Departamento de Patología Animal, Facultad de Veterinaria, Universidade de Santiago de Compostela, 27002 Lugo, Spain.
Instituto de Investigación e Análises Alimentarias (IIAA), Departamento de Química Analítica, Nutrición e Bromatoloxía, Facultade de Ciencias, Universidade de Santiago de Compostela, 27002 Lugo, Spain.
Food Chem. 2018 Oct 30;264:210-217. doi: 10.1016/j.foodchem.2018.05.044. Epub 2018 May 9.
An authentication procedure for differentiating between organic and non-organic cattle production on the basis of analysis of serum samples has been developed. For this purpose, the concentrations of fourteen mineral elements (As, Cd, Co, Cr, Cu, Fe, Hg, I, Mn, Mo, Ni, Pb, Se and Zn) in 522 serum samples from cows (341 from organic farms and 181 from non-organic farms), determined by inductively coupled plasma spectrometry, were used. The chemical information provided by serum analysis was employed to construct different pattern recognition classification models that predict the origin of each sample: organic or non-organic class. Among all classification procedures considered, the best results were obtained with the decision tree C5.0, Random Forest and AdaBoost neural networks, with hit levels close to 90% for both production types. The proposed method, involving analysis of serum samples, provided rapid, accurate in vivo classification of cattle according to organic and non-organic production type.
已经开发出一种基于血清样本分析区分有机和非有机牛生产的认证程序。为此，使用电感耦合等离子体光谱法测定了 522 份来自奶牛（341 份来自有机农场，181 份来自非有机农场）血清样本中的 14 种矿物质元素（As、Cd、Co、Cr、Cu、Fe、Hg、I、Mn、Mo、Ni、Pb、Se 和 Zn）的浓度。采用血清分析提供的化学信息来构建不同的模式识别分类模型，以预测每个样本的来源：有机或非有机类别。在所考虑的所有分类程序中，决策树 C5.0、随机森林和 AdaBoost 神经网络的结果最好，两种生产类型的准确率都接近 90%。该方法涉及血清样本分析，可根据有机和非有机生产类型快速、准确地对牛进行体内分类。
