Determination of zearalenone content in wheat by modified one-dimensional convolutional neural network-based Raman spectra

Abstract

In this study, a Raman spectral model trained based on Transformer-improved one-dimensional convolutional neural network (1D-CNN) was proposed for the quantitative analysis of zearalenone in wheat. Raman spectra of wheat flour samples with varying levels of mold contamination were acquired using a portable Raman spectrometer. The 1D-CNN was introduced to extract local features of Raman spectra using three convolutional layers, and then the Transformer module was employed to encode and decode these local features for obtaining global information. This approach ensured that the network could better learn complex input spectra data, thereby improving the detection accuracy and precision of the model. The results showed that the improved 1D-CNN model had better generalization performance compared to the 1D-CNN model, with a root mean square error of prediction (RMSEP) of 2.5759 μg/kg, a coefficient of determination (RP2) of 0.9837, and a relative prediction deviation (RPD) of 7.9516. The study demonstrated that the 1D-CNN model improved by Transformer could achieve better feature self-learning and multivariate model correction of Raman spectroscopy data. This research introduces a novel reference method for chemometric analysis of Raman spectra.