Internal potato diseases significantly affect the quality of processed products in production. In this study, a potato quality near-infrared online detection system was designed to improve detection efficiency. The proposed system consists of four parts: a transmission device, a spectral acquisition device, a light source system, and a culling device. To improve the accuracy of the detection model, this study used four preprocessing methods and three feature wavelength extraction algorithms to process the original spectra and develop the soft independent modeling of the class analogy (SIMCA) potato quality discrimination model. The results showed that the SIMCA potato quality identification model using mean-normalization preprocessing and competitive adaptive reweighted sampling (CARS) feature wavelength extraction algorithm was the most effective with sensitivity = 100.00%, specificity = 95.83%, and ACCP = 97.92%. The sensitivity = 100.00%, specificity = 90.48%, and ACCP = 95.24% were tested for the NIR online detection system. The results of this study show that the use of near-infrared reflectance spectroscopy combined with preprocessing algorithms and variable selection algorithms to construct discriminative models can achieve online detection of internal potato diseases.
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