Abstract
In order to achieve rapid and effective identification of Hebei yam, a qualitative discrimination model was constructed based on near infrared (NIR), mid infrared (MIR), and microscopic Raman spectra in combination with individual spectra and multispectral data fusion strategies. The results showed that the gray wolf optimizer-support vector machine (GWO-SVM) model constructed by mid-level fusion using the three feature spectra performed the best in distinguishing the geographic origin of the yam, with a prediction accuracy of 100.00% in both the training set and the test set, and an F1 score of 1.00. The results indicated that due to spectral complementarity, NIR, MIR and Raman combined with feature-level fusion can be used as a powerful, non-destructive, fast and feasible tool for geographic origin classification and brand protection of Hebei yam. This work is expected to be a potential method for origin identification analysis and quality monitoring in the food and pharmaceutical industries.
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