The deliberate pork adulteration with lymph nodes is a common adulteration phenomenon, and it poses a serious threat to public health and food safety. An untargeted metabolomics and lipidomics approach based on ultrahigh performance liquid chromatography coupled with linear ion trap quadrupole-Orbitrap high resolution mass spectrometry (MS) was used to distinguish lymph nodes from minced pork. The principal component analysis and orthogonal projection to latent structures discriminant analysis models were established with the good of fitness and predictivity. The results showed that there were significant differences in metabolites and lipids between lymph nodes and pork. A total of 16 significantly differentiated metabolites were identified, of which 1-palmitoylglycerophosphocholine, 12,13-dihydroxy-9-octadecenoic acid, and prostaglandin E₂ (PGE₂) were positively correlated with lymph node content and were identified as potential markers of lymph nodes. These three markers were combined to create a binary logistic regression model, and a combined-factor exceeding 0.75 was ultimately identified as a marker for pork adulteration with lymph nodes. The desorption electrospray ionization-MS images showed that PGE₂ had a higher relative abundance in the lymph node region than in adjacent non-lymph node regions, indicating that PGE₂ was a marker that contributed significantly for identifying lymph nodes adulteration into pork. Our results provide a theoretical basis for identifying lymph node adulteration, which will contribute to combating fraud in the meat industry.

中文翻译：

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基于UPLC/LTQ-Orbitrap MS的综合代谢组学和脂质组学方法鉴定猪肉碎中的淋巴结掺假

猪肉故意掺假淋巴结是一种常见的掺假现象，对公众健康和食品安全构成严重威胁。采用基于超高效液相色谱与线性离子阱四极杆 Orbitrap 高分辨率质谱 (MS) 相结合的非靶向代谢组学和脂质组学方法来区分淋巴结和猪肉末。建立了主成分分析和正交投影潜在结构判别分析模型，具有良好的拟合度和预测性。结果表明，淋巴结和猪肉的代谢物和脂质存在显着差异。共鉴定出16种显着差异化代谢物，其中1-棕榈酰甘油磷酸胆碱、12,13-二羟基-9-十八碳烯酸和前列腺素E ₂ (PGE ₂ )与淋巴结含量呈正相关，被确定为潜在的淋巴标志物节点。将这三个标记组合起来创建二元逻辑回归模型，并且组合因子超过0.75最终被确定为猪肉淋巴结掺假的标记。解吸电喷雾电离-MS图像显示，PGE ₂在淋巴结区域的相对丰度高于邻近非淋巴结区域，表明PGE ₂是对识别猪肉淋巴结掺假有显着贡献的标记物。我们的研究结果为识别淋巴结掺假提供了理论基础，这将有助于打击肉类行业的欺诈行为。