ArticleJournal of food science and technology2023
A dedicated electronic nose combined with chemometric methods for detection of adulteration in sesame oil.
Article in Journal of food science and technology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Discovery of Species-Specific Peptide Markers for Superseed Authentication Using Targeted LC-MS/MS Proteomics.Molecules (Basel, Switzerland) · 2025Article
- Chemometric Tools Associated with Quality Parameters for Evaluation ofFoods (Basel, Switzerland) · 2025Article
- Investigation of the differences in volatile organic compounds of sesame oil under different processing methods using GC-IMS and electronic nose.Frontiers in nutrition · 2025Article
- Design of an Artificial Intelligence of Things-Based Sesame Oil Evaluator for Quality Assessment Using Gas Sensors and Deep Learning Mechanisms.Foods (Basel, Switzerland) · 2023Article
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Authors and funding
3 authors.
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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
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