Simultaneous Detection for Storage Condition and Storage Time of Yellow Peach under Different Storage Conditions Using Hyperspectral Imaging with Multi-Target Characteristic Selection and Multi-Task Model

The detection for storage condition and storage time can ensure quality and safety of yellow peaches, and optimize storage management. This study explored the feasibility of simultaneously detecting the storage condition and storage time of yellow peach under different storage conditions using hyperspectral imaging technology combined with multi-target characteristic selection and multi-task model. Firstly, a total of 1080 hyperspectral images of 120 yellow peach samples under different storage conditions and different storage time were acquired using visible near-infrared hyperspectral imaging system. Subsequently, Savitzky-Golay first derivative was used to preprocess the raw spectra to improve the signal-to-noise ratio of the spectra. Distinguished index modified competitive adaptive reweighted sampling (DI-CARS) was proposed to select characteristic wavelengths that simultaneously characterize information of storage condition and storage time. Wild horse optimizer optimized support vector machine (WHO-SVM) was proposed for the multi-task modeling analysis. The results showed that multi-target characteristic selection and multi-task model not only reduced 66.67 % in computational cost, but also improved the accuracy, and multi-task WHO-SVM model based on multi-target DI-CARS achieved the optimal result with the overall accuracy reached 98.06 % and the number of characteristic wavelengths being 99. In conclusion, hyperspectral imaging combined with multi-target DI-CARS and multi-task WHO-SVM is feasible to simultaneously detect storage condition and storage time of yellow peach under different storage conditions.