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Evaluation of Pattern Recognition for Detecting Adulteration in Sesame Oil using Machine Olfaction System Based on Multivariate Analysis | ||
| تحقیقات سامانهها و مکانیزاسیون کشاورزی | ||
| Article 3, Volume 23, Issue 81, June 2022, Pages 37-56 PDF (2.21 M) | ||
| Document Type: Original Article | ||
| DOI: 10.22092/amsr.2022.356371.1400 | ||
| Authors | ||
| Hadi Hosseini1; Saeid Minaei* 2; Babak Beheshti1 | ||
| 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 | ||
| Abstract | ||
| 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. | ||
| Keywords | ||
| Edible Oil; Electronic Nose; Non-Destructive; Olfactometry; Principal Component Analysis | ||
| References | ||
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