Color-Sensitive Sensor Array Combined with Machine Learning for Non-Destructive Detection of AFB1 in Corn Silage

Aflatoxin B1 (AFB1) contamination in corn silage poses significant risks to livestock and human health. This study developed a non-destructive detection method for AFB1 using color-sensitive arrays (CSAs). Twenty self-developed CSAs were employed to react with samples, with reflectance spectra collected using a portable spectrometer. Spectral data were optimized through seven preprocessing methods, including Standard Normal Variate (SNV), Multiplicative Scatter Correction (MSC), first-order derivative (1st D), second-order derivative (2nd D), wavelet denoising, and their combinations. Key variables were selected using five feature selection algorithms: Competitive Adaptive Reweighted Sampling (CARS), Principal Component Analysis (PCA), Random Forest (RF), Uninformative Variable Elimination (UVE), and eXtreme Gradient Boosting (XGBoost). Five machine learning models were constructed: Light Gradient Boosting Machine (LightGBM), XGBoost, Support Vector Regression (SVR), RF, and K-Nearest Neighbor (KNN). The results demonstrated significant AFB1-responsive characteristics in three dyes: (2,3,7,8,12,13,17,18-octaethylporphynato)chloromanganese(III) (Mn(OEP)Cl), Bromocresol Green, and Cresol Red. The combined 1st D-PCA-KNN model showed optimal prediction performance, with determination coefficient (Rp2 = 0.87), root mean square error (RMSEP = 0.057), and relative prediction deviation (RPD = 2.773). This method provides an efficient solution for silage AFB1 monitoring.