Fabrication and development of a machine olfaction system combined with pattern recognition techniques for detecting formalin adulteration in raw milk

Document Type : Research Paper

Author

Abstract

Adulteration in milk and other dairy products not only is a serious threat to human health but also leads to the economic losses in the dairy industry. Utilization of the materials reducing microbial load is a common adulteration. In this study, a machine olfaction (electronic nose) based on 8 metal oxide semiconductor (MOS) sensors was fabricated and developed and its capability to formalin detection in the raw milk was investigated. Feature vector was then extracted from the sensors’ response and used as the inputs to pattern recognition models. Based on the obtained results, Principal Component Analysis (PCA) with two first PCs (PC1 and PC2) could describe 93 % of variance within data. In the sensor array, MQ4, FIS, TGS822, and TGS2620 sensors had the highest loading coefficient values whilst TGS2602 devoted the lowest loading value. Linear Discriminant Analysis (LDA) revealed the classification accuracy as 80.1 %. Support Vector Machine (SVM) with three order multinomial kernel function showed the training and validation accuracy values as 100% and 90.91%, respectively. Also, the full success rate was obtained for overall classification using the artificial neural network.

Keywords

Main Subjects


Anonymous. (2009). Code of Hygiene practice for milk and milk productions, last modified 2009. Codex Alimentarius-CAC/RCP 57-2004.
Bhattacharyya, N., Bandyopadhyay, R., Bhuyan, M., Tudu, B., Ghosh, D. & Jana, A. (2008). Electronic nose for black tea classification and correlation of measurements with “Tea Taster” Marks. IEEE Transactions on Instrumentation and Measurement, 57: 13113-11321.
Botre, B., Gharpure, D., Shaligram, A. & Sadistap, S. (2009). Semiconductor sensor array based electronic nose for milk, rancid milk and yoghurt odors identification. IEEE Transaction on Instrumentation and Measurement, 63: 1482-1491.
Cristianini, N. & Shawe-Taylor, J. (2000). An Introduction to support vector machines and other kernel-based learning methods (1st ed.). Cambridge: Cambridge University Press
Das, M., Sivaramakrishna, M., Biswas ,K,. & Goswami, B. (2015). A low cost instrumentation system to analyze different types of milk adulteration. ISA Transactions, 56, 268-275.
Ding, W., Zhang, Y., Kou, L. &. Jurick. M. (2015). Electronic nose application for the determination of penicillin G in Saanen goat milk with fisher discriminate and multilayer perceptron neural network analyses. Journal of Food Processing and Preservation, 32(6), 927–932.
Fan, R.E., Chen, P.H. & Lin, C.j. (2005). Working set selection using second order information for training support vector machines. Journal of Machine Learning Research,6: 1889–1918.
Foroughirad, A., Mohtasebi, S.S., Ghasemi-Varnamkhasti, M. & omid, M. (2014). Nondestructive evaluation of kiwifruit (cv. Abbot variety) Abbot using electronic nose. Iranian Journal of Biosystems Engineering, 45, 1-9, (In Farsi)
Fuca, N., Pasta, C., Impoco, G., Caccamo, M. & Licitra, G. (2013). Microstructural properties of milk fat globules. International Dairy Journal, 31, 44–50.
Ghasemi-Varnamkhasti, M., Mohtasebi, S.S., Siadat, M., Ahmadi, H.& Razavi, S.H. (2015). From simple classification methods to machine learning for the binary discrimination of beers using electronic nose data. Engineering in Agriculture, Environment and Food, 8, 44-51.
Ghasemi-Varnamkhasti, M., Mohtasebi, S.S., Siadat, M., Lozano, J., Ahmadi, H., Razavi, S.H. & Dicko, A.(2012). Discriminatory power assessment of the sensor array of an electronic nose system for the detection of non alcoholic beer aging. Czech Journal of Food Sciences, 30(3), 236–240.
