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.