsible for odor recognition.

Table 2.1 Electronic Nose vs Human Olfactory System [43, 44]

Electronic Nose Human Olfactory System

Sensor/transducer

Coating

6 – 30 sensors (array)

signal processing module

pattern recognition module

Sensitivity: 1 part per million

Selectivity: < 50 odors

Receptor Neuron

Odorant Binding Protein

10 000 000 receptors

Glomeruli

Brain

1 part per trillion

10 000 – 20 000 odors

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2.2 Sensor Technologies

The ideal chemical sensor for e-nose fabrication should possess high

sensitivity, stability and reproducibility; must respond to the headspace volatiles

of the sample; short recovery time; easy calibration; and must be robust and

portable [45]. Table 2.2 shows the different types of sensor technologies for

electronic nose.

Table 2.2 Types of sensors used in electronic nose technology [46]

Sensor Type

Mode of Activity

1. Metal-Oxide Semiconductor sensors (MOS)

The oxide materials in the sensors contain chemically adsorbed oxygen species. When an electrical current passes through the sensors it causes the oxidation of gas molecules via electron transfer from the gas to the metal oxide leading to a change of resistance.

2. Chemical Field Effect Transistors (ChemFET)

Has a gas selective coating between the transistor gate and the analyte. This chemical element modifies the source-drain conduction in relationship with selected chemical species. The sensor’s conductance is measured by a differential amplifier.

3. Conducting Polymers (CP)

Responds in two different ways depending on the gas species. First, nucleophilic gases cause an increase in the CP’s resistance and electrophilic gases cause a decrease in the CP’s resistance. This process is reversible at room temperature. Second, some chemical species have a solvent type action on the polymer, making it swell. Changes in the CP’sdimension change the conductivity of the material.

4. Quartz Crystal Microbalance (QCM)

Sensors containing piezoelectric crystals used in the radio frequency resonance of quartz materials coated with membranes, the adsorption of volatile molecules onto the membrane produces a change in the magnitude of the resonance frequency.

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5. Surface Acoustic Wave devicses (SAW)

Similar to quartz crystal microbalance but operate at much higher frequencies.

MOS sensors are simple and rugged devices but require electronic circuit

to operate [47]. They also require elevated temperatures (200-5000C) for their

operation, and have slow response time which is limited by the chemical reaction

rate [48].

ChemFET can detect H2 in air, O2 in blood, NH3, CO2, and explosive gases

[47]. This sensor is attractive since it is rugged, batch fabricated, and disposable.

However, chemFET is not biocompatible and suffers drift and degradation with

time [49].

CP are widely used as odor-sensing device. It has the following

advantages: low power consumption, specificity can be achieved by modifying

the structure of the polymer, not easily inactivated by contaminants, and reusable.

However, the sensor is not corrosive resistant, must be agent specific at certain

concentrations, and is insensitive to gases like O2, Cl2, H2, and NO [47].

QCM sensors have advantages such as light weight, low power

consumption, low cost, and design flexibility. However, they are very susceptible

to external pressure, humidity, and temperature. They have poor reversibility and

slow response time [49].

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SAW devices have better detection sensitivity than BAW since they can

be operated at much higher frequencies. A SAW sensor can achieve a mass

change resolution of 3 femto-grams. It has been used to detect inorganic gases

such as NO2, H2, H2S, and SO2 and organic gases such as CH4, C6H6, and C2H5OH

[48]. Surface acoustic wave sensors are more sensitive compared to BAW sensors

[28].

Below are the different commercially available electronic nose

instruments, with their manufacturer and place of origin.

Table 2.3 Commercially available electronic nose instruments [44].

Manufacturer Place of origin

Sensor type No. of sensors

Size of instrument

Air sense analysis GmbH

Germany MOS 10 Laptop

Alpha MOS France CP, MOS, QCM, SAW

6 - 24 Desktop

AromaScan PLC UK CP 32 Desktop Bloodhound Sensors Ltd.

UK CP 14 Laptop

Cyrano Science Inc. USA CP 32 Palmtop EEV Ltd. Chemical Sensor Systems

UK CP, MOS, QCM, SAW

8-28 Desktop

Electronic Sensor technology Inc.

