Convenient and accurate method for the identification of Chinese teas by an electronic nose
Main Article Content
Keywords
Chinese teas, electronic nose, identification, random forest
Abstract
A convenient, accurate, and effective approach for the identification of Chinese teas and their production area has been developed. For this, Chinese tea samples from different regions were collected and their odours were analysed by an electronic nose (E-nose). An unambiguous identification of the Chinese teas could not be achieved by means of traditional principal component analysis or linear discriminant analysis methods. Thus, multiple logistic regression (MLR), support vector machines (SVM), and random forests (RF) were employed as alternative to build identification models. The experimental results show that the method aiming within scope based on the RF performs very well, with prediction accuracies and computation times being superior to the two others (MLR and SVM). The results were demonstrated that E-nose could be used in the classification of Chinese teas, when an optimal pattern recognition algorithm is selected. The present study provides a critical outlook on the developments of Chinese teas identification, authenticity control and against adulteration in the Chinese circulation market.
References
Banerjee, R., Tudu, B., Shaw, L., Jana, A., Bhattacharyya, N. and Bandyopadhyay, R., 2012. Instrumental testing of tea by combining the responses of electronic nose and tongue. Journal of Food Engineering 110: 356-363.
Barrett, P., 2009. A review of ‘statistical models: theory and practice’. Structural Equation Modeling – A Multidisciplinary Journal 16: 391-395.
Brudzewski, K., Osowski, S. and Golembiecka, A., 2012. Differential electronic nose and support vector machine for fast recognition of tobacco. Expert Systems with Applications 39: 9886-9891.
Brudzewski, K., Osowski, S. and Pawlowski, W., 2012. Metal oxide sensor arrays for detection of explosives at sub-parts-per million concentration levels by the differential electronic nose. Sensors and Actuators B Chemical 161: 528-533.
Castilla, E., Martín, N. and Pardo, L., 2017. Minimum phi-divergence estimators for multinomial logistic regression with complex sample design. Asta Advances in Statistical Analysis 9: 1-31.
Cesare, F.D., Pantalei, S., Zampetti, E. and Macagnano, A., 2008. Electronic nose and SPME techniques to monitor phenanthrene biodegradation in soil. Sensors and Actuators B Chemical 131: 63-70.
Chapman, E.A., Thomas, P.S., Stone, E., Lewis, C. and Yates, D.H., 2012. A breath test for malignant mesothelioma using an electronic nose. European Respiratory Journal 40: 448-454.
Chen, Q., Liu, A., Zhao, J. and Ouyang, Q., 2013. Classification of tea category using a portable electronic nose based on an odor imaging sensor array. Journal of Pharmaceutical & Biomedical Analysis 84: 77-83.
Ciosek, P. and Wróblewski, W., 2006. The analysis of sensor array data with various pattern recognition techniques. Sensors and Actuators B Chemical 114: 85-93.
Cortes, C. and Vapnik, V., 1995. Support vector network. Machine Learning 20: 273-297.
Dai, Y., Zhi, R., Zhao, L., Gao, H., Shi, B. and Wang, H., 2015. Longjing tea quality classification by fusion of features collected from E-nose. Chemometrics & Intelligent Laboratory Systems 144: 63-70.
Fluky, B., 2012. A user’s guide to principal components. Siam Review 35: 148-149.
Gobbi, E., Falasconi, M., Zambotti, G., Sberveglieri, V., Pulvirenti, A. and Sberveglieri, G., 2015. Rapid diagnosis of Enterobacteriaceaein vegetable soups by a metal oxide sensor based electronic nose. Sensors and Actuators B Chemical 207: 1104-1113.
Green, G.C., Chan, A.D.C., Dan, H. and Lin, M., 2011. Using a metal oxide sensor (MOS)-based electronic nose for discrimination of bacteria based on individual colonies in suspension. Sensors and Actuators B Chemical 152: 21-28.
Hartyáni, P., Dalmadi, I. and Knorr, D., 2013. Electronic nose investigation of Alicyclobacillus acidoterrestris, inoculated apple and orange juice treated by high hydrostatic pressure. Food Control 32: 262-269.
