Next Article in Journal
Postprandial Glycemic and Insulinemic Effects of the Addition of Aqueous Extracts of Dried Corn Silk, Cumin Seed Powder or Tamarind Pulp, in Two Forms, Consumed with High Glycemic Index Rice
Next Article in Special Issue
Online Prediction of Physico-Chemical Quality Attributes of Beef Using Visible—Near-Infrared Spectroscopy and Chemometrics
Previous Article in Journal
Determination of Xanthohumol in Hops, Food Supplements and Beers by HPLC
Previous Article in Special Issue
Lipid Oxidation Inhibition Capacity of 11 Plant Materials and Extracts Evaluated in Highly Oxidised Cooked Meatballs
Open AccessReview

Predicting the Quality of Meat: Myth or Reality?

1
UMR Biologie des Oiseaux et Aviculture, INRA, Université de Tours, 37380 Nouzilly, France
2
UMR Herbivores, INRA, VetAgro Sup, Theix, 63122 Saint-Genès Champanelle, France
3
UMR Physiologie, Environnement et Génétique pour l’Animal et les Systèmes d’Élevage, INRA, AgroCampus Ouest, 35590 Saint-Gilles, France
4
Laboratoire de Physiologie et Génomique des poissons, INRA, 35000 Rennes, France
5
Institut du porc, La motte au Vicomte, 35651 Le Rheu, CEDEX, France
6
Institut de l’Elevage, Maison Régionale de l’Agriculture—Nouvelle Aquitaine, 87000 Limoges, France
*
Author to whom correspondence should be addressed.
Foods 2019, 8(10), 436; https://doi.org/10.3390/foods8100436
Received: 6 September 2019 / Revised: 16 September 2019 / Accepted: 20 September 2019 / Published: 24 September 2019
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. View Full-Text
Keywords: meat; quality; prediction; biological marker; spectroscopy; phenotypic model meat; quality; prediction; biological marker; spectroscopy; phenotypic model
Show Figures

Figure 1

MDPI and ACS Style

Berri, C.; Picard, B.; Lebret, B.; Andueza, D.; Lefèvre, F.; Le Bihan-Duval, E.; Beauclercq, S.; Chartrin, P.; Vautier, A.; Legrand, I.; Hocquette, J.-F. Predicting the Quality of Meat: Myth or Reality? Foods 2019, 8, 436.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop