Rapid Detection of Deoxynivalenol in Soybeans by NIR Spectroscopy Coupled With ETs‐PLS Feature Selection Method

This study developed a hybrid feature selection approach combining Extra Trees (ETs) with partial least squares (PLS) regression for rapid and accurate deoxynivalenol (DON) quantification in soybeans using near‐infrared (NIR) spectroscopy. The ETs algorithm ranked spectral variables by importance, and the top 50 wavelengths were selected as inputs for the PLS model. The proposed ETs‐PLS method was compared with three conventional wavelength selection methods, and the ETs‐PLS achieved superior performance with R 2 P of 0.950 and RMSE P of 87.3 µg/kg, significantly outperforming full‐spectra PLS and all benchmark methods. The selected wavelengths were concentrated in the 1139–1183 nm region, corresponding to C─H stretching vibrations. This ETs‐PLS method offers an effective balance between model parsimony and predictive accuracy, demonstrating great potential for rapid and nonconsumptive mycotoxin screening in agricultural products.