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ارزیابی الگوهای شناسایی برای تشخیص ناخالصی روغن کنجد توسط سامانۀ ماشینبویایی مبتنی بر تحلیل چند متغیره | ||
| تحقیقات سامانهها و مکانیزاسیون کشاورزی | ||
| مقاله 3، دوره 23، شماره 81، خرداد 1401، صفحه 37-56 اصل مقاله (2.21 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/amsr.2022.356371.1400 | ||
| نویسندگان | ||
| هادی حسینی1؛ سعید مینایی* 2؛ بابک بهشتی3 | ||
| 1دانشجوی دکتری گروه مهندسی مکانیک بیوسیستم، دانشکده علوم کشاورزی و صنایع غذایی، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران | ||
| 2استاد گروه مهندسی مکانیک بیوسیستم، دانشکده کشاورزی، دانشگاه تربیت مدرس، تهران، ایران | ||
| 3استادیار گروه مهندسی مکانیک بیوسیستم، دانشکده علوم کشاورزی و صنایع غذایی، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران | ||
| چکیده | ||
| روغن کنجد یکی از روغنهای بسیار با کیفیتِ خوراکی است که قیمت بالای آن، انگیزه را برای تولید نمونۀ تقلبی افزایش داده است. بنابراین، شناسایی ناخالصی به ویژه با ابزار یا روشهای غیرتماسی برای تشخیص خلوص روغن کنجد نسبت به نوع تقلبی آن، اهمیت زیادی دارد. در این پژوهش، میزان اسیدهای چرب نمونههای خالص روغن کنجد، کلزا، آفتابگردان و ترکیب آنها با سطوح ناخالصی (5، 10، 20، 30، 40 و50 درصد) به روش گازکروماتوگرافی تعیین و سپس، قابلیت سامانۀ ماشینبویایی طراحی شده بر پایۀ ده حسگر نیمههادی اکسید فلزی برای تشخیص و طبقهبندی نمونهها ارزیابی شد. پس از استخراج بردار ویژگیِ پاسخ حسگرها نسبت به ترکیبات فرار مواد آلی روغنها، دادههای پیشپردازش شده با روشهای تحلیل چندمتغیره تحلیل مؤلفههای اصلی، تحلیل تفکیک خطی، حداقل مربعات جزئی، K-نزدیکترین همسایه و ماشین بردارپشتیبان به منظور تشخیص و طبقهبندی نمونههای ناخالص، بررسی شدند. در روش PCA، واریانس مجموعه دادهها 68/95 درصد به دست آمد. برای انتخاب الگوی مناسب با دقت بالا، دادههای به دست آمده با روشهای LDA، KNN و SVM طبقهبندی شدند. نتایج نشان داد ماشین بردارپشتیبان با تابع کرنل پایۀ شعاعی در روش C-SVM دارای بیشترین دقت طبقهبندی بود و دقت آموزش و اعتبارسنجی به ترتیب 34/96 و 56/90 درصد به دست آمد. بعد از آن، مدلهای LDA و KNN به ترتیب دارای دقت طبقهبندی 30/92 و 83/89 درصد بودند. بر پایه نتایج به دست آمده، این سامانه به همراه الگورتیم طبقهبندی مناسب میتواند به عنوان روشی غیرمخرب برای طبقهبندی موفق نمونهها و تشخیص ناخالصیهای روغن کنجد به کار گرفته شود. | ||
| کلیدواژهها | ||
| بوسنجی؛ بینیالکترونیک؛ تحلیل مولفۀ اصلی؛ روغن خوراکی؛ غیرمخرب | ||
| عنوان مقاله [English] | ||
| Evaluation of Pattern Recognition for Detecting Adulteration in Sesame Oil using Machine Olfaction System Based on Multivariate Analysis | ||
| نویسندگان [English] | ||
| Hadi Hosseini1؛ Saeid Minaei2؛ Babak Beheshti3 | ||
| 1Department of Biosystems Engineering, Faculty of Agricultural Sciences and Food Industry, Science and Research Branch, Islamic Azad University, Tehran, Iran | ||
| 2Biosystems Eng. Dept. Tarbiat Modares Univ. Tehran, Iran | ||
| 3Department of Biosystems Engineering, Faculty of Agricultural Sciences and Food Industry, Science and Research Branch, Islamic Azad University, Tehran, Iran | ||
| چکیده [English] | ||
| Sesame oil which is one of the most popular and expensive edible oils, is prone to adulteration. High price of Sesame oil has motivated adulterers to mix the high-quality Sesame oil with low-quality, less expensive vegetable oils. In this study, the fatty-acid profiles of sesame, rapeseed, sunflower oil samples as well as their mixtures (0, 5, 10, 20, 30, 40 and 50% levels) were determined using Gas Chromatography. Also, Machine olfaction system containing 10 MOS sensors was utilized for detection experiments. Sensor response