Ion mobility spectrometry fingerprints: A rapid detection technology for adulteration of sesame oil. (1st February 2016)

• Record Type: Journal Article
• Authors: Zhang, Liangxiao; Shuai, Qian; Li, Peiwu; Zhang, Qi; Ma, Fei; Zhang, Wen; Ding, Xiaoxia
• Is Part Of: Food chemistry. Volume 192(2016)
• Page Start: 60
• Page End: 66
• Publication Date: 2016-02-01
• Subjects: Ion mobility spectrometry fingerprints -- Sesame oil -- Adulteration detection -- Rapid detection technology -- Recursive support vector machine -- Random forests; Food -- Analysis -- Periodicals; Food -- Composition -- Periodicals; 664
• DOI: 10.1016/j.foodchem.2015.06.096
• Languages: English
• ISSNs: 0308-8146

Abstract

Highlights: A simple and rapid IMS fingerprint was developed for adulteration of sesame oil. Discriminant model for adulteration detection was built by R-SVM and RF. R-SVM model could identify adulterated sesame oils with an accuracy of 94.2%. IMS fingerprint was an effective method to detect the counterfeit sesame oils. 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 20 s. 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.