Near-infrared spectroscopy based on colorimetric sensor array coupled with convolutional neural network detecting zearalenone in wheat

Wheat is a vital global cereal crop, but its susceptibility to contamination by mycotoxins can render it unusable. This study explored the integration of two novel non-destructive detection methodologies with convolutional neural network (CNN) for the identification of zearalenone (ZEN) contamination in wheat. Firstly, the colorimetric sensor array composed of six selected porphyrin-based materials was used to capture the olfactory signatures of wheat samples. Subsequently, the colorimetric sensor array, after undergoing a reaction, was characterized by its near-infrared spectral features. Then, the CNN quantitative analysis model was proposed based on the data, alongside the establishment of traditional machine learning models, partial least squares regression (PLSR) and support vector machine regression (SVR), for comparative purposes. The outcomes demonstrated that the CNN model had superior predictive performance, with a root mean square error of prediction (RMSEP) of 40.92