RIKILT, Wageningen University and Research Centre, P.O. Box 230, 6700 AE Wageningen, The Netherlands.
J Agric Food Chem. 2012 Aug 22;60(33):8129-33. doi: 10.1021/jf302309t. Epub 2012 Aug 14.
Organic products tend to retail at a higher price than their conventional counterparts, which makes them susceptible to fraud. In this study we evaluate the application of near-infrared spectroscopy (NIRS) as a rapid, cost-effective method to verify the organic identity of feed for laying hens. For this purpose a total of 36 organic and 60 conventional feed samples from The Netherlands were measured by NIRS. A binary classification model (organic vs conventional feed) was developed using partial least squares discriminant analysis. Models were developed using five different data preprocessing techniques, which were externally validated by a stratified random resampling strategy using 1000 realizations. Spectral regions related to the protein and fat content were among the most important ones for the classification model. The models based on data preprocessed using direct orthogonal signal correction (DOSC), standard normal variate (SNV), and first and second derivatives provided the most successful results in terms of median sensitivity (0.91 in external validation) and median specificity (1.00 for external validation of SNV models and 0.94 for DOSC and first and second derivative models). A previously developed model, which was based on fatty acid fingerprinting of the same set of feed samples, provided a higher sensitivity (1.00). This shows that the NIRS-based approach provides a rapid and low-cost screening tool, whereas the fatty acid fingerprinting model can be used for further confirmation of the organic identity of feed samples for laying hens. These methods provide additional assurance to the administrative controls currently conducted in the organic feed sector.
有机产品的零售价格往往高于传统产品，这使得它们容易受到欺诈。在这项研究中，我们评估了近红外光谱（NIRS）作为一种快速、经济有效的方法来验证蛋鸡饲料的有机身份。为此，我们共测量了来自荷兰的 36 种有机和 60 种常规饲料样本。使用偏最小二乘判别分析（PLS-DA）建立了一个二分类模型（有机与常规饲料）。使用五种不同的数据预处理技术开发模型，通过 1000 次实现的分层随机重采样策略进行外部验证。与蛋白质和脂肪含量相关的光谱区域是分类模型中最重要的区域之一。基于直接正交信号校正（DOSC）、标准正态变量（SNV）以及一阶和二阶导数进行数据预处理的模型在灵敏度中位数（外部验证中为 0.91）和特异性中位数（SNV 模型的外部验证为 1.00，DOSC 和一阶和二阶导数模型为 0.94）方面提供了最成功的结果。基于同一组饲料样本的脂肪酸指纹图谱建立的先前模型提供了更高的灵敏度（1.00）。这表明基于 NIRS 的方法提供了一种快速且低成本的筛选工具，而脂肪酸指纹图谱模型可用于进一步确认蛋鸡饲料的有机身份。这些方法为有机饲料行业目前进行的管理控制提供了额外的保障。