USA SAW 1 Desktop

Hewlett-Packard Co. USA QMS - Desktop

HKR- Sensorsysteme GmbH

Germany QCM 6 Desktop

Lennartz Electronic GmbH

Germany MOS, QCM 16-40 Desktop

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Nordic Sensor Technologies AB

Sweden MOS, QCM

22 Laptop

Sawtek Inc. USA SAW 2 Palmtop

2.3 Pattern Recognition Technologies

Data analyses applied to electronic nose involve mathematical procedures

that make no assumptions about the frequency distributions of the variables being

assessed. Using classical nonparametric techniques, they correlate each tested

sample to a vector in multidimensional space [50]. By presenting many different

chemicals to the sensor array, a database of signatures is built up. This database of

labeled signatures is used to train the pattern recognition system. The goal of this

training process is to configure the recognition system to produce unique

classifications of each chemical so that an automated identification can be

implemented [40].

To determine the classification of the samples, electronic noses use

multivariate statistics such as Principal Component Analysis (PCA) as

unsupervised technique and Discriminant Function Analysis (DFA) as supervised

technique. A formal method of treating samples is unsupervised pattern

recognition which is aimed at detecting similarities. Supervised pattern

recognition requires a training set of known groupings to be able in advance, and

tries to answer a precise question as to the class of an unknown sample [51]. Fig.

2.2 shows a summary of the available methods for the analysis of e-nose data.

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Fig. 2.2 Some of the available methods of analysis for data from sensor arrays [52]

2.3.1 Principal Component Analysis (PCA)

In PCA, each main component is characterized by two pieces of

information, the scores and the loadings. The numbers of original

variables are reduced to a number of significant principal components

after the PCA, making the data interpretation more easily. PCA is a

Neural Network

Statistical

Classification

Supervised Unsupervised

Linear Discriminant Analysis

Principal Component Analysis

Quantification

Supervised

Partial Least Squares

Cluster AnalysisData Processing

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method where there is no information regarding the sample’s classification

is given, and is based solely on the variance of the data [53].

In general, it is best to use as few variables as possible to develop a

model, as this would result in higher data point to variable ratios and result

in a simpler, more easily understood model [53].

2.3.2 Discriminant Function Analysis (DFA)

DFA can be used to separate classes of objects or assign new

objects to appropriated classes. Discriminant functions are calculated with

the objective of maximizing the distance between classes relative to the

variation within classes [52]. Most traditional approaches to classification

in science are called discriminant analysis. The majority of statistically-

based software refers to DFA by various names such as linear (Fisher)

discriminant analysis and canonical variates analysis. It uses a priori

classifications to maximize the emphasis of those variables that generate

the greatest difference between the specified classes [53].

2.3.3 Cluster analysis (CA)

PCA and DFA are used primarily to determine general

relationships between data. If the operation needs to determine which

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objects are most similar to one another and needs to group them

accordingly, cluster analysis is performed [52]. It can also be executed

using the Mahalanobis distances to create a dendogram where the sample

are clustered in groups. However, this method cannot easily be applied

where the number of measurements or variables exceeds the number of

objects, because the variance-covariance matrix would not have an inverse

[51, 53].

2.3.4 Partial Least Square (PLS)

PLS is a regression procedure that was originally reported by Wold

in the mid 1960’s. It is often applied to the analysis of gas mixture

analysis because it accept collinear data, separates out sample noise and

makes meaningful linear combinations in the dependent concentration

matrix [28].

2.4 Applications of Electronic Nose

A series of electronic noses have been produced commercially during the

last decades. Relevant literature shows that the application of electronic noses can

be categorized as follows: (1) food quality assessment (2) environmental and

pollution monitoring (3) forensics and security (4) health safety and medical

applications.

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2.4.1 Food Quality Assessment

Most of the reported literatures on applications of electronic nose

are for food quality assessment, which includes fruits, drinks and

beverages, oils, grains, fishes, etc. Some of the applications in this area

are shown in table 2.4.

