Prediction of Quality and Ripeness in ‘Weidi’ and ‘Fengweimeigui’ Apricot–Plum Using Near-Infrared Spectroscopy and Machine Learning Analysis

To meet consumer demand for high-quality fruit and replace traditional subjective assessment methods, there is a growing interest in objective, quantitative, and non-destructive testing techniques within the agricultural and food industries. This study explores the integration of near-infrared (NIR) spectroscopy with machine learning for the quality detection of apricot–plum hybrids, aiming to provide a rapid and efficient technical approach. Two cultivars, ‘Fengweimeigui’ and ‘Weidi’, were selected for analysis. The relationships between various quality attributes were analyzed using analysis of variance (ANOVA) and Pearson correlation. Raw spectral data were preprocessed using Savitzky–Golay (SG) smoothing, and principal component analysis (PCA) was employed to reduce the high dimensionality of the spectral data. The scores of the first 15 principal components (PCs) were extracted as input features for the subsequent models. A comparative study was conducted between backpropagation neural network (BPNN) and support vector machine (SVM) models. The results indicated that during the color-break period, significant differences existed across all quality indicators except for dry matter content, with significant correlations observed among these parameters. The results demonstrated that BPNN achieved the best predictive performance for total phenols content, peel L*, peel b*, vitamin C content, flavonoids content, soluble solids content, soluble sugars content, and soluble protein content in ‘Weidi’ and ‘Fengweimeigui’ from the color-turning to the ripening stages. The RP2 values for these indicators were 0.968, 0.966, 0.950, 0.939, 0.939, 0.923, 0.921, and 0.905, respectively, with residual predictive deviation (RPD) values exceeding 3.0. These findings indicate that near-infrared (NIR) spectroscopy is a feasible tool for the rapid detection of plum–apricot quality. However, the model performance for Flesh a* requires further optimization. In conclusion, the combination of NIR spectroscopy and machine learning enables the rapid, efficient, and non-destructive quality assessment of plum–apricot hybrids, providing robust technical support for maturity prediction and quality control in commercial production.