Gas chromatography-ion mobility spectrometric classification of vegetable oils based on digital image processing
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In this paper, a headspace instrument equipped with gas chromatography-ion mobility spectrometry (GC-IMS) was used to classify three kinds of vegetable oils in cooperation with chemometric tools. The procedure contained direct loading of the vegetable oil sample into a vial, headspace generation, and automatic injection of volatile organic components into GC-IMS device. A total of 187 oil samples were detected by GC-IMS, and Otsu’s threshold segmentation and colorized difference methods were adopted to realize automatic peak detection of two-dimensional matrix and comparative visualization for further chemometric pretreatment. Based on the obtained data, principal components analysis showed that 95.77% of sample information could be explained by the first two principal components. Moreover, the oil samples were divided into calibration set (n = 130) and prediction set (n = 57), and the model built by the k-nearest neighbors algorithm showed that the recognition accuracy of calibration set was 100% and the recognition accuracy of prediction set was 98.24%. These results verify that digital image processing methods applied to GC-IMS datasets could preserve chemical information and support qualitative analysis. Thus, GC-IMS technique can be considered a vanguard and reliable tool for recognition of different types of common vegetable oils.
KeywordsGC-IMS Vegetable oil Comparative analysis Classification
This work was funded by the National Natural Science Foundation of China (Grant No. 31772056). The authors also would like to thank the support of Jinan Hanon Instrument (China).
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