Fusarium head blight (FHB) is one of the most severe fungal diseases that reduces yield of cereal crops and degrades kernel quality with mycotoxins, which are harmful to human and animal health. The majority of FHB identification at post-harvest stage is through lab-based analysis, whilst effective it is a time consuming, expensive, and laborious process. Hence, a non-destructive, rapid, accurate, and robust method is required for FHB detection at post-harvest. This study explores the potential of visible near-infrared (vis-NIR) in the wavelength range from 400 to 1700 nm and the mid-infrared (MIR) in the wavenumber range from 4000 to 650 cm⁻¹ to predict FHB infection of wheat kernels and flour. A total of 143 ear samples (93 infected, and 50 healthy) were collected from an inoculated trial covering several winter wheat varieties. The collected spectral data was analysed with two different machine learning algorithms, namely, random forest (RF) and linear discriminant analysis (LDA). Both models produced a higher test accuracy of 96.6 % and 100 %, respectively, for the flour samples than that (e.g., 93.1 %) for the kernels, using the MIR spectroscopy. Recursive feature elimination (RFE) demonstrated notable improvements in accuracy of the vis-NIR for the kernels, with LDA model providing 100 % classification accuracy. While RFE failed to improve the accuracy of MIR-LDA models. The results highlight the effectiveness of vis-NIR and MIR spectroscopy with RFE and machine learning for classifying FHB in wheat kernel and flour samples.

赤霉病（FHB）是最严重的真菌病害之一，它会降低谷类作物的产量，并通过霉菌毒素降低籽粒质量，对人类和动物健康有害。收获后阶段的大部分 FHB 鉴定都是通过实验室分析进行的，但这是一个耗时、昂贵且费力的过程。因此，需要一种无损、快速、准确和稳健的方法来进行收获后的 FHB 检测。本研究探讨了波长范围 400 至 1700 nm 的可见近红外 (vis-NIR) 和波数范围 4000 至 650 cm ^{-1的中红外 (MIR)}预测小麦籽粒 FHB 感染的潜力和面粉。从涵盖多个冬小麦品种的接种试验中收集了总共 143 个穗样本（93 个感染的，50 个健康的）。使用两种不同的机器学习算法对收集的光谱数据进行分析，即随机森林（RF）和线性判别分析（LDA）。使用 MIR 光谱法，两种模型对面粉样品的测试精度分别高于对玉米粒的测试精度（例如 93.1%），分别为 96.6% 和 100%。递归特征消除 (RFE) 证明了内核的 vis-NIR 准确性显着提高，LDA 模型提供了 100% 的分类准确性。而RFE未能提高MIR-LDA模型的准确性。结果凸显了可见近红外和中红外光谱结合 RFE 和机器学习对小麦仁和面粉样品中的 FHB 进行分类的有效性。