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Review
. 2019 Sep 24;8(10):436.
doi: 10.3390/foods8100436.

Predicting the Quality of Meat: Myth or Reality?

Cécile Berri  1 , Brigitte Picard  2 , Bénédicte Lebret  3 , Donato Andueza  4 , Florence Lefèvre  5 , Elisabeth Le Bihan-Duval  6 , Stéphane Beauclercq  7 , Pascal Chartrin  8 , Antoine Vautier  9 , Isabelle Legrand  10 , Jean-François Hocquette  11
Affiliations
Review

Predicting the Quality of Meat: Myth or Reality?

Cécile Berri et al. Foods. .

Abstract

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.

Keywords: biological marker; meat; phenotypic model; prediction; quality; spectroscopy.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
(a) Projection of individuals according to major principal components based on an OPLS-DA (orthogonal projections to latent structures discriminant analysis) model with an explanatory ability (R2Y) of 0.73 and a predictive value (Q²) of 0.64 (the pHu− and pHu + individuals are shown in green and blue, respectively); (b) Contribution of the seven metabolites identified by the OPLS-DA model (pHu−/pHu +). Illustration based on results published in [21].
Figure 2
Figure 2
Visible/near infrared spectrum and first derivative between 400–2500 nm of a sample of bovine muscle (Rectus abdominis) after grinding (a) and lyophilization (b).
Figure 3
Figure 3
Prediction of the overall beef eating quality score (combining tenderness, flavor liking, juiciness, and overall liking) from different traits related to animals, carcasses, and cuts using the “Meat Standards Australia” (MSA) grading scheme.
See this image and copyright information in PMC

References

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