Cristea Gabriela, Covaciu Florina-Dorina, Feher Ioana, Puscas Romulus, Voica Cezara, Dehelean Adriana
National Institute for Research and Development of Isotopic and Molecular Technologies, 67-103 Donat Street, 400293 Cluj-Napoca, Romania.
Foods. 2024 Oct 11;13(20):3240. doi: 10.3390/foods13203240.
The ability to trace the origin of eggs from backyard-raised hens is important due to their higher market value compared to barn-raised eggs. This study aimed to differentiate eggs from these two rearing systems using isotopic, elemental, and fatty acid profiles of egg yolks. A total of 90 egg yolk samples were analyzed, analytical results being followed by statistical tests (Student's -test) showing significant differences in δO, several elements (Mg, K, Sc, Mn, Fe, Ni, Cu, Zn, As, Cd, Ba, Pb), and fatty acids compositions (C23:0, C17:0, C18:0, C16:1n7, C18:1n9, C18:2n6, C20:1n7, C20:4n6, C20:5n3, C22:6n3), as well as in the ratios of SFA, PUFA, and UFA. The results indicated a nutritional advantage in backyard eggs due to their lower n-6 polyunsaturated fatty acid content and a more favorable n-6 to n-3 ratio, linked to differences in the hens' diet and rearing systems. To classify the production system (backyard vs. barn), three pattern recognition methods were applied: linear discriminant analysis (LDA), k-nearest neighbor (k-NN), and multilayer perceptron artificial neural networks (MLP-ANN). LDA provided perfect initial separation, achieving 98.9% accuracy in cross-validation. k-NN yielded classification rates of 98.4% for the training set and 85.7% for the test set, while MLP-ANN achieved 100% accuracy in training and 92.3% in testing, with minor misclassification. These results demonstrate the effectiveness of fusion among isotopic, elemental, and fatty acid profiles in distinguishing backyard eggs from barn eggs and highlight the nutritional benefits of the backyard-rearing system.
与笼养鸡蛋相比，追溯后院饲养母鸡所产鸡蛋的来源很重要，因为其市场价值更高。本研究旨在利用蛋黄的同位素、元素和脂肪酸谱来区分这两种饲养系统所产的鸡蛋。共分析了90个蛋黄样本，分析结果随后进行统计检验（学生t检验），结果显示在δO、几种元素（镁、钾、钪、锰、铁、镍、铜、锌、砷、镉、钡、铅）以及脂肪酸组成（C23:0、C17:0、C18:0、C16:1n7、C18:1n9、C18:2n6、C20:1n7、C20:4n6、C20:5n3、C22:6n3）以及饱和脂肪酸、多不饱和脂肪酸和单不饱和脂肪酸的比例方面存在显著差异。结果表明，后院鸡蛋具有营养优势，因为其n-6多不饱和脂肪酸含量较低，且n-6与n-3的比例更有利，这与母鸡的饮食和饲养系统差异有关。为了对生产系统（后院与笼养）进行分类，应用了三种模式识别方法：线性判别分析（LDA）、k近邻（k-NN）和多层感知器人工神经网络（MLP-ANN）。LDA提供了完美的初始分离，在交叉验证中准确率达到98.9%。k-NN在训练集上的分类率为98.4%，在测试集上为85.7%，而MLP-ANN在训练中的准确率为100%，在测试中为92.3%，错误分类较少。这些结果证明了同位素、元素和脂肪酸谱融合在区分后院鸡蛋和笼养鸡蛋方面的有效性，并突出了后院饲养系统的营养益处。
