Applied Mycology Unit, Food Technology Department, University of Lleida, Agrotecnio Center, Av. Rovira Roure 191, 25198 Lleida, Spain.
Department of Chemistry, University of Lleida (UdL), Av. Rovira Roure, 191, Lleida 25198, Spain.
Food Chem. 2021 Mar 30;341(Pt 2):128206. doi: 10.1016/j.foodchem.2020.128206. Epub 2020 Sep 30.
The present study aimed to evaluate the use of hyperspectral imaging (HSI)-NIR spectroscopy to assess the presence of DON and ergosterol in wheat samples through prediction and classification models. To achieve these objectives, a first set of bulk samples was scanned by HSI-NIR and divided into two subsamples, one that was analysed for ergosterol and another that was analysed for DON by HPLC. This method was repeated for a second larger set to build prediction and classification models. All the spectra were pretreated and statistically processed by PLS and LDA. The prediction models presented a RMSEP of 1.17 mg/kg and 501 µg/kg for ergosterol and DON, respectively. Classification achieved an encouraging accuracy of 85.4% for an independent validation set of samples. The results confirm that HSI-NIR may be a suitable technique for ergosterol quantification and DON classification of samples according to the EU legal limit for DON.
本研究旨在评估高光谱成像（HSI）-近红外光谱技术通过预测和分类模型，用于评估小麦样品中 DON 和麦角甾醇含量的应用。为了实现这些目标，首先使用 HSI-NIR 对一批散装样品进行扫描，并将其分为两个子样本，一个用于分析麦角甾醇，另一个用于通过 HPLC 分析 DON。然后对第二批更大的样本重复该方法，以建立预测和分类模型。所有光谱均通过 PLS 和 LDA 进行预处理和统计处理。预测模型对麦角甾醇和 DON 的 RMSEP 分别为 1.17mg/kg 和 501μg/kg。对于 DON 分类，根据欧盟 DON 限量，对独立验证样本集的分类达到了 85.4%的令人鼓舞的准确性。结果证实，HSI-NIR 可能是一种适合用于根据欧盟限量对 DON 进行分类以及定量分析麦角甾醇的技术。
