Non-destructive origin identification of millet based on the combination of NIRS and improved WOA-based feature wavelength selection

1 score all reaching 99.20%; compared with the full-spectrum LSSVM model, these metrics improved by 21.67%, 19.86%, 21.88%, and 20.87%, respectively. In addition, the effectiveness of the proposed IWOA feature wavelength selection method and CPO-LSSVM model was validated on public datasets. The research results demonstrate that the IWOA algorithm, while selecting an effective number of wavelengths, also improves the model's performance. The CPO-LSSVM model can rapidly and accurately identify the origin information of millet, achieving precise traceability of the millet's provenance while simultaneously providing a new reference for the origin identification of other agricultural products.