Almoujahed Muhammad Baraa, Apolo-Apolo Orly Enrique, Alhussein Mohammad, Kazlauskas Marius, Kriaučiūnienė Zita, Šarauskis Egidijus, Mouazen Abdul Mounem
Department of Environment, Faculty of Bioscience Engineering, Ghent University, 9000 Ghent, Belgium.
Molecular Phytopathology and Mycotoxin Research, University of Göttingen, 37077 Göttingen, Germany.
Spectrochim Acta A Mol Biomol Spectrosc. 2025 Apr 5;330:125718. doi: 10.1016/j.saa.2025.125718. Epub 2025 Jan 7.
Contamination of wheat by the mycotoxin Deoxynivalenol (DON), produced by Fusarium fungi, poses significant challenges to the quality of crop yield and food safety. Visible and near-infrared (vis-NIR) spectroscopy has emerged as a promising, non-destructive, and efficient tool for detecting mycotoxins in cereal crops and foods. This study aims to utilize vis-NIR spectroscopy, coupled with a feature selection technique and machine learning modelling, to predict and classify DON contamination in wheat kernels and flour. A total of ninety-five samples, collected from commercial wheat fields in Lithuania and Belgium, were scanned using a vis-NIR (400-1650 nm) spectrophotometer. The DON content was subsequently determined by a liquid chromatography-mass spectrometry (LC-MS). The data were preprocessed and analyzed using random forest classifier (RFC), and regressor (RFR), extra trees classifier (ETC), and regressor (ETR), AdaBoost classifier (ABC), and regressor (ABR) for classification and regression tasks, respectively. To enhance model accuracy, recursive feature elimination (RFE) algorithm to select the most informative wavebands was applied, and the random over-sampler (ROS) was adopted to mitigate the imbalance of data in DON classes. Results showed that the feature selection approach improved the prediction and classification accuracy of the models. Notably, the performance of the algorithms was better for the flour samples compared to the kernels. The most effective DON prediction model was achieved with the ETR-RFE modelling approach for the flour samples, demonstrating high accuracy [determination coefficient (R) = 0.94 and root mean square error of prediction (RMSEP) = 3.42 mg.kg]. On the other hand, the ETC applied to the full spectrum data along with ROS, achieved the highest classification accuracy of 89.5 %. These results demonstrate the potential of using vis-NIR with RF-RFE modelling approach, for rapid analysis of DON levels in wheat kernel and flour.
镰刀菌产生的脱氧雪腐镰刀菌烯醇（DON）对小麦的污染给作物产量质量和食品安全带来了重大挑战。可见-近红外（vis-NIR）光谱已成为一种有前景的、非破坏性且高效的工具，用于检测谷物作物和食品中的霉菌毒素。本研究旨在利用vis-NIR光谱，结合特征选择技术和机器学习建模，对小麦籽粒和面粉中的DON污染进行预测和分类。从立陶宛和比利时的商业麦田采集了总共95个样本，使用vis-NIR（400 - 1650 nm）分光光度计进行扫描。随后通过液相色谱-质谱联用（LC-MS）测定DON含量。分别使用随机森林分类器（RFC）和回归器（RFR）、极端随机树分类器（ETC）和回归器（ETR）、自适应增强分类器（ABC）和回归器（ABR）对数据进行预处理和分析，以分别完成分类和回归任务。为提高模型准确性，应用递归特征消除（RFE）算法选择最具信息性的波段，并采用随机过采样器（ROS）来缓解DON类别中数据的不平衡。结果表明，特征选择方法提高了模型的预测和分类准确性。值得注意的是，与籽粒相比，算法对面粉样本的性能更好。对于面粉样本，采用ETR - RFE建模方法实现了最有效的DON预测模型，显示出高精度［决定系数（R）= 0.94，预测均方根误差（RMSEP）= 3.42 mg·kg］。另一方面，将ETC应用于全光谱数据并结合ROS，实现了最高分类准确率89.5%。这些结果证明了使用vis-NIR与RF - RFE建模方法快速分析小麦籽粒和面粉中DON水平的潜力。
