| Title: | A dedicated electronic nose combined with chemometric methods for detection of adulteration in sesame oil |
| Author(s): | Hosseini H; Minaei S; Beheshti B; |
| Address: | "Department of Biosystems Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran. GRID: grid.411463.5. ISNI: 0000 0001 0706 2472 Biosystems Engineering Department, Faculty of Agriculture, Tarbiat Modares University, Tehran, Iran. GRID: grid.412266.5. ISNI: 0000 0001 1781 3962" |
| DOI: | 10.1007/s13197-023-05792-2 |
| ISSN/ISBN: | 0022-1155 (Print) 0975-8402 (Electronic) 0022-1155 (Linking) |
| Abstract: | "Sesame oil (SO), one of the most popular and expensive edible oils, is prone to adulteration. In this study, the fatty acid profiles of pure sesame seed oil and samples adulterated with two less expensive edible oils (canola and sunflower) were analyzed using Gas Chromatography. A dedicated e-nose system was developed and tested on 15 mixtures of sesame-canola and sesame-sunflower samples. Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Multi-Layered Perceptron (MLP) methods were utilized to identify adulteration through the evaluation of Volatile Organic Compound. Result of chromatography showed that most samples of sesame oil containing impurities at levels less than 30% were recognized incorrectly in the standard range of SO fatty acids. This is while the developed e-nose system was able to detect adulteration at much lower levels. According to the results, PCA and LDA methods can describe the data set variance with precision of 95.6% and 97%, respectively. The MLP model had better results compared to PCA and LDA, with high determination coefficient (R(2) = 0.981) and low RMSE (0.0178). Results indicate that the e-nose system provided an effective non-destructive method to detect SO adulteration at levels as low as 5%, which GC was unable to detect" |
| Keywords: | Aroma Artificial neural networks Edible oil Machine olfaction Metal-oxide semiconductor sensors Quality assessment; |
| Notes: | "PubMed-not-MEDLINEHosseini, Hadi Minaei, Saeid Beheshti, Babak eng India 2023/08/21 J Food Sci Technol. 2023 Oct; 60(10):2681-2694. doi: 10.1007/s13197-023-05792-2. Epub 2023 Aug 4" |