Table 2.4 Application of electronic nose in food quality assessment

Analyte Mode of Application Sensor array (No. of sensor)

Ref

Apple (Fuji) characterization QCM (8) [54]bakery products early detection and differentiation

of spoilageCP (14) [55]

Beef Spoilage clasiification Mixed (15) [56]Beer discrimination of aroma

compoundsMOS (6) [57]

cheese test for ripening MOS (6) [58],[59]

citrus (unshiu) characterization MOS (6) [60]coffee test for ripening MOS (6) [61]corn detection of toxigenic strains of

fusarium verticilliodes MOS (6) [62]fish detection of spoilage MOS (4) [63]frying oil determination of quality QCM (6) [64]ham discrimination MOS (16) [65]mandarin test for the ripening MOS (10) [66]milk a) determination of shelf life

b) recognition of spoilage bacteria and yeasts

CP (14) [67][68]

Oranges freeze damage detection QCM (7) [69]pineapple test for ripening SAW (8) [70]RBD palm olein detection of lard adulteration SAW (8) [71]red wines differentiation SAW (8) [72]

16


tea quality standardization MOS (4) [73]Tomato aroma profiling MOS (6) [74]vegetable oil characterization SAW (8) [75]wheat (contaminated)

evaluation MOS (6) [76]

vinegar characterization MOS (17) [77]white wine classification of aromas MOS (4) [78]

2.4.2. Environmental and Pollution Monitoring

The application of electronic nose has also been found promising

for environmental and pollution monitoring. The device is useful for some

analysis that cannot be performed in the laboratory. In this field, the

background is an ever-changing chemical mixture. Below are the

examples of applications of electronic nose in this area.

Table 2.5 Application of electronic nose in environmental and pollution monitoring

Analyte Mode of Application Sensor array (No. of sensor)

Ref

Indoor air quality monitoring MOS (6) [80]Municipal

waste treatment

landfill gas quantification

SnO2 (16) [81]

Composting plants

monitoring of odours MOS (6) [82]

Waste treatment

plants

environmental odours assessment

MOS (10) [83]

Potable water

early detection and differentiation between

Streptomyces

CP (14) [84]

2.4.3. Forensics and Security

The potential of electronic nose in forensics and security were even

found promising. Below are some of the examples.

Table 2.6 Application of electronic nose in forensics and security

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Analyte Mode of Application Sensor array (No. of sensor) RefExplosives nitro aromatics

vapour detectionSAW (6) [85]

Fire detection CP (14) [86]Human breath

detection of alcohol MOSFET [79]

2.4.4 Health Safety and Medical Applications

Electronic nose could be applied in biotechnology for health safety

by monitoring the growth of living organisms that may cause spoilage and

contamination. The device had even been used in the field of medicine for

health diagnoses, by subjecting human breath for the analysis.

Table 2.7 Application of electronic nose in health safety and medicine

Analyte Mode of Application Sensor array (No.) RefCoated tablets evaluation of unpleasant

odoursMOS (6) [87]

Human breath detection of uremia QMB (6) [88]Human breath detection of lung cancer QMB (8) [89]

Helicobacter pylori and other gastroesophageal

bacteria

discrimination CP (14) [90]

Human sputum detection of Mytobacterium tuberculosis

CP (14) [91]

Urinary tract infections diagnosis CP (14) [92]Kidney patients monitoring of haemodialysis CP (14) [93]

2.4.5 Application of electronic nose in medicinal plants

Electronic nose has found valuable in quality control of medicinal

plants. The quality control involved authentication or identification of

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plants, and discrimination of different species or varieties, even of

different origins and years of crop. Table 2.8 shows some of the related

studies on the quality control of medicinal plants based on electronic nose.

Table 2.8 Application of electronic nose in the quality control of medicinal plants.

Raw Material/Sample

Sensor Array

Description Result Ref

Twelve E. longifolia Jack samples that were extracted using water and methanol and were either freeze or spray dried.

Eight QCM Electronic nose was compared with Gas chromatography-Mass spectrometry(GC-MS)

Electronic nose offers a simpler approach than GC-MS, and provides key information of the samples analyzed.

[31]

Valerians species (valeriana officinalis and valeriana walichii) with different origins and years of crop.

MOSES II Electronic nose was compared with Thin-layer Chromatography (TLC) and High Performance Thin-layer Chromatography (HPTLC)

The electronic nose has proved to be a fast tool to discriminate not only both species, but to separate specimens from different origin or crop belonging to the same Valerian variety.

[8]

Valerian species(valeriana carnosa sm. And valeriana clarionifolia Phil.) with different origins and years of crop.

(1) Eight QCM

(2) Eight SnO2

(1) Electronic nose and Thin-layer chromatography were compared.

(2) Both sensor sets together (QCM + SnO2) and single performance were compared

The electronic nose has proved to be a fast and convenient tool to separate different species of valerians or valerians from the same specie but grown in different regions of the same zone. It was observed that using QCM alone gave a better separation.