Jia, P., Huang, T., Li, W., Duan, S., Jia, Y. and Wang, L., 2016. A novel pre-processing technique for original feature matrix of electronic nose based on supervised locality preserving projections. Sensors 16: 1019-1029.
Jia, P., Tian, F., He, Q., Fan, S., Liu, J. and Yang, S.X., 2014. Feature extraction of wound infection data for electronic nose based on a novel weighted KPCA. Sensors and Actuators B Chemical 201: 555-566.
Kuswanto, H., Salamah, M. and Fachruddin, M.I., 2017. Random forest classification and support vector machine for detecting epilepsyusing electroencephalograph records. American Journal of Applied Sciences 14: 533-539.
Li, Q., Gu, Y. and Jia, J., 2017a. Classification of multiple Chinese liquors by means of a QCM-based E-Nose and MDS-SVM classifier. Sensors 17: 272-286.
Li, Q., Gu, Y. and Wang, N.F., 2017b. Application of random forest classifier by means of a QCM-based E-nose in the identification of Chinese liquor flavors. IEEE Sensors Journal 17: 1788-1794.
Liao, S., Kao, Y.H. and Hiipakka, R.A., 2001. Green tea: biochemical and biological basis for health benefits. Vitamins & Hormones 62: 1-94.
Ling, W., Gui, Y.H. and Zhang, S.P., 2007. Research on explosives detection by electronic nose. Chinese Journal of Sensors & Actuators 20: 42-45.
Liu, Q., Wang, H., Li, H., Zhang, J., Zhuang, S. and Zhang, F., 2013. Impedance sensing and molecular modeling of an olfactory biosensor based on chemosensory proteins of honeybee. Biosensors & Bioelectronics 40: 174-179.
Loutfi, A., Coradeschi, S., Mani, G.K., Shankar, P. and Rayappan, J.B.B., 2015. Electronic noses for food quality: a review. Journal of Food Engineering 144: 103-111.
Norman, A., Stam, F., Morrissey, A., Hirschfelder, M. and Enderlein, D., 2003. Packaging effects of a novel explosion-proof gas sensor. Sensors and Actuators B Chemical 95: 287-290.
Pardo, M. and Sberveglieri, G., 2005. Classification of electronic nose data with support vector machines. Sensors and Actuators B Chemical 107: 730-737.
Qiu, S. and Wang, J., 2017. The prediction of food additives in the fruit juice based on electronic nose with chemometrics. Food Chemistry 230: 208-214.
Romain, A.C. and Nicolas, J., 2009. Long term stability of metal oxide-based gas sensors for E-nose environmental applications: an overview. Sensors and Actuators B Chemical 146: 502-506.
Roy, R.B., Bandyopadhyay, R., Tudu, B. and Bhattacharyya, N., 2016. Tea and the use of the electronic nose. Electronic noses and tongues in food science. Elsevier Inc., New York, NY, USA, 125 pp.
Roy, R.B., Bandyopadhyay, R., Tudu, B. and Bhattacharyya, N., 2018. Electronic noses in food analysis. Electronic nose technologies and advances in machine olfaction. IGI Global, Hershey, PA, USA, 132 pp.
Sereshti, H., Samadi, S. and Jalali-Heravi, M., 2013. Determination of volatile components of green, black, oolong and white tea by optimized ultrasound-assisted extraction-dispersive liquid-liquid microextraction coupled with gas chromatography. Journal of Chromatography A 1280: 1-8.
Sohn, J.H., Hudson, N., Gallagher, E., Dunlop, M., Zeller, L. and Atzeni, M., 2008. Implementation of an electronic nose for continuous odour monitoring in a poultry shed. Sensors and Actuators B Chemical 133: 60-69.
Wang, L.F., Jooyeon, L., Jinoh, C., Joohyun, B., Sung, S. and Seungkook, P., 2008. Discrimination of teas with different degrees of fermentation by SPME-GC analysis of the characteristic volatile flavour compounds. Food Chemistry 109: 196-206.
Wang, W.C., Chau, K.W., Cheng, C.T. and Qiu, L., 2009. A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series. Journal of Hydrology 374: 294-306.
