Evidence map›Paper›PMID 31554284›Full record

ReviewFoods (Basel, Switzerland)2019

Predicting the Quality of Meat: Myth or Reality?

Cécile Berri, Brigitte Picard, Bénédicte Lebret, Donato Andueza, Florence Lefèvre, Elisabeth Le Bihan-Duval, Stéphane Beauclercq, Pascal Chartrin, Antoine Vautier, Isabelle Legrand and 1 more

Open access · goldAbstract readReview
In one paragraph

Review in Foods (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
6.0field-weighted citation impact, top 3% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

15 citing papers in PubMed, 47 citations in OpenAlex.

  1. Article
  2. The Inclusion ofAnimals : an open access journal from MDPI · 2026
    Article
  3. Relationships Among Growth, Carcass Characteristics, andAnimals : an open access journal from MDPI · 2024
    Article
  4. Article
  5. Article
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  12. Review
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  14. Review
  15. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors at 6 institutions in 1 country.

Cécile BerriUMR Biologie des Oiseaux et Aviculture, INRA, Université de Tours, 37380 Nouzilly, France. cecile.berri@inra.fr.
Brigitte PicardUMR Herbivores, INRA, VetAgro Sup, Theix, 63122 Saint-Genès Champanelle, France. brigitte.picard@inra.fr.
Bénédicte LebretUMR Physiologie, Environnement et Génétique pour l'Animal et les Systèmes d'Élevage, INRA, AgroCampus Ouest, 35590 Saint-Gilles, France. benedicte.lebret@inra.fr.
Donato AnduezaUMR Herbivores, INRA, VetAgro Sup, Theix, 63122 Saint-Genès Champanelle, France. donato.andueza@inra.fr.
Florence LefèvreLaboratoire de Physiologie et Génomique des poissons, INRA, 35000 Rennes, France. florence.lefevre@inra.fr.ORCID 0000-0002-5573-8411
Elisabeth Le Bihan-DuvalUMR Biologie des Oiseaux et Aviculture, INRA, Université de Tours, 37380 Nouzilly, France. elisabeth.duval@inra.fr.
Stéphane BeauclercqUMR Biologie des Oiseaux et Aviculture, INRA, Université de Tours, 37380 Nouzilly, France. s.beauclercq@gmail.com.ORCID 0000-0002-1029-559X
Pascal ChartrinUMR Biologie des Oiseaux et Aviculture, INRA, Université de Tours, 37380 Nouzilly, France. pascal.chartrin@inra.fr.
Antoine VautierInstitut du porc, La motte au Vicomte, 35651 Le Rheu, CEDEX, France. antoine.vautier@ifip.asso.fr.
Isabelle LegrandInstitut de l'Elevage, Maison Régionale de l'Agriculture-Nouvelle Aquitaine, 87000 Limoges, France. isabelle.legrand@idele.fr.
Jean-François HocquetteUMR Herbivores, INRA, VetAgro Sup, Theix, 63122 Saint-Genès Champanelle, France. jean-francois.hocquette@inra.fr.ORCID 0000-0003-2409-3881
Université de Tours · FRVetAgro Sup · FRInstitut de l’Elevage · FRInstitut du Porc · FRLaboratoire de Physiologie et Génomique des Poissons · FRPhysiologie, Environnement et Génétique pour l'Animal et les Systèmes d'Elevage · FR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

biological markermeatphenotypic modelpredictionqualityspectroscopy

Identifiers

PMID31554284
PMCPMC6836130
OpenAlexW2977231448

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.