Department of Animal Production, Faculty of Agricultural and Forestry Engineering, University of Córdoba, Córdoba, Spain.
Appl Spectrosc. 2011 Jul;65(7):771-81. doi: 10.1366/10-06177.
This paper proposes a method based on near-infrared hyperspectral imaging for discriminating between terrestrial and fish species in animal protein by-products used in livestock feed. Four algorithms (Mahalanobis distance, Kennard-Stone, spatial interpolation, and binning) were compared in order to select an appropriate subset of pixels for further partial least squares discriminant analysis (PLS-DA). The method was applied to a set of 50 terrestrial and 40 fish meals analyzed in the 1000-1700 nm range. Models were then tested using an external validation set comprising 45 samples (25 fish and 20 terrestrial). The PLS-DA models obtained using the four subset-selection algorithms yielded a classification accuracy of 99.80%, 99.79%, 99.85%, and 99.61%, respectively. The results represent a first step for the analysis of mixtures of species and suggest that NIR-CI, providing valuable information on the origin of animal components in processed animal proteins, is a promising method that could be used as part of the EU feed control program aimed at eradicating and preventing bovine spongiform encephalopathy (BSE) and related diseases.
本文提出了一种基于近红外高光谱成像的方法，用于通过牲畜饲料中使用的动物蛋白副产物来区分陆生和鱼类物种。比较了四种算法（马氏距离、肯纳德-斯通、空间插值和分箱），以选择适当的像素子集进行进一步的偏最小二乘判别分析（PLS-DA）。该方法应用于一组在 1000-1700nm 范围内分析的 50 种陆生和 40 种鱼粉。然后使用包含 45 个样本（25 种鱼和 20 种陆生）的外部验证集对模型进行了测试。使用这四种子集选择算法获得的 PLS-DA 模型的分类准确率分别为 99.80%、99.79%、99.85%和 99.61%。结果代表了分析物种混合物的第一步，并表明近红外-CI 提供了有关加工动物蛋白中动物成分来源的有价值信息，是一种很有前途的方法，可以作为欧盟饲料控制计划的一部分，旨在根除和预防牛海绵状脑病（BSE）和相关疾病。
