Monitoring of the Physicochemical Properties and Aflatoxin of -Contaminated Peanut Kernels Based on Near-Infrared Spectroscopy Combined with Machine Learning.

This study explores the application of near-infrared (NIR) spectroscopy combined with machine learning for the non-destructive detection of aflatoxin in peanuts contaminated by (). The key innovation lies in the development of an optimized spectral processing pipeline that effectively overcomes moisture interference while maintaining high sensitivity to low aflatoxin concentrations. NIR spectra were collected from peanut samples at different incubation times within the spectral range of 950 to 1650 nm. Spectral data were preprocessed, and Competitive Adaptive Reweighted Sampling (CARS) selected ten characteristic bands. Correlation analysis was performed to examine the relationships between physicochemical properties, characteristic bands, and aflatoxin content. Three machine learning models-Backpropagation Neural Network (BPNN), Support Vector Machine (SVM), and Random Forest (RF)-were used to predict aflatoxin levels. The SNV-SVM model demonstrated superior performance, achieving calibration metrics (R = 0.9945, RMSE = 9.92, RPD = 14.59) and prediction metrics (R = 0.9528, RMSE = 19.58, RPD = 7.01), along with leave-one-out cross-validation (LOOCV) results (R = 0.9834, RMSE = 11.20). The results demonstrate that NIR spectroscopy combined with machine learning offers a rapid, non-destructive approach for aflatoxin detection in peanuts, with significant implications for food safety and agricultural quality control.