Zhang Tao, Zhang Shanshan, Chen Lan, Ding Hao, Wu Pengfei, Zhang Genxi, Xie Kaizhou, Dai Guojun, Wang Jinyu
College of Animal Science and Technology, Yangzhou University, Yangzhou 225009, China.
Joint International Research Laboratory of Agriculture and Agri-Product Safety, Ministry of Education, Yangzhou University, Yangzhou 225009, China.
Foods. 2020 Sep 20;9(9):1326. doi: 10.3390/foods9091326.
To identify metabolic biomarkers related to the freshness of chilled chicken, ultra-high-performance liquid chromatography-mass spectrometry (UHPLC-MS/MS) was used to obtain profiles of the metabolites present in chilled chicken stored for different lengths of time. Random forest regression analysis and stepwise multiple linear regression were used to identify key metabolic biomarkers related to the freshness of chilled chicken. A total of 265 differential metabolites were identified during storage of chilled chicken. Of these various metabolites, 37 were selected as potential biomarkers by random forest regression analysis. Receiver operating characteristic (ROC) curve analysis indicated that the biomarkers identified using random forest regression analysis showed a strong correlation with the freshness of chilled chicken. Subsequently, stepwise multiple linear regression analysis based on the biomarkers identified by using random forest regression analysis identified indole-3-carboxaldehyde, uridine monophosphate, s-phenylmercapturic acid, gluconic acid, tyramine, and Serylphenylalanine as key metabolic biomarkers. In conclusion, our study characterized the metabolic profiles of chilled chicken stored for different lengths of time and identified six key metabolic biomarkers related to the freshness of chilled chicken. These findings can contribute to a better understanding of the changes in the metabolic profiles of chilled chicken during storage and provide a basis for the further development of novel detection methods for the freshness of chilled chicken.
为了鉴定与冷藏鸡肉新鲜度相关的代谢生物标志物，采用超高效液相色谱-质谱联用仪（UHPLC-MS/MS）获取不同储存时长的冷藏鸡肉中代谢物的图谱。利用随机森林回归分析和逐步多元线性回归来鉴定与冷藏鸡肉新鲜度相关的关键代谢生物标志物。在冷藏鸡肉储存过程中总共鉴定出265种差异代谢物。在这些各异的代谢物中，通过随机森林回归分析筛选出37种作为潜在生物标志物。受试者工作特征（ROC）曲线分析表明，利用随机森林回归分析鉴定出的生物标志物与冷藏鸡肉的新鲜度具有很强的相关性。随后，基于随机森林回归分析鉴定出的生物标志物进行逐步多元线性回归分析，确定了吲哚-3-甲醛、尿苷单磷酸、S-苯巯基尿酸、葡萄糖酸、酪胺和丝氨酰苯丙氨酸为关键代谢生物标志物。总之，我们的研究描绘了不同储存时长的冷藏鸡肉的代谢图谱，并鉴定出六种与冷藏鸡肉新鲜度相关的关键代谢生物标志物。这些发现有助于更好地理解冷藏鸡肉储存期间代谢图谱的变化，并为进一步开发冷藏鸡肉新鲜度的新型检测方法提供依据。
