[ieee 2013 ieee conference on wireless sensor (icwise) - kuching, sarawak, malaysia...

Electronic Flavour Assessment Techniques for Orthosiphon stamineus Tea from Different

Manufacturers

N.Z.I. Zakariaa, M.J. Masnana, A. Y. M. Shakaffa,c

a Centre of Excellence for Advanced Sensor Technology (CEASTech), Universiti Malaysia Perlis

02600 Jejawi, Perlis, Malaysia [email protected]

A. Zakariaa,b,c, L. M. Kamarudina,c, N. Yusuf a, A.H.A. Aziza

b School of Mechatronic Engineering,Universiti Malaysia Perlis (UniMAP), Malaysia

c Member, IEEE

Abstract— The development of electronic nose (e-nose) and electronic tongue (e-tongue) to recognize simple or complex solutions has been a great success to overcome the drawbacks of conventional analytical instrument and human organoleptic profiling panels. The fusion of these sensors is believed to be able to assess flavours. To grab the advantage of the promising achievements and its broad prospect, this research paper focuses on the discrimination of herbal tea flavour. Multiple analyses based on low level data fusion (LLDF) and intermediate level data fusion (ILDF) in assessing herbal drink from different manufacturers were demonstrated in this research. Classification using Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), Probabilistic Neural Network (PNN) and k-Nearest Neighbour (KNN) analysis were illustrated. Experimental results show better classification was achieved for fusion data using the KNN and LDA with zero error rate. The findings demonstrate that the combination of e-nose and e-tongue with either LDA or KNN as the classification methods are suitable for discrimination of herbal tea flavour.

Keywords-LDA; PNN; SVM; KNN; e-nose; e-tongue; LLDF; ILDF

I. INTRODUCTION

Nowadays, herbal based products such as beverages and liquid supplements can be easily purchased everywhere. More and more herbal based companies are eager to promote such “healthy” products with the hope that consumers may benefit from the nutritional values provided in the ingredients. To some extent, some companies may use mixture of herbal in order to boost the product competitive values to tap the promising domestic and global demand.

Although Malaysia is one of the richest tropical countries

with an estimated 2,000 plant species that have medical or herbal values, the herbal industry is still at its infancy stage. Related studies to prove the benefit of herbal nutritional values including Orthosiphon stamineus [1] may have been carried out extensively, but research on herbal drinks flavour is still lacking. Generally, flavour is very important in food and beverages industries as it is one of the benchmarks of product quality [2]. Although determining acceptable flavour for herbal drinks is a big challenge, to sustain the original flavour

is more important to convince consumer on the authenticity of herbals added in the products.

Original flavour of Orthosiphon stamineus varies due to

different processing methods, harvesting times, herbal tree varieties, and regions produce [3]. Although it may comes from the same regions, processing methods, harvesting times, or even the same branch of herbal tree; however different storage and brewing methods can affect the flavour [4]. The varieties of flavours are usually assessed by human panels or gas chromatography-mass spectroscopy (GC-MS). These approaches are normally time consuming, expensive and can only be conducted by an expert.

With the advanced of current technologies and innovation

to replace the human panel, the abovementioned conventional approaches are now being complemented by the electronic nose (e-nose) and electronic tongue (e-tongue) [5][6]. These artificial sensors are inspired by the human flavour detection mechanism that mimics the human nose and tongue [7]. These sensors are further enhanced by its capability to classify simple and complex flavours by using pattern recognition system derived from an array sensors. Herbal flavour assessment can be achieved through different classification techniques including linear discriminant analysis (LDA), k-Nearest Neighbour (KNN), Support Vector Machine (SVM) and Probabilistic Neural Network (PNN) [8].

II. MATERIALS AND METHODS

A. Sample For the purpose of this experiment, seven samples of

herbal tea were taken from four different brands of commercial Orthosiphon stamineus (labelled as according to manufacturer names such as HPA, RH, PH and POL), seven samples from fresh coarsely ground Orthosiphon stamineus dried leaves obtained from our home-grown plants (UniMAP‘s Sungai Chuchuh plantation) and another seven samples from two different types of commercial Camelia sinesis, (green and black tea from the same brands (BOH)). In order to ensure no bias on the storage effect, all herbal leaves

2013 IEEE Conference on Wireless Sensors (ICWiSe2013), December 2 - 4, 2013, Kuching, Sarawak

978-1-4799-1576-7/13/$31.00 ©2013 IEEE 134

Figure 1. Setup for e-nose data acquisition

Figure 2. Setup for e-tongue data acquisition

are removed from the tea bag and stored in a stainless steel canister. For preparation purposes, the same brewing method applies for all the samples.

