Wang Jishi, Xu Lei, Xu Zhenzhen, Wang Yanyun, Niu Chune, Yang Shuming
Key Laboratory of Agro-food Safety and Quality, Institute of Quality Standard & Testing Technology for Agro-Products, Chinese Academy of Agricultural Sciences, Ministry of Agriculture and Rural Affairs, Beijing 100081, China.
College of Food Science and Nutritional Engineering, China Agricultural University, Beijing 100083, China.
Foods. 2020 Nov 24;9(12):1723. doi: 10.3390/foods9121723.
A untargeted metabolomics approach was proposed in this study based on ultra-high performance liquid chromatography quadrupole time-of-flight (UHPLC-QTOF) and rapid evaporative ionization mass spectrometry (REIMS) to discriminate lamb and mutton meat and investigate their subtle metabolic differences, considering the higher popularity of lamb meat than mutton in the market. Multivariate statistical analysis was performed for data processing in order to distinguish between the two sample types. A total of 42 potential metabolites (20 in positive and 22 in negative ion mode) were defined for UHPLC-QTOF analysis, which provided references for discriminating the two kinds of meat. Furthermore, three potential markers were tentatively identified using LC/MS data against chemical databases. In addition, 14 potential metabolites were putatively identified in negative ion mode using the LipidMaps database. Meanwhile, the data-driven soft independent modeling of class analogy (DD-SIMCA) model was established, which could rapidly differentiate non-pretreated lamb meat and mutton with 92% specificity, rendering REIMS a promising technique for meat identification.
考虑到羊肉在市场上比绵羊肉更受欢迎，本研究提出了一种基于超高效液相色谱四极杆飞行时间质谱（UHPLC-QTOF）和快速蒸发电离质谱（REIMS）的非靶向代谢组学方法，以区分羔羊肉和绵羊肉，并研究它们细微的代谢差异。为了区分这两种样品类型，对数据进行了多变量统计分析。通过UHPLC-QTOF分析共确定了42种潜在代谢物（正离子模式下20种，负离子模式下22种），为区分这两种肉提供了参考。此外，利用LC/MS数据与化学数据库比对，初步鉴定出三种潜在标志物。另外，使用LipidMaps数据库在负离子模式下推定鉴定出14种潜在代谢物。同时，建立了数据驱动的类类比软独立建模（DD-SIMCA）模型，该模型能够以92%的特异性快速区分未经预处理的羔羊肉和绵羊肉，这使得REIMS成为一种有前景的肉类鉴定技术。
