| Literature DB >> 26304320 |
Liangxiao Zhang1, Qian Shuai2, Peiwu Li3, Qi Zhang4, Fei Ma4, Wen Zhang5, Xiaoxia Ding6.
Abstract
A simple and rapid detection technology was proposed based on ion mobility spectrometry (IMS) fingerprints to determine potential adulteration of sesame oil. Oil samples were diluted by n-hexane and analyzed by IMS for 20s. Then, chemometric methods were employed to establish discriminant models for sesame oils and four other edible oils, pure and adulterated sesame oils, and pure and counterfeit sesame oils, respectively. Finally, Random Forests (RF) classification model could correctly classify all five types of edible oils. The detection results indicated that the discriminant models built by recursive support vector machine (R-SVM) method could identify adulterated sesame oil samples (⩾ 10%) with an accuracy value of 94.2%. Therefore, IMS was shown to be an effective method to detect the adulterated sesame oils. Meanwhile, IMS fingerprints work well to detect the counterfeit sesame oils produced by adding sesame oil essence into cheaper edible oils.Entities:
Keywords: Adulteration detection; Ion mobility spectrometry fingerprints; Random forests; Rapid detection technology; Recursive support vector machine; Sesame oil
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Year: 2015 PMID: 26304320 DOI: 10.1016/j.foodchem.2015.06.096
Source DB: PubMed Journal: Food Chem ISSN: 0308-8146 Impact factor: 7.514