Wei, Z., Wang, J. and Jin, W., 2013. Evaluation of varieties of set yogurts and their physical properties using a voltammetric electronic tongue based on various potential waveforms. Sensors and Actuators B Chemical 177: 684-694.
Young, R.C., Buttner, W.J., Linnell, B.R. and Ramesham, R., 2003. Electronic nose for space program applications. Sensors and Actuators B Chemical 93: 7-16.
Yun, Z., Guo, Z.H. and Meng, F.L., 2007. Dynamic detection and recognition system based on the segmental average differentiation. Chinese Journal of Sensors & Actuators 8: 1706-1711.
Barrett, P., 2009. A review of ‘statistical models: theory and practice’. Structural Equation Modeling – A Multidisciplinary Journal 16: 391-395.
Brudzewski, K., Osowski, S. and Golembiecka, A., 2012. Differential electronic nose and support vector machine for fast recognition of tobacco. Expert Systems with Applications 39: 9886-9891.
Brudzewski, K., Osowski, S. and Pawlowski, W., 2012. Metal oxide sensor arrays for detection of explosives at sub-parts-per million concentration levels by the differential electronic nose. Sensors and Actuators B Chemical 161: 528-533.
Castilla, E., Martín, N. and Pardo, L., 2017. Minimum phi-divergence estimators for multinomial logistic regression with complex sample design. Asta Advances in Statistical Analysis 9: 1-31.
Cesare, F.D., Pantalei, S., Zampetti, E. and Macagnano, A., 2008. Electronic nose and SPME techniques to monitor phenanthrene biodegradation in soil. Sensors and Actuators B Chemical 131: 63-70.
Chapman, E.A., Thomas, P.S., Stone, E., Lewis, C. and Yates, D.H., 2012. A breath test for malignant mesothelioma using an electronic nose. European Respiratory Journal 40: 448-454.
Chen, Q., Liu, A., Zhao, J. and Ouyang, Q., 2013. Classification of tea category using a portable electronic nose based on an odor imaging sensor array. Journal of Pharmaceutical & Biomedical Analysis 84: 77-83.
Ciosek, P. and Wróblewski, W., 2006. The analysis of sensor array data with various pattern recognition techniques. Sensors and Actuators B Chemical 114: 85-93.
Cortes, C. and Vapnik, V., 1995. Support vector network. Machine Learning 20: 273-297.
Dai, Y., Zhi, R., Zhao, L., Gao, H., Shi, B. and Wang, H., 2015. Longjing tea quality classification by fusion of features collected from E-nose. Chemometrics & Intelligent Laboratory Systems 144: 63-70.
Fluky, B., 2012. A user’s guide to principal components. Siam Review 35: 148-149.
Gobbi, E., Falasconi, M., Zambotti, G., Sberveglieri, V., Pulvirenti, A. and Sberveglieri, G., 2015. Rapid diagnosis of Enterobacteriaceaein vegetable soups by a metal oxide sensor based electronic nose. Sensors and Actuators B Chemical 207: 1104-1113.
Green, G.C., Chan, A.D.C., Dan, H. and Lin, M., 2011. Using a metal oxide sensor (MOS)-based electronic nose for discrimination of bacteria based on individual colonies in suspension. Sensors and Actuators B Chemical 152: 21-28.
Hartyáni, P., Dalmadi, I. and Knorr, D., 2013. Electronic nose investigation of Alicyclobacillus acidoterrestris, inoculated apple and orange juice treated by high hydrostatic pressure. Food Control 32: 262-269.
Jia, P., Huang, T., Li, W., Duan, S., Jia, Y. and Wang, L., 2016. A novel pre-processing technique for original feature matrix of electronic nose based on supervised locality preserving projections. Sensors 16: 1019-1029.
Jia, P., Tian, F., He, Q., Fan, S., Liu, J. and Yang, S.X., 2014. Feature extraction of wound infection data for electronic nose based on a novel weighted KPCA. Sensors and Actuators B Chemical 201: 555-566.
Kuswanto, H., Salamah, M. and Fachruddin, M.I., 2017. Random forest classification and support vector machine for detecting epilepsyusing electroencephalograph records. American Journal of Applied Sciences 14: 533-539.