B. Sample Preparation Herbal drink equivalent to 2 g of each brand was infused with 200 ml boiled distilled water for 5 minutes (min) and filtered using special sieve for tea preparation. The filtrate is immediately cooled to about 25 oC in tap water. Then 5 ml of the tea infusion was replaced into 20 ml vials and the rest (195 ml) was replaced into filtering flask and covered with silicon stopper as illustrated in Figure 1. The chamber is covered with parafilm to avoid any volatile compound from exit the flask. To stabilize the concentration of tea aroma in the headspace of the filtering flask, the flask with tea infusion was kept for 10 min at 35 oC using hot plate in a dark (wrap the flask with aluminium foil) and then further analysed by the e-nose. The same prepared sample will be used for e-tongue as shown in Figure 2.

C. E-nose and Measuring Condition

Experiments were performed using PEN3, WMA (Win Muster Airsense) Analytics Inc., Germany. PEN3 comprises of a sampling tool, a chamber consists of an array of sensors, and pattern recognition software (Win Muster v.1.6.2.14) for data logging. The sensor array was make up of 10 MOSs. The sensor response was indicated as the ratio of conductance (G/G0).

The headspace gas was pumped into the sensor chamber

with a constant rate of 400 ml/min via a Teflon-tubing connected to a needle during the measurements process. When the gas accumulated in the headspace of vials and was pumped into the sensor chamber, the ratio of conductance of each sensor changed.

The measurement procedure was controlled by a computer program known as Win Muster v.1.6.2.14. The measurement phase lasted for 30 second (s), enough for the sensors to reach stable values. The interval for data collection is 0.2 s. A computer recorded the responses of the e-nose for every 0.2 s. When the measurement was completed, the acquired data was properly stored for later use. Then, the chamber is cleaned using activated carbon for 85 s. The temperature of the filtering flask was kept for 35 ± 1 oC using hotplate. The filtering flask is covered with aluminium foil to avoid temperature gradient effect.

D. E-tongue and Measuring Condition

Experiments were performed using chalcogenide-based potentiometric sensors with eight distinct ion-selective sensors from SENSOR SYSTEM, LLC [8]. This potentiometric sensor is designed to be partially selective. The e-tongue sensors were developed by arranging eight of the potentiometric sensor around a reference, pH and ORP probe. Each sensor output was connected to two analogue input of a data acquisition board (NI USB-6008) from National Instruments


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and the reference probe is connected to the common ground of the board.

The sensor array was dipped for two min in distilled water (stirred at 800 rpm) at the beginning of the experiment. After each sampling, the sensor array was rinsed twice using distilled water (stirred at 400 rpm for two min) again to remove any residues from previous sample sticking on the e-tongue and contaminating the next sample. In each measurement, the sensor array was steeped simultaneously (sensor tip 2 cm below the solution level) and left for 5 min, and the potential readings were recorded for the whole duration.

E. SPME and GC-MS Analysis Setting

Solid-phase microextraction (SPME) needles made by CAR/PDMS (Supelco-57320-U, Bellefonte, PA, USA) was used to extract the headspace of aroma released from the solution. The SPME was used as sample introduction and Mass Spectrometry (MS) as a detector for Gas Chromatography (GC). About 5 ml of herbal tea extraction was used for this purpose.

i. SPME-GC Setting

An SPME fiber (75 lm Carboxen-PDMS; Supelco, Inc., Bellefonte, PA, USA) was exposed to the sample headspace in a 20 ml vial while heated with hotplate for 80 oC for 10 min. The Volatile Flavour Compound (VFC) were desorbed by inserting the SPME fiber into a GC injector (injector temperature 230 oC) in split less mode connected with a fused-silica GC column (Elite 5MS, 30 m, 0.25 mm ID, 0.25 µm film thickness) (Perkin Elmer, Shelton, USA) for 15 min. The initial temperature of the GC was set at 40 oC for 4 min, and then the oven temperature was increased at a rate of 5 oC/min until it reaches 230 oC which remained for another 3 min. The detector temperature was set at 250 oC.

ii. GC-MS Setting

For GC–MS analysis, a GC (Clarus680) coupled with a mass spectrometry (Clarus600T, Perkin Elmer, Shelton, USA) was used. The GC operating conditions (temperature and time) were the same as described above. The mass spectrometer was operated in the electron-ionization (EI) mode at an ionization voltage of 70 eV.

F. Pattern recognition of e-nose and e-tongue data For the data analysis, four types of classification

techniques such as LDA, KNN, PNN and SVM were applied. LDA is a well-known classical statistical technique to find the projection that maximize the ratio of scatter among the data of difference classes to scatter within the data of the same class [9]. While, PNN is a part of radial basis network that is

implemented based on predominant nearest neighbour classifier. The classification factor is highly dependent on the spread of its radial basis function [10]. KNN is the simplest method for deciding the class to which a sample belongs and is a popular nonparametric method. SVM is the most modern method applied to classify gene expression data, which works by separating space into two regions by a straight line or hyper plane in higher dimensions. Further reading of KNN and SVM methods refer to [11].