Ghasemi-Varnamkhasti, M., Mohtasebi, S.S., Siadat, M., Lozano, J., Ahmadi, H., Razavi, S.H. & Dicko, A. (2011a). Aging fingerprint characterization of beer using electronic nose. Sensors and Actuators B: Chemical, 159, 51–59.
Ghasemi-Varnamkhasti, M., Mohtasebi, S.S., Rodriguez-Mendez, M.L., Lozano, J., Razavi, S.H. & Ahmadi, H. (2011b). Potential application of electronic nose technology in brewery. Trends in Food Science and Technology, 22(4), 165–174.
Ghasemi-Varnamkhasti, M. (2011). Design, development and implementation of a metal oxide semiconductor (MOS) based machine olfaction system and bioelectronics tongue to quality change detection of beers coupled with pattern recognition analysis techniques. Ph. D. dissertation, University of Tehran. (In Farsi).
Guney, S. &, Atasoy, A. (2015). Study of fish species discrimination via electronic nose. Computers and Electronics in Agriculture, 119, 83-91.
Gutierrez-Mendez, N., Vallejo-Cordoba, B., Gonzalez-Cordova, A.F. & NevarezMoorillon, G.V. (2008). Evaluation of aroma generation of lactococcus lactis with an electronic nose and sensory analysis. Journal of dairy Science, 91: 49–57.
Haddi, Z., Alami, H., ElBari, N., Tounsi, M., Barhoumi, H., Maaref, A., Jaffrezic-Renault,N. & Bouchikhi, B. (2013). Electronic nose and tongue combination for improved classification of Moroccan virgin olive oil profiles. Food Research International, 54,1488–1498.
Heidarbeigi, K., Mohtasebi, S.S., Foroughirad, A., Ghasemi-Varnamkhasti, M., Rafiee, S. & Rezaei, K.(2015). Detection of adulteration in saffron samples using electronic nose. International Journal of Food Properties, 18:1391–1401.
Kiani, S., Minaei, S. & Ghasemi-Varnamkhasti, M. (2016). portable electronic nose as an expert system for aroma-based classification of saffron. Chemometrics and Intelligent Laboratory Systems, 156, 148-156.
Loutfi, A., Coradeschi, S., Mani, G.K., Shankar, P. & Rayappan, J.B. (2015). Electronic noses for food quality: a review. Journal of Food Engineering, 144, 103–111.
Lozano, J., Santos, J.P. & Horrillo, M.C. (2005). Classification of white wine aromas with an electronic nose. Talanta, 67, 610–616.
Lutter, P., Perroud, M.C, Gimenez, C, & Meyer, L. (2011) Screening and confirmatory methods for the determination of melamine in cow’s milk and milk-based powdered infant formula: Validation and proficiency-tests of ELISA, HPLC-UV, GC-MS and LC-MS/MS. Food Control, 22(6), 903-913.
Mansour, A., Elloly, M. & Ahmed, R. (2012). A Preliminary detection of physical and chemical properties, inhibitory substances and preservatives in raw milk. Internet Journal of Food Safety, 4, 99-103.
Mousavi, T., Salehi, M., Mohammad sadegh, M. & mohammadyar, L.(2011). A study of additives residues in raw milk collected from the area PAKDASHT, Journal of Food Hygiene, 1, 43-47, (In Farsi)
Nieuwoudt, M.K., Holroyd, S.E., McGoverin, C.M. & Williams, D.E. (2016). Raman spectroscopy as an effective screening method for detecting adulteration of milk with small nitrogen-rich molecules and sucrose. Journal of Dairy Science, 99(4), 2520-2536.
Oliveros, C.C., Pavon, J.L.P., Pinto, C.G., Laespada, E.F., Cordero, B.M., & Forina, M. (2002). Electronic nose based on metal oxide semiconductor sensors as a fast alternative for the detection of adulteration of virgin olive oils. Analytica Chimica Acta, 459: 219–228.
Palaniswami, M. & Begg.R. (2006) Computational intelligence for movement sciences:  Neural Networks and Other Emerging Techniques. London: Idea Group Inc
Panigrahi, S., Balasubramanian, S., Gu, H., Logue, C.M. & Marchello, M. (2006). Design and development of a metal oxide based electronic nose for spoilage classification of beef. Sensors and Actuators B: Chemical, 119(1), 2-14.
