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.
journal_name
Food Chemjournal_title
Food chemistryauthors
Zhang L,Shuai Q,Li P,Zhang Q,Ma F,Zhang W,Ding Xdoi
10.1016/j.foodchem.2015.06.096subject
Has Abstractpub_date
2016-02-01 00:00:00pages
60-6eissn
0308-8146issn
1873-7072pii
S0308-8146(15)00988-7journal_volume
192pub_type
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