Application of olfactory machine system for detection of adulteration in caraway samples

Document Type : Research Article


1 MSc student, Department of Mechanical Engineering of Biosystems, Faculty of Agriculture, Shahrekord University

2 Associate Professor, Department of Mechanical Engineering of Biosystems, Faculty of Agriculture, Shahrekord University

3 Ph.D., Department of Mechanical Engineering of Biosystems, Faculty of Agriculture, Shahrekord University

4 Professor, Department of Agricultural Machinery Engineering, College of Agriculture and Natural Resources, University of Tehran

5 Assistant Professor, Department of Food Science and Technology, Toyserkan Faculty of Industrial Engineering, Bu-Ali Sina University


Caraway as one of the most valuable herbs are widely used in pharmaceutical and food industries and due to the high cost and quality difference between different varieties of caraway, adulteration maybe carried out in this product in market that leads to the low satisfactory sense in consumers. In this study, an olfactory machine system based on eight metal oxide semiconductor sensors combined with the pattern recognition method was used to identify the different levels of adulteration in the caraway and its authenticity assessment. The principal component analysis method was used to analyze the extracted data from the sensor response signal. Based on the results, the principal component analysis with the two main components of PC1 and PC2 described %94 of the variance of the data set for the used samples. In the sensor array, MQ4 and FIS sensors revealed the highest loading coefficient values and MQ135, MQ3 and TGS813 sensors devoted the lowest ones. Then, the classification of samples was done using support vector machine (SVM) and decision tree (DT) techniques. SVM with linear kernel function showed the training and validation accuracy values as 100% and 97.5%, respectively. Also, the success rate of the DT method in the distinction and classification samples of adulterated caraway was estimated as 90%.