[32]

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2.5 Quartz Crystal Microbalance

Quartz crystal microbalance (QCM) is a type of piezoelectric sensor also

known as bulk acoustic wave (BAW). It is the best-known, oldest, simplest

acoustic wave device. The most commonly used bulk acoustic wave devices are

the thickness shear mode (TSM) resonator and the shear-horizontal acoustic plate

(SH-APM) sensor [94]. The TSM features simplicity of manufacture, ability to

withstand harsh environments, temperature stability, and good sensitivity to

change in mass [95]. SH-APM, on the other hand, is more sensitive to change in

mass than TSM, but is less sensitive than surface wave sensor [94].

2.5.1 Design of Quartz Crystal Microbalance

Quartz crystal microbalance (QCM) is a type of piezoelectric

sensor that has a simple design. It is made of slice of single crystal quartz,

with a diameter around 1 cm, and with gold electrodes (see fig. 2.4) [28].

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1.5 cm

Fig. 2.4 Design of Quartz Crystal Microbalance [28]

Due to the piezoelectrical properties of the quartz material, an

alternating voltage can be converted into an acoustic wave [55]. Also,

quartz (SiO2) is found promising because of its potential to select the

temperature dependence of the material by the cut angle and the wave

propagation direction [94].

2.5.2 Principle of Operation

Pizoelectricity was discovered by Pierre and Paul-Jacques Curie in

1880, and the name was given in 1881 by Wilhelm Hankel [96]. The term

refers to the production of electrical charges by the imposition of

mechanical stress. Applying an appropriate a.c. voltage across the two

electrodes, the device is made to oscillate to a corresponding frequency

(see fig. 2.5) [28].

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Fig 2.5 Operation of Quartz Crystal Microbalance [55]

The change in resonant frequency (Δf) is related to the change in the mass

loading, described by the Saubrey equation below. The increase in Δf corresponds

to the decrease in crystal thickness, and therefore a decrease in mass [28].

2.5.3 Applications

Quartz crystal microbalance has found a wide range of applications

in food, environmental, and clinical analysis since its discovery. Table 2.4

shows some of its applications.

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Table 2.4 Some applications of quartz crystal microbalance

Mode of Application

Analyte (Mode of Action) Ref

Chemical sensor a. formaldehyde (determination)

b. environmental pollutants (determination)

c. sulfur dioxide (determination)

d. cyanide (determination)

e. chlorinated and aromatic hydrocarbon (detection)

f. carbon monoxide (determination)

g. potassium (quantification)

h. organic compounds (detection in liquid environment)

i. boron (determination)

j. organic vapors (recognition)

[97]

[98]

[99], [100]

[101]

[102]

[103]

[104]

[105]

[106]

[107], [108],

[109]

Immunosensor a. viruses and bacteria (detection)

b. HIV (detection)

c. cortisol (detection)

d. cocaine (detection)

e. Chlamydia trachomatis (analysis of urine specimens)

f. phytohormone b-Indole Acetic acid (detection)

g. genetically modified organisms (detection)

[110]

[111]

[112]

[113]

[114]

[115]

[116]

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h. Sputum (detection of SARS associated coronavirus) [117]

Mode of Application

Analyte (Mode of Action) Ref

DNA sensor a. DNA (detection of point mutation and insertion

mutation)

b. DNA (electrochemical detection of hybridization)

c. DNA oligonucleotide (detection of hybridization)

[118]

[119]

[120]

Thermal sensor a. polymer and fullerene films (measurement of mass

change and heat flow)

b. polymer (determination of polymer-solvent

thermodynamics)

[121]

[122]

CHAPTER 3

Research Methodology

This chapter discusses the development and characteristics of an array of

quartz crystal microbalance sensors for the discrimination of mentha varieties.

This will include the optimization of sensor preparation, measurement, and

discrimination-potential of the sensors’ array.

The discussions comprise four major parts: (1) reagents and materials

preparation (2) sensor system preparation (3) gas sampling and sensing

procedure and (4) data analysis.

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3.1 Reagents and Materials Preparation

Crude leaves of mentha plants (M. cordifolia and M. arvensis) will be

collected from cultivated and wild source. Some will be purchased from certified

and uncertified distributors. For the powdered sample, some of the crude leaves

will be air-dried inside the laboratory. The crude and air dried leaves will undergo

milling using laboratory grinder. Equal weights of each specimen will be placed

in 20mL vials and be covered with Teflon septa and Aluminum strap, so as to

concentrate the volatiles in the headspace of the sample.

