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Gas Chromatography-Ion Mobility Spectrometry (GC-IMS) was applied to identify rice varieties and adulteration in order to address the inefficiencies caused by the dependence on intricate biochemical procedures for the identification of rice species and adulteration. The original migration profile data for the identification of rice varieties and adulteration were provided by testing the volatile flavor substances of five kinds of rice. The original data was compressed through Principal Component Analysis (PCA), which was then used as the classifier's input. An improved Multi-classification Maximum-interval Twin Support Vector Machine (MMTSVM) was proposed to construct a multi-classifier with increased classification efficiency and accuracy compared to the conventional support vector machine. The experimental results on the testing set demonstrated that the accuracy of rice variety identification was close to 94.00%, which was obviously better than that of conventional support vector machines, decision trees, and other models. At the same time, the experimental results also showed that the accuracy of the GC-IMS method in identifying adulterated rice reached 93.3%, outperforming the traditional chromatographic or spectral analysis methods.
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