Main Subjects

[1] حقیرالسادات، ب.؛ ف. برنارد، ف.؛ کلانتر، س.م.؛ شیخها، م.ح.؛ حکم اللهی، ف.؛ عظیم زاده، م.؛ حوری، م. 1389. بررسی ترکیبات موثر و خواص آنتی اکسیدانی اسانس گیاه دارویی زیره سیاه استان یزد. مجله علمی پژوهشی دانشگاه علوم پزشکی شهید صدوقی یزد. دوره 18، شماره 4،ص 284-291.
[2] Laribi, B., Kouki, K., Bettaieb, T., Mougou, A., & Marzouk, B. (2013). Essential oils and fatty acids composition of Tunisian, German and Egyptian caraway (Carum carvi L.) seed ecotypes: A comparative study. Industrial Crops and Products, 41, 312-318.
 [3] Seidler-Lozykowska, K., Baranska, M., Baranski, R., Krol, D. (2010). Raman analysis of caraway (Carum carvi L.) single fruits. evaluation of essential oil content and its composition. Journal of agricultural and food chemistry, 58(9), 5271-5275.
[4] Johri, R.K. (2011). Cuminum cyminum and Carum carvi: An update. Pharmacognosy reviews, 5(9), 63.
[5] Keshavarz, A., Minaiyan, M., Ghannadi, A., Mahzouni, P. (2012). Effects of Carum carvi L.(Caraway) extract and essential oil on TNBS-induced colitis in rats. Research in pharmaceutical sciences, 8(1), 1-8.
[6] Rasooli, I., Allameh, A. (2016). Caraway (Carum carvi L.) Essential Oils.
[7] Thappa, R.K., Ghosh, S., Agarwal, S.G., Raina, A. K., Jamwal, P.S. (1991). Comparative studies on the major volatiles of Kalazira (Bunium persicum seed) of wild and cultivated sources. Food chemistry, 41(2), 129-134.
[8] 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(7), 1391-1401.
[9] Peris, M., Escuder-Gilabert, L. (2016). Electronic noses and tongues to assess food authenticity and adulteration. Trends in Food Science & Technology, 58, 40-54.
[10] Gliszczyńska-Świgło, A., Chmielewski, J. Electronic nose as a tool for monitoring the authenticity of food. a review. Food Analytical Methods, 1-17.
[11] Ghasemi-Varnamkhasti, M., Mohtasebi, S.S., Siadat, M., Lozano, J., Ahmadi, H., Razavi, S.H., Dicko, A. (2011). Aging fingerprint characterization of beer using electronic nose. Sensors and Actuators B: Chemical, 159(1), 51-59.
[12] Ghasemi-Varnamkhasti, M., Aghbashlo, M. (2014). Electronic nose and electronic mucosa as innovative instruments for real-time monitoring of food dryers. Trends in Food Science & Technology, 38(2), 158-166
[13] Tahri, K., Tiebe, C., El Bari, N., Hübert, T., Bouchikhi, B. (2016). Geographical provenience differentiation and adulteration detection of cumin by means of electronic sensing systems and SPME-GC-MS in combination with different chemometric approaches. Analytical Methods, 8(42), 7638-7649.
[14] Gardner, J.W., Bartlett, P.N. (1994). A brief history of electronic noses. Sensors and Actuators B: Chemical, 18(1-3), 210-211
[15] Loutfi, A., Coradeschi, S., Mani, G. K., Shankar, P., Rayappan, J.B.B. (2015). Electronic noses for food quality: a review. Journal of Food Engineering, 144, 103-111.
[16] 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.
[17] Banach, U., Tiebe, C., Hübert, T. (2012). Multi gas sensors for the quality control of spice mixtures. Food Control, 26(1), 23-27
.[18] 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(7), 1313-1321.
[19] Ongo, E., Falasconi, M., Sberveglieri, G., Antonelli, A., Montevecchi, G., Sberveglieri, V., ... Sevilla III, F. (2012). Chemometric discrimination of Philippine civet coffee using electronic nose and gas chromatography mass spectrometry. Procedia Engineering, 47, 977-980.
[20] Pardo, M., Niederjaufner, G., Benussi, G., Comini, E., Faglia, G., Sberveglieri, G., ... Lundstrom, I. (2000). Data preprocessing enhances the classification of different brands of Espresso coffee with an electronic nose. Sensors and Actuators B: Chemical, 69(3), 397-403.
[21] Liu, H., Zeng, F. K., Wang, Q. H., Wu, H. S. (2013). Studies on the chemical and flavor qualities of white pepper (Piper nigrum L.) derived from five new genotypes. European Food Research and Technology, 237(2), 245-251.
[22] Chen, Q., Zhao, J., Chen, Z., Lin, H., Zhao, D. A. (2011). Discrimination of green tea quality using the electronic nose technique and the human panel test, comparison of linear and nonlinear classification tools. Sensors and Actuators B: Chemical, 159(1), 294-300.
[23] Zou, H.Q., Li, S., Huang, Y.H., Liu, Y., Bauer, R., Peng, L., ... Yan, Y.H. (2014). Rapid identification of Asteraceae plants with improved RBF-ANN classification models based on MOS sensor E-nose. Evidence-Based Complementary and Alternative Medicine, 2014.
[24] توحیدی، م.؛ قاسمی ورنامخواستی، م.؛ غفاری نیا، و.؛ محتسبی، س.س.؛ بنیادیان، م.؛ (1395). ساخت و توسعه یک سامانه ماشین بویایی در ترکیب با روش‌های شناسایی الگو برای تشخیص تقلب فرمالین در شیر خام. مهندسی بیوسیستم ایران. دوره 47، شماره 4، ص 1-10.
[25] قاسمی‌ورنامخواستی، م. (1390). طراحی، توسعه و پیاده سازی سیستم ماشین بویایی و زبان بیوالکتریک بر پایه نیمه‌هادی‌های اکسید فلزی به‌منظور آشکارسازی تغییر کیفیت ماءالشعیر در ترکیب با روش‌های آنالیز تشخیص الگو. رساله دکتری. گروه مکانیک ماشین‌های کشاورزی. دانشگاه تهران.
[26] Pearce, T.C., Schiffman, S.S., Nagle, H.T., Gardner, J.W. (Eds.). (2006). Handbook of machine olfaction: electronic nose technology. John Wiley & Sons.
[27] Gardner, J.W., Craven, M., Dow, C., Hines, E.L. (1998). The prediction of bacteria type and culture growth phase by an electronic nose with a multi-layer perceptron network. Measurement Science and Technology, 9(1), 120.
[28] Arshak, K., Moore, E., Lyons, G.M., Harris, J., Clifford, S. (2004). A review of gas sensors employed in electronic nose applications. Sensor review, 24(2), 181-198.
[29] 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(1), 44-51.
[30] Ye, T., Jin, C., Zhou, J., Li, X., Wang, H., Deng, P., ... Xiao, X. (2011). Can odors of TCM be captured by electronic nose? The novel quality control method for musk by electronic nose coupled with chemometrics. Journal of pharmaceutical and biomedical analysis, 55(5), 1239-1244.
[31] Sanaeifar, A., Mohtasebi, S., Ghasemi-Varnamkhasti, M., Ahmadi, H., Lozano Rogado, J.S. (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).
[32] کشاورز، ا.؛ قاسمیان یزدی، ح. (1384) یک الگوریتم سریع مبتنی بر ماشین بردار پشتیبان برای طبقه‌بندی تصاویر ابرطیفی با استفاده از همبستگی مکانی. مهندسی برق و مهندسی کامپیوتر ایران، سال 3، شماره 1، ص 37-44.
[33] طلوعی اشلقی، ع.؛ پورابراهیمی، ع.؛ ابراهیمی، م.؛ قاسم احمد، ل. (1391) پیش‌بینی عود مجدد سرطان پستان به کمک سه تکنیک داده کاوی. بیماری‌های پستان ایران، سال 5، شماره 4، ص 23-34.
[34] غضنفری، م.؛ علیزاده، س.؛ تیمورپور، ب. (1387) داده کاوی و کشف دانش. چاپ اول، انتشارات دانشگاه علم و صنعت ایران، ص 218-238.
[35] صفدری، ر.؛ قاضی سعیدی، م.؛ نصیری، م.؛ ارجی، گ. (1393) مقایسه عملکرد درخت تصمیم گیری و شبکه عصبی در پیشگویی ابتلا به آنفارکتوس قلبی. علوم پیراپزشکی و توانبخشی مشهد، دوره 3، شماره 2، ص 26-37.
[36] Kiani, S., Minaei, S., Ghasemi-Varnamkhasti, M. (2016). A portable electronic nose as an expert system for aroma-based classification of saffron. Chemometrics and Intelligent Laboratory Systems, 156, 148-156.
[37] Patil, T.R., Sherekar, S.S. (2013). Performance analysis of Naive Bayes and J48 classification algorithm for data classification. International Journal of Computer Science and Applications, 6(2), 256-261.
[38] Ravi, R., Prakash, M., and Bhat, K. K. (2013). Characterization of aroma active compounds of cumin (Cuminum cyminum L.) by GC-MS, e-nose and sensory techniques. International Journal of Food Properties. 16(5): 1048-1058.