Quartz crystal microbalance, with the following specifications will be used

as sensor: AT-cut quartz crystal, 9MHz, Ag/Au electrode area(19.6mm2), SEIKO

EG & G. Sensing films that will be used for quartz crystal microbalance such as

trioctyl methylammonium chloride (TOMA), polyethylene glycol 1000 (PEG-

1000), polypropylene glycol 1200 (PPG-1200), polyaniline (PA), polypyrrole

(PP), polythiopene (PT) and ethyl cellulose (EC) will be purified before using.

Distilled water will be used throughout for preparation of aqueous solution.

3.2 Sensor System Preparation

This part of the methodology discusses the preparation of sensor system.

Prior to the setting-up of the instrumentation is the sensor preparation.

3.2.1 Sensor preparation

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The array sensor will consist of 6 AT-cut quartz crystals with gold

electrodes on both sides. Prior to the coating of each crystal’s surface,

resonance frequency of the bare crystal will be measured for mass

deposition computations. Six different sensing materials will be used for

coating, consisting of polar, non-polar and amphiphilic materials: trioctyl

methylammonium chloride (TOMA), polyethylene glycol 1000 (PEG-

1000), polypropylene glycol 1200 (PPG-1200), polyaniline (PA),

polypyrrole (PP), polythiopene (PT) and ethyl cellulose (EC). Both sides

of the quartz crystal will be coated with sensing material using drop-

coating method. This coating technique will be done by applying a drop of

sensing material on top of the sensor. After drying, the quality of the

obtained film will be checked by optical microscopy and measurements on

the network analyzer.

3.2.2 Instrumentation

A schematic diagram of the electronic nose system is shown in

figure 3.1. To start the system, nitrogen gas will be used as the carrier gas

of the headspace volatile compounds of the sample from the sample

chamber to the sensor chamber, with controlled flow rate. The sample

chamber will contain the sample and the sensor chamber will contain the

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array of quartz crystal microbalance sensors, at thermostated conditions.

The sensors will be fastened in the cap, with the crystals exposed inside

the chamber at vertical position. An oscillator, supplied with 5V from a

portable DC adaptor, will be used to enable oscillation of quartz crystals.

A universal frequency counter (Thurlby Thandar) will be utilized to

measure frequency shift up to 9 digits. The response of the sensor will be

displayed in the computer. See fig 3.1 for the instrumentation set-up.

thermometer

sensor array

Figure 3.1 Schematic diagram of the electronic nose system

3.3 Gas sampling and sensing procedure

nitrogen

Sample chamber Quartz chamber

oscillator

Frequency reading

computer

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This section discusses the gas sampling and sensing procedure for volatile

organic compounds and real samples.

3.3.1 Volatile Organic Compounds

The responses of sensors’ array to different volatile organic

compounds (VOCs) such as alcohols, phenols, carboxylic acids, esters,

and ethers will be evaluated prior to the real samples. The ability of the

sensors to respond with those VOCs will be the basis of their potentials to

indicate the VOCs present in the real samples.

Ten mL of each VOCs will be placed in the sample chamber, one

at a time, at room temperature. The vapors will then be carried by the

nitrogen gas to the sensor chamber. The array of sensors will respond to

vapors, which will then create a shift in frequency. The analysis for each

VOCs will be done in replicates.

3.3.2 Real samples

Samples contained in 20mL vials will be placed in the sample

chamber, one at a time, and will be thermostated at 700C for one hour. The

headspace vapors from the sample will be carried to the sensor chamber by a

nitrogen gas. A static headspace sampling method will be used. The

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electronic nose will give an overall response to the headspace vapors which

are reversibly adsorbed when in contact with sensing layers. This temporary

and reversible mass change will lead the vibrational frequency of quartz

resonator. Analysis for each sample will be done in replicates.

3.4 Data Analysis

Principal component analysis will be carried out on the sensor array data

in order to find out the discrimination ability of the array sensor. The data will be

preprocessed before analysis. The preprocessing will include standardization. This

will be done by expressing as deviations from the means in units of standard

deviation: Z = (x – x)/ σ where z is the standardized value, x the real value, x the

mean, and σ is the standard deviation. The radar plot will be used for plotting the

responses of each fabricated sensors.

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REFERENCES

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