外文摘要：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 mu g center dot kg-1 and a coefficient of determination on the prediction (R2P) of 0.91. These results affirmed the potential of integrating colorimetric sensor array with near-infrared spectroscopy in evaluating the safety of wheat and potentially other grains. Moreover, CNN can have the capacity to autonomously learn and distill features from spectral data, enabling further spectral analysis and making it a forward-looking spectroscopic tool.
外文关键词：wheat;Near-infrared spectroscopy;Convolutional Neural Network;Zearalenone;Colorimetric sensor array
作者：Deng, Jihong;Jiang, Hui;Zhao, Yongqin;Chen, Quansheng
作者单位：Jiangsu Univ;Jimei Univ
期刊名称：FOOD CHEMISTRY-X
期刊影响因子：0.0
出版年份：2024
出版刊次：22
原文传递申请：江苏省科技资源（工程技术文献）统筹服务平台