Advancing Loquat Total Soluble Solids Content Determination by Near-Infrared Spectroscopy and Explainable AI

文献类型： 外文期刊

第一作者： Luo, Yizhi

作者： Luo, Yizhi;Lu, Huazhong;Qiu, Guangjun;Qi, Haijun;Li, Bin;Zhou, Xingxing;Jin, Qingting;Li, Peng

作者机构：

关键词： total soluble solids content; loquat; near-infrared spectroscopy; explainable artificial intelligence

期刊名称：AGRICULTURE-BASEL （ 影响因子：3.6； 五年影响因子：3.8 ）

ISSN：

年卷期： 2025 年 15 卷 3 期

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收录情况： SCI

摘要： TSSC is one of the most important factors affecting loquat flavor, consumer satisfaction, and market competitiveness. To improve the ability to assess the TSSC of loquats, a method leveraging near-infrared spectroscopy and explainable artificial intelligence was proposed. The 900-1700 nm near-infrared spectroscopy of 156 fresh loquat samples was collected and preprocessed using seven preprocessing techniques, significant wavelength extraction utilizing six feature methods to eliminate data redundancy. Linear and nonlinear models were employed to establish the relationship between the feature spectrum and TSSC, with a focus on comparing and analyzing prediction performance. The findings reveal that the combination of 26 spectral bands selected by SPA and the PLSR model yielded the best prediction outcomes (R = 0.9031, RMSEP = 0.6171, RPD = 2.2803). The contribution of key wavelengths can be obtained by SHAP, which explains differences in model prediction accuracy and provides a reference for the application of loquat TSSC determination.

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