patterns were used for analyzing and recognizing pattern of electronic-nose signals using multivariate data analysis, including Principal Component Analysis (PCA), Linear Discriminant Analyses (LDA), Partial Least Squares (PLS), K-Nearest Neighbors (KNN) and Support Vector Machine (SVM). Results of the SVM with RFB kernel in C-SVM method had the highest classification accuracy. The accuracy of training and validation were 96.34 and 90.56%, respectively, and next were LDA and KNN models with classification accuracies of 92.30% and 89.94%, respectively. In the light of these results, the proposed models along with the measurement system represent excellent tools for the detection of sesame seed oil adulteration with cheaper vegetable oils. | ||
| کلیدواژهها [English] | ||
| Edible Oil, Electronic Nose, Non-Destructive, Olfactometry, Principal Component Analysis | ||
| مراجع | ||
|
Anon. (2015). ISIRI 13392, Edible cold pressed oils – specifications & test methods. 1st Revision. Iranian National Standardization Organization. (in Persian)
Anon. (2019). FAOSTAT database. Food and Agriculture Organization of the United Nations.
Anon. (2020). Import statistics by tariff code, The Islamic Republic of Iran Customs Administration (IRICA), Available at: https://irica.ir. (in Persian)
Arshak, K., Moore, E., Lyons, G. M., Harris, J., & Clifford, S. (2004). A review of gas sensors employed in electronic nose applications. Journal of Sensor Review, 24(2), 181-198.
Ayari, F., Mirzaee-Ghaleh, E., Rabbani, H., & Heidarbeigi, K. (2020). Implementation of a Machine Olfaction for the Detection of Adulteration in Cow Ghee. Journal of Agricultural Machinery, 10(2),
Baeten, V., & Aparicio, R. (2000). Edible oils and fats authentication by Fourier transform Raman spectrometry. Biotechnology, Agronomy and Society and Environment, 4(4), 196–203.
Baldwin, E. A., Bai, J., Plotto, A., & Dea, S. (2011). Electronic noses and tongues: Applications for the food and pharmaceutical industries. Sensors, 11(5), 4744-4766.
Beygami, M., Dadgar, B., Eslami, M., & Haghanifar, H. (2013). Investigation and comparison of fatty acid composition and quality characteristics of Iranian and foreign olive oils. Proceedings of the 21th National Congress of Food Science and Technology. Oct. 29-31. Shiraz University, Shiraz, Iran. (in Persian)
Doleman, B. J., & Lewis, N. S. (2001). Comparison of odor detection thresholds and odor discriminablities of a conducting polymer composite electronic nose versus mammalian olfaction. Sensors and Actuators B: Chemical, 72(1), 41-50.
Das, S., Sivaramakrishna, M., Biswas, K., & Goswami, B. (2015). A low cost instrumentation system to analyze different types of milk adulteration. ISA Transactions, 56, 268-275.
Esteki, M., Farajmand, B., Kolahderazi, Y., & Simal-Gandara, J. (2017). Chromatographic fingerprinting with multivariate data analysis for detection and quantification of apricot kernel in almond powder. Food Analytical Methods, 10(10), 3312-3320.
Fisher, R. A. (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7(2), 179-188.
Geladi, P., & Kowalski, B. R. (1986). Partial least-squares regression: a tutorial. Analytica Chimica Acta, 185, 1-17.
Gómez, A. H., Hu, G., Wang, J., & Pereira, A. G. (2006). Evaluation of tomato maturity by electronic nose. Computers and Electronics in Agriculture, 54(1), 44-52.
Hwang, L. S. (2005). Sesame oil, bailey’s industrial oil and fat products. Vol. 6. Edited by Fereidoon Shahidi. Sixth Ed. John Wiley & Sons, Inc.
