Interpretable Small-Sample Hyperspectral Phenotyping of Wheat Protein Fractions via Order-Optimized Derivative Preprocessing and TabPFN

Rapid phenotyping of water- and salt-soluble wheat protein fractions can support grain quality evaluation and breeding. Whole-wheat flour from water-nitrogen and water-fertilizer trials was used to predict measured albumin and globulin contents and their derived sum, termed the metabolic-protein sum (albumin + globulin). After systematically comparing various preprocessing, wavelength selection, and regression approaches, a framework combining order-optimized derivative preprocessing (ODP), a hybrid feature-selection approach based on minimum redundancy maximum relevance (MRMR) and recursive feature elimination (RFE) and a tabular prior-data fitted network (TabPFN) regressor provided consistently strong test-set performance. The optimal derivative orders were 1.0 for albumin and the metabolic-protein sum and 0.8 for globulin. The ODP-MRMR-RFE-TabPFN framework retained 147, 46, and 70 wavelengths for albumin, globulin, and the metabolic-protein sum, yielding test set coefficient of determination (R2) values of 0.847, 0.693, and 0.850; root mean square error (RMSE) values of 3.073, 0.951, and 3.368 mg g−1; and residual prediction deviation (RPD) values of 2.554, 1.804, and 2.586, respectively. Leave-one-cultivar-out validation revealed substantial variation in predictive performance across held-out cultivars, with R2 and RPD ranging from 0.282 to 0.736 and from 1.180 to 1.945, respectively, for albumin, and from 0.416 to 0.750 and from 1.308 to 2.001, respectively, for globulin. Thus, although albumin achieved quantitative-level performance under the random train-test split, this performance was not consistently maintained for unseen cultivars, whereas globulin was generally more suitable for screening. SHapley additive explanations (SHAP) and partial dependence plots (PDPs) revealed that visible wavelengths contributed strongly to all models, with near-infrared (NIR) wavelengths supplying complementary information for globulin and the metabolic-protein sum. Overall, this framework offers rapid, interpretable flour-based estimation of metabolically active wheat protein fractions.