Department of Animal Nutrition, Grasslands and Forages, Regional Institute for Research and Agro-Food Development, SERIDA, 33300 Villaviciosa, Spain.
Talanta. 2009 Nov 15;80(1):54-60. doi: 10.1016/j.talanta.2009.06.025. Epub 2009 Jun 17.
To guarantee feed quality and safety the development and improvement of analytical methods for feed authentication and detection of contaminants is fundamental. Near infrared reflectance microscopy (NIRM) has been investigated as an alternative method to contribute to control systems for feed materials. The major task is the need to build NIRM reference spectral libraries that must represent the variability in feed ingredients. The aim of the present work was to evaluate the performance of a NIRM reference spectral library on animal feed, with external samples of animal feed ingredients and possible contaminants such as processed animal proteins, and in particular to assess its ability to identify ingredients in mixtures. Three external sample sets were used: (A) artificial mixtures, (B) synthetic mixtures and (C) synthetic binary mixtures. The prediction and repeatability results for set A, in which the spectra are from pure ingredients, were very good for both animal and vegetable ingredients and confirm that the spectral library is very good at identifying spectra from pure ingredients. For sets B and C, in which the spectra were measured on mixtures, the prediction results were very disappointing compared with the artificial samples. This means that a strategy that tries to match the spectra taken from a mixture with those of pure ingredients is unlikely to meet with much success. It is possible that an interpolation between pure ingredients for suitably chosen spectral ranges may provide a way to extend this system to mixtures, including mixtures of several ingredients.
为了保证饲料质量和安全，开发和改进饲料鉴定和污染物检测的分析方法是基础。近红外反射显微镜（NIRM）已被研究作为一种替代方法，以促进饲料材料控制系统。主要任务是需要建立代表饲料成分变化的 NIRM 参考光谱库。本工作的目的是评估动物饲料的 NIRM 参考光谱库的性能，使用动物饲料成分的外部样品和可能的污染物，如加工动物蛋白，并特别评估其识别混合物中成分的能力。使用了三个外部样品集：（A）人工混合物，（B）合成混合物和（C）合成二元混合物。对于包含纯成分的光谱的集合 A，其预测和重复性结果对于动物和植物成分都非常好，这证实了光谱库非常善于识别纯成分的光谱。对于包含混合物上测量的光谱的集合 B 和 C，与人工样品相比，预测结果非常令人失望。这意味着，一种试图将从混合物中获取的光谱与纯成分的光谱进行匹配的策略不太可能取得很大成功。在适当的光谱范围内对纯成分进行插值可能是将该系统扩展到包括多种成分的混合物的一种方法。
