Institute of Sciences of Food Production, National Research Council, 70126 Bari, Italy.
Food Addit Contam Part A Chem Anal Control Expo Risk Assess. 2009 Jun;26(6):907-17. doi: 10.1080/02652030902788946.
Fourier transform near-infrared spectroscopy (FT-NIR) was used for rapid and non-invasive analysis of deoxynivalenol (DON) in durum and common wheat. The relevance of using ground wheat samples with a homogeneous particle size distribution to minimize measurement variations and avoid DON segregation among particles of different sizes was established. Calibration models for durum wheat, common wheat and durum + common wheat samples, with particle size <500 microm, were obtained by using partial least squares (PLS) regression with an external validation technique. Values of root mean square error of prediction (RMSEP, 306-379 microg kg(-1)) were comparable and not too far from values of root mean square error of cross-validation (RMSECV, 470-555 microg kg(-1)). Coefficients of determination (r(2)) indicated an "approximate to good" level of prediction of the DON content by FT-NIR spectroscopy in the PLS calibration models (r(2) = 0.71-0.83), and a "good" discrimination between low and high DON contents in the PLS validation models (r(2) = 0.58-0.63). A "limited to good" practical utility of the models was ascertained by range error ratio (RER) values higher than 6. A qualitative model, based on 197 calibration samples, was developed to discriminate between blank and naturally contaminated wheat samples by setting a cut-off at 300 microg kg(-1) DON to separate the two classes. The model correctly classified 69% of the 65 validation samples with most misclassified samples (16 of 20) showing DON contamination levels quite close to the cut-off level. These findings suggest that FT-NIR analysis is suitable for the determination of DON in unprocessed wheat at levels far below the maximum permitted limits set by the European Commission.
傅里叶变换近红外光谱（FT-NIR）用于快速和非侵入式分析脱氧雪腐镰刀菌烯醇（DON）在硬质小麦和普通小麦中的含量。通过使用具有均匀粒度分布的磨碎小麦样品，建立了最小化测量变化并避免不同大小颗粒之间 DON 分离的相关性。使用偏最小二乘（PLS）回归和外部验证技术，获得了粒径<500 微米的硬质小麦、普通小麦和硬质小麦+普通小麦样品的校准模型。预测均方根误差（RMSEP，306-379μgkg-1）的值相当，且与交叉验证均方根误差（RMSECV，470-555μgkg-1）相差不远。决定系数（r2）表明，FT-NIR 光谱在 PLS 校准模型中对 DON 含量的预测具有“近似良好”的水平（r2=0.71-0.83），并且在 PLS 验证模型中对低和高 DON 含量的区分具有“良好”的水平（r2=0.58-0.63）。通过范围误差比（RER）值高于 6，确定了模型的“有限到良好”实际效用。基于 197 个校准样本，开发了一种定性模型，通过在 300μgkg-1 DON 处设置截止值来区分空白和自然污染的小麦样品，从而将这两个类别分开。该模型正确分类了 65 个验证样本中的 69%，其中大多数错误分类的样本（20 个中的 16 个）显示 DON 污染水平非常接近截止水平。这些发现表明，FT-NIR 分析适用于在远远低于欧盟委员会设定的最大允许限量的水平下测定未经加工的小麦中的 DON。