Before any classification process is being done, data from each modality were fused using two approaches; low level data fusion (LLDF) and intermediate level data fusion (ILDF). In LLDF, raw data from e-nose and e-tongue are combined before further classification process, whereas in ILDF extracted significant features from e-nose and e-tongue are combined before classification process. Details of each fusion level are referred to [12]

III. RESULT

Data analyses were performed using MATLAB R2012. Findings of the analyses are described base on different fusion levels.

A. Low level data fusion Based on the result in Table I, the classification results of

the test set shows that KNN and LDA outperformed PNN and SVM methods. Between KNN and LDA, KNN seems excellent in classifying with zero error for all the three stages. Unlike SVM, eventhough during training it can classify 100% correct, the performance deteriorate during cross validation and testing. In contrast, though during training PNN has recorded some missclassification error of 2.61%, the testing result i.e. 97.16% is better than the SVM result .

TABLE I. CLASSIFICATION PERFORMANCE FOR LLDF

Process\Sample LDA PNN KNN SVM Train 100.00% 97.39% 100.00% 100.00%

Cross validation 99.60% 96.12% 100.00% 99.80% Test 100.00% 97.16% 100.00% 96.85% Error 0.00% 2.84% 0.00% 3.15%

B. Intermediate level data fusion The performances of KNN and LDA for ILDF in

Table II are somewhat similar with the results of LLDF. Generally, all the methods successfully classify with zero missclassification error during the training stage. However, only KNN remain stable in all the training, cross validation and testing stage. The performance of LDA is exactly the same as of LLDF. However, the performance of PNN and SVM are almost similar with only 0.4% error difference. The findings of ILDF are not as good as of LLDF. This is believe due to different nature of data extracted for further classification in ILDF. Another reason is because some information have been discarded when performing features extraction during the process.


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TABLE II. CLASSIFICATION PERFORMANCE FOR ILDF

Process\Sample LDA PNN KNN SVM Train 100.00% 100.00% 100.00% 100.00% Cross

validation 99.60% 97.14% 100.00% 99.80%

Test 100.00% 96.83% 100.00% 96.43% Error 0% 3.17% 0% 3.57%

TABLE III. CLASSIFICATION PERFORMANCE FOR GC-MS OUTPUT USING LDA

C. GC-MS To support the findings of this research, output from

GC-MS were also analysed using LDA. The methodology involves concatenating all concurrently measured individual m/z chromatograms from m/z 30 to 40 for each GC-MS separation into a row vector [13]. From the findings of the testing classification about 72.3% were correctly classified as illustrated in Table III. It can be observed that the performance of classification using LDA base on the GC-MS output is not as good as the result of the fusion method.

CONCLUSION Based on the findings we can conclude that KNN is

the best classification technique for LLDF and ILDF of this experiment. It can be clearly seen from the performance of KNN where the method outperformed the other classification techniques. It may not be too rigid to assume that classification for fusion of e-nose and e-tongue data best meet the non-parametric approach. Based on the classification result of different fusion level with the GC-MS output, it seems that by fusing e-nose and e-tongue better classification result can be obtained. This findings support the application of e-nose and e-tongue in flavour assessment test.

ACKNOWLEDGEMENT Special thanks to the Agrotechnology Unit, Sg. Chuchuh

Campus, Universiti Malaysia Perlis(UniMAP) for providing the samples. Ministry of Higher Education Malaysia (MOHE). Nur Zawatil Isqi Zakaria acknowledges the sponsorship provided by UniMAP and MOHE.

REFERENCES

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Train 93.6% Cross validation 76.5%

Test 72.3% Error 27.7%


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[11] R. Mallika and V. Saravanan, “An SVM based Classification Method for Cancer Data using Minimum Microarray Gene Expressions,” pp. 543–547, 2010.

[12] M. J. Masnan, A. Zakaria, A. Y. Shakaff, N. Idayu, H. Hamid, N. Subari, and J. M. Saleh, “Chapter Number Principal Component Analysis – A Realization of Classification Success in Multi Sensor Data Fusion,” in Principal Component Analysis, 2007, pp. 1–25.

[13] N. E. Watson, M. M. VanWingerden, K. M. Pierce, B. W. Wright, and R. E. Synovec, “Classification of high-speed gas chromatography–mass spectrometry data by principal component analysis coupled with piecewise alignment and feature selection,” Journal of Chromatography A, vol. 1129, no. 1, pp. 111–118, Sep. 2006.


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