Cristea Gabriela, Voica Cezara, Feher Ioana, Puscas Romulus, Magdas Dana Alina
National Institute for Research, Development of Isotopic and Molecular Technologies, 67-103 Donat, 400293 Cluj-Napoca, Romania.
National Institute for Research, Development of Isotopic and Molecular Technologies, 67-103 Donat, 400293 Cluj-Napoca, Romania.
Meat Sci. 2022 Jul;189:108825. doi: 10.1016/j.meatsci.2022.108825. Epub 2022 Apr 9.
In this study 93 pork meat samples (tenderloin) were analyzed via isotope ratios mass spectrometry (δH, δO, δC) and inductively coupled plasma - Mass spectrometry (55 elements). The meat samples are coming from Romania and abroad. Those from Romania are originating from conventional farms and yard rearing system. The analytical results in conjunction with linear discriminant analysis (LDA) and artificial neural networks (ANNs) were used to assess: The geographical origin, and animal diet. The most powerful markers which could differentiate pork meat samples concerning the geographical origin were δO, terbium, and tin. The results of chemometric models showed that, along with C signature, rubidium concentration, and a few rare earth-elements (lanthanum, and cerium) were efficient to discriminate animal diet in a percent of 97.8% (initial classification) and 94.6% (cross-validation), respectively. Some of predictors for feeding regime differentiation by using LDA were identified also to be the best markers to distinguish corn-based diet by using ANNs (δC, Rb, La).
在本研究中，通过同位素比率质谱法（δH、δO、δC）和电感耦合等离子体质谱法（55种元素）对93份猪肉样本（里脊肉）进行了分析。这些肉类样本来自罗马尼亚及其他国家。罗马尼亚的样本来自传统农场和庭院饲养系统。结合线性判别分析（LDA）和人工神经网络（ANNs）的分析结果用于评估：地理来源和动物饮食。能够区分猪肉样本地理来源的最有效标志物是δO、铽和锡。化学计量模型的结果表明，除了碳同位素特征外，铷浓度以及一些稀土元素（镧和铈）分别以97.8%（初始分类）和94.6%（交叉验证）的准确率有效区分动物饮食。通过LDA确定的一些用于区分饲养方式的预测因子也是通过ANNs区分玉米型日粮的最佳标志物（δC、Rb、La）。
