Prediction of Total Soluble Solids Content in Loquat Based on Hyperspectral Imaging and Interpretable Deep Learning

Loquat (Eriobotrya japonica) is a commercially important subtropical fruit, and its internal sweetness is an important indicator of market quality. Accurate and non-destructive determination of total soluble solids content (TSSC) is therefore essential for fruit grading and quality evaluation. In this study, short-wave infrared hyperspectral imaging (1000–2400 nm) was combined with a multi-scale spectral attention adaptive convolutional neural network (MSSA-ACNN) for rapid TSSC prediction. Spectral data were preprocessed using an SG-MSC-DT strategy to reduce noise and scattering effects, while conventional models (PLSR, Ridge, and SVM) were used for comparison. The proposed model combines multi-scale feature extraction with a dual-path attention mechanism, enabling adaptive enhancement of informative chemical wavebands while suppressing irrelevant variations. Experimental results, rigorously validated through a 5-fold cross-validation strategy, demonstrated that the proposed approach achieved the best predictive performance, with an Rp2 of 0.942, RMSEP of 0.505, and RPD of 3.091, outperforming traditional methods. In addition, attention weight analysis revealed that the model mainly focused on spectral regions associated with water and carbohydrate absorption, indicating consistency between the learned features and known chemical information. These results suggest that the proposed method provides an effective and interpretable approach for non-destructive evaluation of loquat quality and shows potential for application in intelligent fruit grading systems.