Prediction of soybean fatty-acid composition from hyperspectral imaging with spectral feature processing and structured tabular modeling.

Abstract

Soybean fatty-acid composition is a key determinant of nutritional quality and industrial value, but conventional gas chromatography is destructive, labor-intensive, and time-consuming. This study combined hyperspectral imaging, which enables rapid and nondestructive acquisition of seed-surface spectral information, with the Tabular Prior-data Fitted Network (TabPFN) to predict the relative proportions of five major soybean fatty acids: palmitic, stearic, oleic, linoleic, and linolenic acids. Mean seed reflectance spectra extracted using three region-of-interest (ROI) strategies were subjected to preprocessing, comparison across representative models and feature-reduction strategies, and SHapley Additive exPlanations (SHAP) analysis to identify wavelength regions associated with fatty-acid variation. TabPFN achieved the best regression performance under partial least squares (PLS) reduction, with an overall R 2 of 0.9750, while all four classification metrics exceeded 0.93 under linear discriminant analysis (LDA). These results demonstrate an accurate, interpretable, and nondestructive framework for rapid prediction of soybean fatty-acid composition and quality evaluation.

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