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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).
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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]
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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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