Rapid and label-free detection of aflatoxin B1 in peanut oil using surface-enhanced Raman spectroscopy combined with deep learning models

This study presents a novel approach for rapid, label-free and sensitive detection of Aflatoxin B1 (AFB1) in peanut oil using Surface-Enhanced Raman Spectroscopy (SERS) combined with deep learning models. Silver-coated gold nanoparticles (Au@Ag NPs) were synthesized as SERS substrate. A confocal Raman spectrometer was used to acquire the Raman spectra of the AFB1 spiked peanut oil samples. Then, the collected SERS spectral data were augmented and preprocessed to improve the regression model's generalization capabilities. A total of six regression models including Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), Random Forest Regression (RFR), Convolutional Neural Networks (CNN), Long Short-Term Memory Networks (LSTM), and a combined CNN-LSTM model were developed. The results revealed that CNN-LSTM model efficiently captured complex non-linear relationships, reduced reliance on parameter adjustment and minimized overfitting. It also handled large-scale datasets effectively, reducing the computational load. CNN-LSTM model achieved excellent predictive performance with determination of coefficient (R2 P) = 0.9892, root mean square error of prediction (RMSEP) = 0.2104, ratio of performance to deviation (RPD) = 6.8723 and excellent sensitivity (LOD = 0.31 mu g/kg). These findings demonstrate the proposed method provides rapid, label-free, and efficient AFB1 detection in peanut oil, with significant potential for real-time monitoring application.