To assess the quality of apple samples during storage, this study proposes a spoilage benchmark based on hyperspectral data feature indicators and Mahalanobis distance (MD). Additionally, a quality assessment model is developed utilizing LIB Support Vector Machine (LIBSVM). Initially, a spoilage benchmark for apple samples is preliminarily established using hyperspectral data feature indicators, including the color feature, texture feature of sample hyperspectral image, and wavelet packet energy (WPE) of sample spectral information. Secondly, the study utilized the successive projection algorithm (SPA) to extract three wavelength sets sensitive to changes in the three indicators. This process resulted in the identification of 20 feature wavelengths based on the three sets. Subsequently, the spoilage benchmark for apple samples is verified using MD based on the spectral information of feature wavelengths. Ultimately, utilizing pre-processed spectral information enhanced by the sliding window algorithm and spoilage benchmark, the LIBSVM quality assessment model is developed, achieving a training set accuracy of 99.94 % and a test set accuracy of 99.66 %. Moreover, to assess the strength and applicability of the model, a verification experiment is conducted using a different set of apple samples. The training set accuracy is 100 %, and the test set accuracy is 99.83 %. The findings indicate that the model can effectively indicate the level of spoilage in each sample during storage. This also serves to demonstrate the robustness of the model and the effectiveness of the spoilage benchmark determination method during apple storage.

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