Chemometric classification and quantification of sesame oil adulterated with other vegetable oils based on fatty acids composition by gas chromatography. (July 2019)

• Record Type: Journal Article
• Authors: Xing, Changrui; Yuan, Xiangyu; Wu, Xueyou; Shao, Xiaolong; Yuan, Jian; Yan, Wenjing
• Is Part Of: Lebensmittel-Wissenschaft + Technologie =. Volume 108(2019)
• Page Start: 437
• Page End: 445
• Publication Date: 2019-07
• Subjects: Adulteration -- Sesame oil -- Principal component analysis -- Linear discriminant analysis -- Partial least-squares regression; Food industry and trade -- Periodicals; Food -- Composition -- Periodicals; Microbiology -- Periodicals; Nutrition -- Periodicals; 664.005
• DOI: 10.1016/j.lwt.2019.03.085
• Languages: English
• ISSNs: 0023-6438

Abstract

Abstract: The detection of adulteration of sesame oil with four vegetable oils was investigated by measuring fatty acid composition. Adulteration based fatty acid composition was evaluated with 73 sesame seed samples, 57 rapeseed seed samples, 103 soybean seed samples, 11 brands of sunflower oil and 14 brands of maize oil. Discriminate analysis and principal component analysis were used to study the data distribution patterns based on the fatty acid composition and their ratios obtained from pure sesame oils and blends. Pure vegetable oils were gathered in a tendency in principal component analysis score diagram. Although the parameters calculated by fatty acid composition were simple and effective in detection of oil mixture of single vegetable edible oil or different vegetable edible oil, this method could only be used for qualitative determination. Linear discriminant analysis applied and the obtained overall accuracies were between 97.27% and 100%. Partial least-squares regression, the multivariate quantification method, have been successfully applied and quantitatively recognized the type and the level of extra added vegetable oils into the sesame oils. In general, the fatty acid profile of vegetable oils obtained by GC was useful in both qualitative and quantitative detection of sesame oil blends. Graphical abstract: Concentration values for adulteration obtained from the PLSR model vs. the actual concentration of sesame oil for prediction set. Image 1 Highlights: GC methods with chemometrics for detecting adulterated sesame oil was presented. Quantification model could detect other four oils in percentages from 5% to 50%. Quantification model showed high accuracy and stability.