Quality and Maturity Detection of Korla Fragrant Pears via Integrating Hyperspectral Imaging with Multiscale CNN–LSTM

To address the limitations of single indices in comprehensively evaluating the quality of Korla fragrant pears, this study proposes the firmness-soluble solids ratio (FSR), defined as the ratio of average firmness (FI) to soluble solid content (SSC) for each individual fruit, as a novel index. Using 600 samples from five maturity stages with hyperspectral imaging (950-1650 nm), the dataset was split 4:1 by the SPXY algorithm. The findings demonstrated that FSR's effectiveness in quantifying the dynamic relationship between FI and SSC during maturation. The developed multiscale convolutional neural network-long short-term memory (MSCNN-LSTM) model achieved high prediction accuracy with determination coefficients of 0.8934 (FI), 0.8731 (SSC), and 0.8610 (FSR), and root mean square errors of 0.9001 N, 0.7976%, and 0.1676, respectively. All residual prediction deviation values exceeded 2.5, confirming model robustness. The MSCNN-LSTM showed superior performance compared to other benchmark models. Furthermore, the integration of prediction models with visualization techniques successfully mapped the spatial distribution of quality indices. For maturity discrimination, hyperspectral-based partial least squares discriminant analysis and linear discriminant analysis models achieved perfect classification accuracy (100%) under five-fold cross-validation across all five maturity stages. This work provides both a theoretical basis and a technical framework for non-destructive evaluation of comprehensive quality and maturity in Korla fragrant pears.