Hai, Z., & Wang, J. (2006). Detection of adulteration in camellia seed oil and sesame oil using an electronic nose. European Journal of Lipid Science and Technology, 108(2), 116-124.
Hosseini, H., Minaei, S. & Beheshti, B. (2021). Feasibility of detection of adulteration in sesame oil using olfaction machine system. Proceedings of the 13th Iranian National Congress on Biosystems Engineering and Mechanization. Sep. 15-17. Tarbiat Modares University. Tehran, Iran. (in Persian)
Jurs, P. C., Bakken, G. A., & McClelland, H. E. (2000). Computational methods for the analysis of chemical sensor array data from volatile analytes. Chemical Reviews, 100(7), 2649-2678.
Jiang, H., He, Y., & Chen, Q. (2021). Qualitative identification of the edible oil storage period using a homemade portable electronic nose combined with multivariate analysis. Journal of the Science of Food and Agriculture, 101(8), 3448-3456.
Kiani, S., & Minaei, S. (2015). Feasibility of using smart systems based on olfactory and visual machines to evaluate the quality and determination of effective ingredients of medicinal plant products (case study: Saffron). Proceedings of the 1st National Conference on Herbs and Herbal Medicine. May 28. Shahid Beheshti University, Tehan, Iran. (in Persian)
Khodamoradi, F., Mirzaee-Ghaleh, E., Dalvand, M. J., & Sharifi, R. (2019). Classification of savory (Satureja hortensis L.) based on the level of used urea fertilizer consumed using an olfactory machine. Iranian Journal of Medicinal and Aromatic Plants, 35(5), 789-801. (in Persian)
Lerma-García, M. J., Ramis-Ramos, G., Herrero-Martínez, J. M., & Simó-Alfonso, E. F. (2010). Authentication of extra virgin olive oils by Fourier-transform infrared spectroscopy. Food Chemistry, 118(1), 78-83.
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.
Mildner-Szkudlarz, S., & Jeleń, H. H. (2008). The potential of different techniques for volatile compounds analysis coupled with PCA for the detection of the adulteration of olive oil with hazelnut oil. Food Chemistry, 110(3), 751-761.
Masella, P., Parenti, A., Spugnoli, P., & Calamai, L. (2010). Nitrogen stripping to remove dissolved oxygen from extra virgin olive oil. European Journal of Lipid Science and Technology, 112(12), 1389-1392.
Men, H., Chen, D., Zhang, X., Liu, J., & Ning, K. (2014). Data fusion of electronic nose and electronic tongue for detection of mixed edible-oil. Journal of Sensors, https://doi.org/10.1155/2014/840685.
Mabood, F., Hussain, J., Jabeen, F., Abbas, G., Allaham, B., Albroumi, M., & Farooq, S. (2018). Applications of FT-NIRS combined with PLS multivariate methods for the detection & quantification of saccharin adulteration in commercial fruit juices. Food Additives & Contaminants: Part A, 35(6), 1052-1060.
Mohammad‐Razdari, A., Ghasemi‐Varnamkhasti, M., Yoosefian, S. H., Izadi, Z., & Siadat, M. (2019). Potential application of electronic nose coupled with chemometric tools for authentication assessment in tomato paste. Journal of Food Process Engineering, 42(5), e13119. https://doi.org/10.1111/jfpe.13119.
Makarichian, A., Chayjan, R. A., Ahmadi, E., & Zafari, D. (2021). Early detection and classification of fungal infection in garlic (A. sativum) using electronic nose. Computers and Electronics in Agriculture, 192, 106575. https://doi.org/10.1016/j.compag.2021.106575.
Malekahmadi, R., Ardakani, S. A. Y., Sadeghian, A., & Eslami, H. (2021). Rapid Detection of Adulteration in Mixing Sesame, Sunflower, and Canola Vegetable Oils by Mathematical Model. Food Analytical Methods, 14, 1456–1463. https://doi.org/10.1007/s12161-021-01980-y.
Nam, Y. S., Noh, K. C., Roh, E. J., Keum, G., Lee, Y., & Lee, K. B. (2014). Determination of edible vegetable oil adulterants in sesame oil using 1H nuclear magnetic resonance spectroscopy. Analytical Letters, 47(7), 1190-1200.
