Lipid analysis of meat from Bactrian camel (Camelus bacterianus), beef, and tails of fat-tailed sheep using UPLC-Q-TOF/MS based lipidomics

Front Nutr. 2023 Mar 2:10:1053116. doi: 10.3389/fnut.2023.1053116. eCollection 2023.

Abstract

Introduction: As a source of low-cost and high-quality meat for human beings, the consumption of camel meat was increasing, and beef has similar texture and nutritional characteristics with camel meat. Camel hump and fatty-tails are important parts of fat storage for camels and fat-tailed lambs, respectively, which were to adapt and endure harsh environments. Considering their similar physiological functions, their fat composition might be similar. Lipidomics is a system-level analysis of lipids method, which play an important role in the determination and quantification of individual lipid molecular specie, food adulteration and labeling.

Methods: A GC/MS was used to analyze fatty acids composition of Xinjiang Bactrian camel meat, hump, beef, and fatty-tails. UPLC-Q-TOF/MS based on lipidomics approach was used to analyze lipid composition, characterize and examine the lipid differences in Xinjiang Bactrian camel meat, hump, beef, and fatty-tails.

Results and discussion: The major fatty acids of the four samples were C16:0, C18:0, and C18:1cis, and camel meat had a significant low SFA content and high MUFA content. A total of 342 lipid species were detected, 192, 64, and 79 distinguishing lipids were found in the groups camel hump compared to camel meat, camel meat compared to beef, and camel hump compared to fatty-tails, respectively. Lipid metabolisms of ether lipid, glycerophospholipid, glycerolipid, and sphingolipid were the most influential pathways revealed by KEGG analysis. The results contributed to enrich the lipid information of Bactrian camel meat, and indicated that UPLC-Q-TOF/MS based on lipidomics was an alternative method to distinguish meat samples.

Keywords: beef; camel meat; fatty acid; fatty-tails; hump; lipid metabolism; lipidomics.

Grants and funding

This work was supported by National Key Research and Development Project of China, China (Grant Number 2019YFC1606103), Key Technology Research and Development Program in Autonomous Region, China (Grant Number 2018B01003), and Natural Science Foundation of Xinjiang, China (Grant Number 2022D01C44).