To solve the problem of low accuracy of near-infrared diffuse reflectance spectroscopy for moldy apple core discrimination, this paper proposes a multi-factor nondestructive detection method for moldy apple core by fusing density feature with diffuse reflectance spectroscopy. Based on the spectral acquisition platform to obtain the diffuse reflectance spectrum (200-1 100 nm) information of 195 Fuji apples, the WLD-600 density meter was used to obtain apple density data. In addition, the standard normal variable transformation (SNV) was used to pre-process the spectral data. Moreover, the competitive adaptive reweighted sampling (CARS) and successive projection algorithm (SPA) were combined to extract the feature spectra related to moldy apple core. Four different models of moldy apple core discrimination were established by density, diffuse reflectance spectra, fusing density and diffuse reflectance spectra with partial least squares discriminant analysis (PLS-DA), Fisher’s discrimination, support vector machine (SVM) and least squares support vector machine (LS-SVM). The results showed that the overall discrimination rate of the model based on the fusion of density and feature spectra was higher than that of the model based on density and feature spectra in different moldy apple core discriminant models. The SVM model based on the fusion of density and feature spectra had the highest overall discrimination rate of 95.56%, while the overall discrimination rate of the SVM model based on density and feature spectra was 82.2% and 91.11% respectively. Hence, the fusion of density feature could further improve the discrimination accuracy of diffuse reflectance spectra for moldy apple core, and also provides the possibility of developing portable non-destructive testing equipment based on diffuse reflection detection principle for the integration of apple internal disease and quality.
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