Pearce, T.C., Schiffman, S.S., Nagle, H.T. & Gardner, J.W. (2003). Handbook of Machine Olfaction: Electronic Nose Technology. Wheinheim: Wiley-VCH Velag GmbH & Co. KGaA
Ren, Q.R., Zhang, H., Guo, H.Y., Jiang, L., Tian, M. & Ren, F.Z. (2014). Detection of cow milk adulteration in yak milk by ELISA. Journal of Dairy Science, 97(10), 6000-6006.
Rock, F., Barsan, N. & Weimar, U. (2010). System for dosing formaldehyde vapor at the ppb level. Measurement Science and Technology, 21, 1-7.
Rutolo, M.F., Iliescu, D., Clarkson, G.P. & Covington, J.A. (2016). Early identification of potato storage disease using an array of metal-oxide based gas sensors. Postharvest Biology and Technology, 116, 50-58.
Sanaeifar, A., Mohtasebi, S.S., Ghasemi-Varnamkhasti, M., Ahmadi, H. & Lozano, J.(2014).Development and application of a new low cost electronic nose for the ripeness monitoring of banana using computational techniques (PCA, LDA, SIMCA, and SVM). Czech Journal of Food Sciences, 32(6), 538–548.
Sanaeifar, A., Mohtasebi, S.S., Ghasemi-Varnamkhasti, M. & Ahmadi, H. (2015a). Application of MOS based electronic nose for the prediction of banana quality properties. Measurement, 82, 150-114.
Sanaeifar, A., Mohtasebi, S.S., Ghasemi-Varnamkhasti, M. & Ahmadi, H. (2015b). Design, development and implementation of a metal oxide semiconductor (MOS) based machine olfaction system for monitoring of banana ripeness. Journal of Agricultural Machinery, 5(1), 111-121, (In Farsi)
Spink, J. & Moyer, D.C. (2011). Defining the public health threat of food fraud. Journal of Food Science, 76, 157–163.
Tang, X., Bai, Y., Duong, A., Smith, T., Li, L. & Zhang, L. (2009).Formaldehyde in China: Production, consumption, exposure levels, and health effects. Environment International, 35(8),1210-1224.
Tian, X., Wang, J. & Cui, S. (2013). Analysis of pork adulteration in minced mutton using electronic nose of metal oxide sensors. Journal of Food Engineering,119(4), 744–749.
Tohidi, M. (2010). Simulation of rough rice drying using artificial neural networks. M. Sc. dissertation, Isfahan University of Technology, Iran.
Torri, L., Sinelli, N. & Limbo, S. (2010). Shelf life evaluation of fresh-cut pineapple by using an electronic nose. Postharvest biology and technology, 56(3), 239-245.
Veloso, A., Teixeira, N. & Ferreira, I. (2002). Separation and quantification of the major casein fractions by reverse-phase high-performance liquid chromatography and urea–polyacrylamide gel electrophoresis: Detection of milk adulterations. Journal of Chromatography A, 967(2), 209-218.
Wang, B., Xu, S. & Sun, D. (2010). Application of the electronic nose to the identification of different milk flavorings. Food Research International, 43: 255-262.
Yu, H., Wang, J. & Xu, Y. (2007). Identification of adulterated milk using electronic nose. Sensors and Materials, 19, 275–285.
Yu, X., Xu, L., Liu, L. & Zhang, R. (2016). A novel method for qualitative analysis of edible oil oxidation using an electronic nose. Food Chemistry, 202, 229-235.
Zakaria, A., Shakaff, A.Y.M., Masnan, M.J., Saad, F.S.A., Adom, A.H., Ahmad, M.N., Jaafar, M.N., Abdullah, A.H. & Kamarudin, L.M. (2012). Improved maturity and ripeness classifications of magnifera indica cv. harumanis mangoes through sensor fusion of an electronic nose and acoustic sensor. Sensors, 12(5), 6023-6048.
Zheng, H. & Wang, J. (2006). Electronic nose and data analysis for detection of maize oil adulteration in sesame oil. Sensors and Actuators B: Chemical, 119(2), 449–455.
Zheng, S., Xie, C., Hu, M., Li, H., Bai, Z. & Zeng, D. (2008). An entire feature extraction method of metal oxide gas sensors. Sensors and Actuators B: Chemical, 132(1), 81–89.