Abstract This work evaluated the reliability of the multivariate statistical analysis to discriminate the feeding system and the species of ruminants using their intramuscular fatty acids (FA) profile. FA composition of 53 meat samples (longissimus dorsi muscle) from animals of different species (sheep and cattle) raised with different feeding systems (pasture and stall-fed) (4 groups overall) was determined and expressed as % fatty acid methyl ester (FAME). A stepwise discriminant analysis (SDA) was applied to the full set of FA to select the variables that best discriminated between feeding systems and animal species. The selected variables were then submitted to a canonical discriminant analysis (CDA) to test the ability of those variables in discriminating against the four groups. Discriminant analysis (DA) was then exploited to classify meat samples. From the 62 initial variables detected in the FA profile, 24 were retained in the SDA. The subsequent CDA developed by using the selected variables, significantly discriminated the four groups (Hotelling's test p < 0.0001) by extracting three canonical functions. Heptadecenoic acid C17:1 c10, seemed to play a pivotal role both in discriminating species and feeding system while some 18:1 isomers (C18:1 c12, C18:1 c13 C18:1 t13/t14) together with CLA c9, t11 and ω-3 were important in discriminating feeding systems. Multivariate statistical analysis of FA was able to track both the species and the feeding system of source animals with good accuracy. Highlights The increasing interest in the ‘green image’ of meat obtained from grass-based systems guides the search for methods to trace the animal feeding system. Extracting more information from the large amounts of meat data provided by laboratory equipment is of utmost importance. Multivariate statistical analysis is able to trace with good accuracy meat samples back to their animal species and feeding system origin.

中文翻译：

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判别分析作为一种工具来识别由牧场或畜栏饲养的动物生产的牛和绵羊肉

摘要 这项工作评估了多变量统计分析的可靠性，以使用肌肉内脂肪酸 (FA) 谱来区分饲养系统和反刍动物的种类。确定了来自不同物种（绵羊和牛）的动物（羊和牛）的 53 个肉样品（背最长肌）的 FA 组成，这些动物使用不同的饲养系统（牧场和畜栏饲养）（总共 4 组），并表示为脂肪酸甲酯 (FAME) 百分比）。逐步判别分析 (SDA) 应用于全套 FA 以选择最能区分饲养系统和动物物种的变量。然后将选定的变量提交给典型判别分析 (CDA)，以测试这些变量对四个组的判别能力。然后利用判别分析（DA）对肉类样品进行分类。在 FA 配置文件中检测到的 62 个初始变量中，有 24 个保留在 SDA 中。使用选定变量开发的后续 CDA 通过提取三个典型函数显着区分了四组（Hotelling 检验 p < 0.0001）。十七碳烯酸 C17:1 c10 似乎在区分物种和饲养系统方面发挥着关键作用，而一些 18:1 异构体（C18:1 c12、C18:1 c13 C18:1 t13/t14）与 CLA c9、t11 和ω-3 在区分饲养系统方面很重要。FA 的多变量统计分析能够以良好的准确性跟踪源动物的物种和饲养系统。亮点 对从基于草的系统中获得的肉类“绿色图像”的兴趣日益增加，这引导着寻找追踪动物饲养系统的方法。从实验室设备提供的大量肉类数据中提取更多信息至关重要。多变量统计分析能够准确地追溯肉类样品的动物种类和饲养系统来源。