Predicting the quality of meat: Myth or reality? - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue (Article De Synthèse) Foods Année : 2019

Predicting the quality of meat: Myth or reality?

Résumé

This review is aimed at providing an overview of recent advances made in the field of meat quality prediction, particularly in Europe. The different methods used in research labs or by the production sectors for the development of equations and tools based on different types of biological (genomic or phenotypic) or physical (spectroscopy) markers are discussed. Through the various examples, it appears that although biological markers have been identified, quality parameters go through a complex determinism process. This makes the development of generic molecular tests even more difficult. However, in recent years, progress in the development of predictive tools has benefited from technological breakthroughs in genomics, proteomics, and metabolomics. Concerning spectroscopy, the most significant progress was achieved using near-infrared spectroscopy (NIRS) to predict the composition and nutritional value of meats. However, predicting the functional properties of meats using this method—mainly, the sensorial quality—is more difficult. Finally, the example of the MSA (Meat Standards Australia) phenotypic model, which predicts the eating quality of beef based on a combination of upstream and downstream data, is described. Its benefit for the beef industry has been extensively demonstrated in Australia, and its generic performance has already been proven in several countries.
Fichier principal
Vignette du fichier
2019_Berri_Foods.pdf (1.43 Mo) Télécharger le fichier
Origine : Accord explicite pour ce dépôt
Loading...

Dates et versions

hal-02306127 , version 1 (04-10-2019)

Licence

Paternité

Identifiants

Citer

Cécile Berri, Brigitte Picard, Bénédicte Lebret, Donato Andueza, Florence Lefèvre, et al.. Predicting the quality of meat: Myth or reality?. Foods, 2019, 8 (436), pp.1-22. ⟨10.3390/foods8100436⟩. ⟨hal-02306127⟩
87 Consultations
68 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More