The authentication of Ziziphi Spinosae Semen (ZSS), Ziziphi Mauritianae Semen (ZMS), and Hovenia Acerba Semen (HAS) has become challenging. The chromatic and textural properties of ZSS, ZMS, and HAS are analyzed in this study. Color features were extracted via RGB, CIELAB, and HSI spaces, whereas texture information was analyzed via the gray-level co-occurrence matrix (GLCM) and Law’s texture feature analysis. The results revealed significant differences in color and texture among the samples. The fire–ice ion dimensionality reduction algorithm effectively fuses these features, enhancing their differentiation ability. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) confirmed the algorithm’s effectiveness, with variable importance in projection analysis (VIP analysis) (VIP > 1, p < 0.05) highlighting significant differences, particularly for the fire value, which is a key factor. To further validate the reliability of the algorithm, Back Propagation Neural Network (BP), Support Vector Machine (SVM), Deep Belief Network (DBN), and Random Forest (RF) were used for reverse validation, and the accuracy of the training set and test set reached 98.83–100% and 95.89–99.32%, respectively. The method provides a simple, low-cost, and high-precision tool for the fast and nondestructive detection of food authenticity.

Chinese wolfberry (CW) is a medicinal-food homologous fruit. Key mineral elements are important indicators of the nutritional quality and geographical authenticity of CW. Rapid and non-destructive evaluation of key mineral elements remains challenging because inductively coupled plasma mass spectrometry (ICP-MS) is accurate but destructive and laboratory-dependent. In this study, 120 CW batches from Ningxia, Qinghai, Gansu, and Xinjiang were analyzed by combining ICP-MS reference measurements of Cu, Fe, Mn, and Zn with standardized RGB image features. Visible-color proxy curves were reconstructed from RGB/L*a*b* information and Gaussian fitting, and frequency-domain texture descriptors were extracted from Fourier-derived angle–energy curves. Multivariate analysis showed that mineral profiles, the 570–593 nm visible-color proxy band, and the 0–40° and 63–140° texture-angle ranges contributed to regional differentiation. A Dung Beetle Optimizer-radial basis function (DBO-RBF) regression model was then used for internal prediction of mineral element contents and compared with nine baseline regression algorithms. DBO-RBF achieved test-set R² values of 0.949, 0.965, 0.951, and 0.913 for Cu, Fe, Mn, and Zn, respectively, with corresponding test RPD values of 2.9017, 3.2419, 3.0175, and 2.8416. These results indicate that image-derived visible-color and texture features can provide useful screening information for mineral quality assessment in CW, offering a rapid, low-cost, and non-destructive approach for preliminary batch evaluation.