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首页> 外文期刊>Talanta: The International Journal of Pure and Applied Analytical Chemistry >A methodology based on NIR-microscopy for the detection of animal protein by-products
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A methodology based on NIR-microscopy for the detection of animal protein by-products

机译：基于近红外显微镜的动物蛋白副产物检测方法

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摘要

This study develops a methodology based on NIR-microscopy analysis and chemometric tools for the detection of animal protein by-products in mixtures, such as compound feeds and mixtures of ingredients, using a library of animal meal by-products only. The proposed methodology is a two-step strategy which worked better than the SIMCA approach it was compared with. In the first step, animal particles are identified using one of two methods. a global or a local distance measure. In the second, K-nearest-neighbours (KNN) is used to discriminate between terrestrial and fish particles. The models were developed using a training set comprising 11,727 spectra of pure terrestrial meals and 5843 of fish meals. KNN using second derivative spectra and five neighbours correctly classifies 98.5% of these samples under cross-validation. The procedure was validated using two external datasets, one made up of mixtures of species (fish and bovine), and a second of commercial compound feeds. The results obtained confirm that the procedure is able to reliably detect the presence of animal meals, although further work would be needed to develop it into an accurate quantitative method.
机译：这项研究开发了一种基于NIR显微镜分析和化学计量学工具的方法，该方法仅使用动物粉副产物库检测混合物中的动物蛋白副产物，例如配合饲料和配料混合物。所提出的方法是一种两步策略，其效果优于被比较的SIMCA方法。第一步，使用两种方法之一识别动物颗粒。全局或局部距离度量。在第二个中，K近邻（KNN）用于区分陆地和鱼类颗粒。使用包含11727个纯陆地粉和5843个鱼粉的光谱的训练集开发了模型。使用二阶导数光谱和五个邻域的KNN在交叉验证下正确分类了这些样本的98.5％。使用两个外部数据集验证了该程序，其中一个是物种（鱼和牛）的混合物，另一个是商业化的复合饲料。获得的结果证实了该程序能够可靠地检测动物餐的存在，尽管还需要进一步的工作才能将其发展为准确的定量方法。

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