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

Yizhi Luo, Qingting Jin, Huazhong Lu, Peng Li, Guangjun Qiu, Haijun Qi, Bin Li and Xingxing Zhou ()
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Yizhi Luo: Institute of Facility Agriculture, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, China
Qingting Jin: College of Electronic Engineering, South China Agricultural University, Guangzhou 510642, China
Huazhong Lu: Institute of Facility Agriculture, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, China
Peng Li: School of Computer and Information Engineering, Fuyang Normal University, Fuyang 236037, China
Guangjun Qiu: Institute of Facility Agriculture, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, China
Haijun Qi: Institute of Facility Agriculture, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, China
Bin Li: Institute of Facility Agriculture, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, China
Xingxing Zhou: Institute of Facility Agriculture, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, China

Agriculture, 2025, vol. 15, issue 3, 1-19

Abstract: 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.

Keywords: total soluble solids content; loquat; near-infrared spectroscopy; explainable artificial intelligence (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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