Predicting and classifying deoxynivalenol in wheat flour using ATR-FTIR spectroscopy and explainable machine learning

Deoxynivalenol (DON) is a major hazardous component in wheat kernels infected by Fusarium head blight. Given the lack of both reliable and cost-effective quantification methods, this study aims to use Fourier-transform infrared spectroscopy with attenuated total reflectance (ATR-FTIR) for predicting DON concentrations. Four machine learning models, including eXtreme Gradient Boosting (XGBoost), Random Forest (RF), Tabular Prior-data Fitted Network (TabPFN), and Categorical Boosting (CatBoost), were firstly developed using key spectral features selected by recursive feature elimination (RFE). TabPFN model outperformed others in quantitative prediction, achieving a coefficient of determination (R²) of 0.86 on hold-out test set. SHapley Additive exPlanations (SHAP) were firstly applied to enhance model interpretability and transparency, identifying wavenumbers at 996 cm -1 , 1083 cm -1 , 1135 cm -1 , and 1574 cm -1 as having higher influence on model performance. For binary classification of DON levels (