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Abstract：
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 μ g ∙ kg-1 and a coefficient of determination on the prediction (RP2) 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.
摘要：
小麦是全球重要的谷类作物,但是它对霉菌毒素污染的敏感性会使其无法使用。这项研究探索了两种新颖的无损检测方法与卷积神经网络（CNN）的集成，以识别小麦中的玉米赤霉烯酮（ZEN）污染。首先,由六种选定的卟啉基材料组成的比色传感器阵列用于捕获小麦样品的嗅觉特征。随后，比色传感器阵列，在经历了反应之后，具有近红外光谱特征。然后，基于数据提出了CNN定量分析模型，在建立传统机器学习模型的同时，偏最小二乘回归(PLSR)和支持向量机回归(SVR)，为了比较的目的。结果表明，CNN模型具有优越的预测性能，预测的均方根误差（RMSEP）为40.92μg·kg-1，预测的确定系数（RP2）为0.91。这些结果肯定了将比色传感器阵列与近红外光谱集成在评估小麦和潜在其他谷物安全性方面的潜力。此外，CNN可以有能力从光谱数据中自主学习和提取特征，使进一步的光谱分析，使其成为一个前瞻性的光谱工具。