Ozulku, G., Yildirim, R. M., Toker, O. S., Karasu, S., & Durak, M. Z. (2017). Rapid detection of adulteration of cold pressed sesame oil adultered with hazelnut, canola, and sunflower oils using ATR-FTIR spectroscopy combined with chemometric. Food Control, 82, 212-216.
Olaleye, O. O., & Kukwa, R. E. (2018). Physico chemical properties and chemical constituent characterization of Moringa oleifera seed oil from Benue State, Nigeria, extracted using cold and soxhlet method. International Research Journal of Pure and Applied Chemistry, 16(3), 1-11
Pearce, T. C., Schiffman, S. S., Nagle, H. T., & Gardner, J. W. (2006). Handbook of machine olfaction: electronic nose technology. John Wiley & Sons.
Peng, D., Bi, Y., Ren, X., Yang, G., Sun, S., & Wang, X. (2015). Detection and quantification of adulteration of sesame oils with vegetable oils using gas chromatography and multivariate data analysis. Food Chemistry, 188, 415-421.
Ramesh, B., Mohtasebi, S. S., & Rafiee, S. (2019). Classification of different iranian rice varieties and frauded rice based on volatile compounds detected by electronic nose method. Iranian Journal of Biosystems Engineering, 50(3), 595-606. (In Persian)
Roy, M., & Yadav, B. K. (2021). Electronic nose for detection of food adulteration: a review. Journal of Food Science and Technology, 59(3):846-858. doi: 10.1007/s13197-021-05057-w.
Seo, H. Y., Ha, J., Shin, D. B., Shim, S. L., No, K. M., Kim, K. S., Lee, K. B. & Han, S. B. (2010). Detection of corn oil in adulterated sesame oil by chromatography and carbon isotope analysis. Journal of the American Oil Chemists' Society, 87(6), 621-626.
Shao, X., Li, H., Wang, N., & Zhang, Q. (2015). Comparison of different classification methods for analyzing electronic nose data to characterize sesame oils and blends. Sensors, 15(10), 26726-26742.
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.
Taheri-Garavand, A., Mirzaee-Ghaleh, E., & Ayari, F. (2020). Intelligent Classification of fresh chicken meat from frozen-thawed using olfactory machine. Food Technology & Nutrition, 99(17), 13-22. (in Persian)
Vaclavik, L., Cajka, T., Hrbek, V., & Hajslova, J. (2009). Ambient mass spectrometry employing direct analysis in real time (DART) ion source for olive oil quality and authenticity assessment. Analytica Chimica Acta, 645(1-2), 56-63.
Voss, H. G. J., Mendes Júnior, J. J. A., Farinelli, M. E., & Stevan, S. L. (2019). A prototype to detect the alcohol content of beers based on an electronic nose. Sensors, 19(11), 2646.
Wu, Z., Zhang, H., Sun, W., Lu, N., Yan, M., Wu, Y. & Fan, S. (2020). Development of a low-cost portable electronic nose for cigarette brands identification. Sensors, 20(15), 4239. https://doi.org/10.3390/s20154239.
Xu, L., Yu, X., Liu, L., & Zhang, R. (2016). A novel method for qualitative analysis of edible oil oxidation using an electronic nose. Food chemistry, 202, 229-235.
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.
Zarezadeh, M. R., Aboonajmi, M., Ghasemi Varnamkhasti, M., & Azarikia, F. (2021). Estimation of the best classification algorithm and fraud detection of olive oil by olfaction machine. Journal of Agricultural Machinery, 11(2), 371-383. (in Persian)Zhang, Q., Liu, C., Sun, Z., Hu, X., Shen, Q., & Wu, J. (2012). Authentication of edible vegetable oils adulterated with used frying oil by Fourier Transform Infrared Spectroscopy. Food Chemistry, 132(3), 1607-1613.
Zhang, L., Shuai, Q., Li, P., Zhang, Q., Ma, F., Zhang, W., & Ding, X. (2016). Ion mobility spectrometry fingerprints: A rapid detection technology for adulteration of sesame oil. Food chemistry, 192, 60-66.
Zhu, J., Agyekum, A. A., Kutsanedzie, F. Y., Li, H., Chen, Q., Ouyang, Q., & Jiang, H. (2018). Qualitative and quantitative analysis of chlorpyrifos residues in tea by surface-enhanced Raman spectroscopy (SERS) combined with chemometric models. Lwt, 97, 760-769.
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