Li, Q., Gu, Y. and Jia, J., 2017a. Classification of multiple Chinese liquors by means of a QCM-based E-Nose and MDS-SVM classifier. Sensors 17: 272-286.
Li, Q., Gu, Y. and Wang, N.F., 2017b. Application of random forest classifier by means of a QCM-based E-nose in the identification of Chinese liquor flavors. IEEE Sensors Journal 17: 1788-1794.
Liao, S., Kao, Y.H. and Hiipakka, R.A., 2001. Green tea: biochemical and biological basis for health benefits. Vitamins & Hormones 62: 1-94.
Ling, W., Gui, Y.H. and Zhang, S.P., 2007. Research on explosives detection by electronic nose. Chinese Journal of Sensors & Actuators 20: 42-45.
Liu, Q., Wang, H., Li, H., Zhang, J., Zhuang, S. and Zhang, F., 2013. Impedance sensing and molecular modeling of an olfactory biosensor based on chemosensory proteins of honeybee. Biosensors & Bioelectronics 40: 174-179.
Loutfi, A., Coradeschi, S., Mani, G.K., Shankar, P. and Rayappan, J.B.B., 2015. Electronic noses for food quality: a review. Journal of Food Engineering 144: 103-111.
Norman, A., Stam, F., Morrissey, A., Hirschfelder, M. and Enderlein, D., 2003. Packaging effects of a novel explosion-proof gas sensor. Sensors and Actuators B Chemical 95: 287-290.
Pardo, M. and Sberveglieri, G., 2005. Classification of electronic nose data with support vector machines. Sensors and Actuators B Chemical 107: 730-737.
Qiu, S. and Wang, J., 2017. The prediction of food additives in the fruit juice based on electronic nose with chemometrics. Food Chemistry 230: 208-214.
Romain, A.C. and Nicolas, J., 2009. Long term stability of metal oxide-based gas sensors for E-nose environmental applications: an overview. Sensors and Actuators B Chemical 146: 502-506.
Roy, R.B., Bandyopadhyay, R., Tudu, B. and Bhattacharyya, N., 2016. Tea and the use of the electronic nose. Electronic noses and tongues in food science. Elsevier Inc., New York, NY, USA, 125 pp.
Roy, R.B., Bandyopadhyay, R., Tudu, B. and Bhattacharyya, N., 2018. Electronic noses in food analysis. Electronic nose technologies and advances in machine olfaction. IGI Global, Hershey, PA, USA, 132 pp.
Sereshti, H., Samadi, S. and Jalali-Heravi, M., 2013. Determination of volatile components of green, black, oolong and white tea by optimized ultrasound-assisted extraction-dispersive liquid-liquid microextraction coupled with gas chromatography. Journal of Chromatography A 1280: 1-8.
Sohn, J.H., Hudson, N., Gallagher, E., Dunlop, M., Zeller, L. and Atzeni, M., 2008. Implementation of an electronic nose for continuous odour monitoring in a poultry shed. Sensors and Actuators B Chemical 133: 60-69.
Wang, L.F., Jooyeon, L., Jinoh, C., Joohyun, B., Sung, S. and Seungkook, P., 2008. Discrimination of teas with different degrees of fermentation by SPME-GC analysis of the characteristic volatile flavour compounds. Food Chemistry 109: 196-206.
Wang, W.C., Chau, K.W., Cheng, C.T. and Qiu, L., 2009. A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series. Journal of Hydrology 374: 294-306.
Wei, Z., Wang, J. and Jin, W., 2013. Evaluation of varieties of set yogurts and their physical properties using a voltammetric electronic tongue based on various potential waveforms. Sensors and Actuators B Chemical 177: 684-694.
Young, R.C., Buttner, W.J., Linnell, B.R. and Ramesham, R., 2003. Electronic nose for space program applications. Sensors and Actuators B Chemical 93: 7-16.
Yun, Z., Guo, Z.H. and Meng, F.L., 2007. Dynamic detection and recognition system based on the segmental average differentiation. Chinese Journal of Sensors & Actuators 8: 1706-1711.
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Published
Dec 21, 2018
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