Abstract
Abstract
This study aimed to provide an overview of the strategy of meat quality biomarkers identification from protein profiling to the establishment of putative protein biomarkers with a focus on beef tenderness and colour traits. Further, the current knowledge gained by data-integration, also known as integromics, of published meat proteomics studies during the last decade is briefly discussed in terms of the current list of protein candidate biomarkers revealed using different proteomics platforms and evaluated by proteomics-based approaches. The main biochemical pathways underlying the determination of tenderness and colour traits as important beef eating qualities revealed by bioinformatics analyses such as Gene Ontology annotations, pathway and process enrichments are further considered. This paper also addresses the potential of integromics and data-mining, in the era of big data and data analytics, to broaden our knowledge on the biochemical mechanisms underlying the conversion of muscle into meat and the consequences on beef sensory quality traits (tenderness and colour). Finally, the emerging interest of using such gathered and shortlisted protein biomarkers for first validation and then early post-mortem prediction of the potential quality of beef carcasses is highlighted.
Reference46 articles.
1. Drivers of Consumer Liking for Beef, Pork, and Lamb: A Review;Miller;Foods,2020
2. Consumer perception and the role of science in the meat industry;Troy;Meat science,2010
3. Improving beef color stability: practical strategies and underlying mechanisms;Suman;Meat science,2014
4. Identification of Biomarkers Associated with the Rearing Practices, Carcass Characteristics, and Beef Quality: An Integrative Approach;Gagaoua;Journal of Agricultural and Food Chemistry,2017
5. Clustering of sensory eating qualities of beef: Consistencies and differences within carcass, muscle, animal characteristics and rearing factors;Gagaoua;Livestock Science,2018
Cited by
8 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献