Selection of potential molecular markers for cheese ripening and quality prediction by NMR spectroscopy

Chen, Yangyi; MacNaughtan, William; Jones, Paul; Yang, Qian; Williams, Huw; Foster, Tim

Abstract

© 2020 Elsevier Ltd Predicting cheese quality as early as possible after ripening is important for quality control in the cheese industry. The main aim of this study was to investigate potential metabolites for predictive models of Cheddar cheese quality. Metabolites in aqueous extracts of Cheddar cheese were identified by Nuclear Magnetic Resonance. The metabolites were used to measure the kinetics of up to 450 days ripening in Cheddar cheese. The proton ratios of citrulline and arginine relative to the overall proton content of the aqueous extract are the most important indices for assessing the ripening of Cheddar cheese. The ratios of citrulline and arginine decrease by 59% and 69%, respectively, after 450 days ripening. In comparison to the premium batch B cheese, batch C which was predicted to attain a lower quality level, had higher serine and β-galactose as well as lower lactic acid levels and also had a less mature sensorial profile. Tyrosine, tyramine and lysine are highly correlated with mature Cheddar cheese sensory attributes. β-Galactose and glycerol are correlated with young Cheddar cheese sensory attributes. These metabolites can be used to predict cheese quality.

Citation

Chen, Y., MacNaughtan, W., Jones, P., Yang, Q., Williams, H., & Foster, T. (2021). Selection of potential molecular markers for cheese ripening and quality prediction by NMR spectroscopy. LWT - Food Science and Technology, 136, Article 110306. https://doi.org/10.1016/j.lwt.2020.110306

• Journal Article Type: Article
• Acceptance Date: Sep 25, 2020
• Online Publication Date: Sep 30, 2020
• Publication Date: Jan 1, 2021
• Deposit Date: Oct 9, 2020
• Publicly Available Date: Oct 1, 2021
• Journal: LWT
• Print ISSN: 0023-6438
• Electronic ISSN: 0023-6438
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 136
• Article Number: 110306
• DOI: https://doi.org/10.1016/j.lwt.2020.110306
• Keywords: Food Science
• Public URL: https://nottingham-repository.worktribe.com/output/4948365
• Publisher URL: https://www.sciencedirect.com/science/article/pii/S0023643820312950