Detection and quantification of adulteration of sesame oils with vegetable oils using gas chromatography and multivariate data analysis. (1st December 2015)

• Record Type: Journal Article
• Authors: Peng, Dan; Bi, Yanlan; Ren, Xiaona; Yang, Guolong; Sun, Shangde; Wang, Xuede
• Is Part Of: Food chemistry. Volume 188(2015)
• Page Start: 415
• Page End: 421
• Publication Date: 2015-12-01
• Subjects: Adulterated sesame oil -- Classification -- Quantification -- Gas chromatography -- Multivariate data analysis; Food -- Analysis -- Periodicals; Food -- Composition -- Periodicals; 664
• DOI: 10.1016/j.foodchem.2015.05.001
• Languages: English
• ISSNs: 0308-8146

Abstract

Highlights: A hierarchical method is designed to solve the problem of adulterated sesame oil. 746 samples were prepared by mixing sesame oil with five types of vegetable oil. Intrinsic relationship of samples was investigated by PCA and SOM cluster. Detection limit for authentication is as low as 5% by SVM-based algorithm. The precision of PLS-based predication model ranges from 1.19% to 4.29%. Abstract: This study was performed to develop a hierarchical approach for detection and quantification of adulteration of sesame oil with vegetable oils using gas chromatography (GC). At first, a model was constructed to discriminate the difference between authentic sesame oils and adulterated sesame oils using support vector machine (SVM) algorithm. Then, another SVM-based model is developed to identify the type of adulterant in the mixed oil. At last, prediction models for sesame oil were built for each kind of oil using partial least square method. To validate this approach, 746 samples were prepared by mixing authentic sesame oils with five types of vegetable oil. The prediction results show that the detection limit for authentication is as low as 5% in mixing ratio and the root-mean-square errors for prediction range from 1.19% to 4.29%, meaning that this approach is a valuable tool to detect and quantify the adulteration of sesame oil.