Prédire la qualité des viandes : mythe ou réalité ? Etat des lieux des avancées récentes réalisées dans le domaine de la prédiction des qualités des viandes ; réflexion sur les perspectives d’application
Identifieur interne : 000571 ( Hal/Curation ); précédent : 000570; suivant : 000572Prédire la qualité des viandes : mythe ou réalité ? Etat des lieux des avancées récentes réalisées dans le domaine de la prédiction des qualités des viandes ; réflexion sur les perspectives d’application
Auteurs : Cécile Berri [France] ; Brigitte Picard [France] ; Bénédicte Lebret [France] ; Jesus Donato Andueza Urra [France] ; Antoine Vautier [France] ; Pascal Chartrin [France] ; Stéphane Beauclercq [France] ; Florence Lefèvre [France] ; Isabelle Legrand [France] ; Jean-François Hocquette [France]Source :
- Viandes et Produits Carnés [ 0241-0389 ] ; 2016.
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Abstract
This review was aimed at providing an overview of recent advancements obtained in the field of meat quality prediction. The differentmethods used in the production sectors for the development of equations and the tools used based on different types of biological markers(genomic or phenotypic) or physical (spectroscopy) are discussed. Through the different examples that are presented, it appears that theidentification of biological markers is confronted with the complex determinism of quality parameters. This makes the development of genericmolecular tests to be used on the field even more difficult. However, in recent years, progress has been made, benefitting from genomics,proteomics and metabolomics technological developments. The first equations that predict the sensorial quality and the technological potentialof meat hint to possible applications in the near future. Concerning spectroscopy, the main results were obtained using Near Infrared Spectroscopy(NIR) with developments obtained for the prediction of the composition and nutritional value of meats. The prediction of the technologicalpotential of meats using this method and mainly the sensorial quality is, however, more difficult. Finally, the example of the MSA (« MeatStandards Australia ») phenotypic model which predicts the sensorial quality of bovine meat based on a combination of upstream and downstreamdata and whose added-value for the sector has been identified in Australia, presents a genericness that has already been proven in severalcountries.
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BP 87999 - 21079 Dijon cedex</addrLine> <country key="FR"></country> </address> <ref type="url">http://www.agrosupdijon.fr/</ref> </desc> </org></tutelle><tutelle name="UMR1213" active="#struct-301767" type="direct"><org type="institution" xml:id="struct-301767" status="VALID"> <orgName>VetAgro Sup</orgName> <desc> <address> <country key="FR"></country> </address> </desc> </org></tutelle></tutelles></hal:affiliation><country>France</country></affiliation></author><author><name sortKey="Vautier, Antoine" sort="Vautier, Antoine" uniqKey="Vautier A" first="Antoine" last="Vautier">Antoine Vautier</name><affiliation wicri:level="1"><hal:affiliation type="institution" xml:id="struct-326361" status="INCOMING"> <orgName>Institut du Porc</orgName> <desc> <address> <country key="FR"></country> </address> </desc> </hal:affiliation><country>France</country></affiliation></author><author><name sortKey="Chartrin, Pascal" sort="Chartrin, Pascal" uniqKey="Chartrin P" first="Pascal" last="Chartrin">Pascal Chartrin</name><affiliation wicri:level="1"><hal:affiliation type="laboratory" xml:id="struct-37692" status="VALID"> <orgName>Recherches Avicoles</orgName> <orgName type="acronym">SRA</orgName> <desc> <address> <country key="FR"></country> </address> </desc> <listRelation> <relation name="UR0083" active="#struct-92114" type="direct"></relation> </listRelation> <tutelles><tutelle name="UR0083" active="#struct-92114" type="direct"><org type="institution" xml:id="struct-92114" status="VALID"> <orgName>Institut National de la Recherche Agronomique</orgName> <orgName type="acronym">INRA</orgName> <desc> <address> <country key="FR"></country> </address> <ref type="url">http://www.inra.fr</ref> </desc> </org></tutelle></tutelles></hal:affiliation><country>France</country></affiliation></author><author><name sortKey="Beauclercq, Stephane" sort="Beauclercq, Stephane" uniqKey="Beauclercq S" first="Stéphane" last="Beauclercq">Stéphane Beauclercq</name><affiliation wicri:level="1"><hal:affiliation type="laboratory" xml:id="struct-37692" status="VALID"> <orgName>Recherches Avicoles</orgName> <orgName type="acronym">SRA</orgName> <desc> <address> <country key="FR"></country> </address> </desc> <listRelation> <relation name="UR0083" active="#struct-92114" type="direct"></relation> </listRelation> <tutelles><tutelle name="UR0083" active="#struct-92114" type="direct"><org type="institution" xml:id="struct-92114" status="VALID"> <orgName>Institut National de la Recherche Agronomique</orgName> <orgName type="acronym">INRA</orgName> <desc> <address> <country key="FR"></country> </address> <ref type="url">http://www.inra.fr</ref> </desc> </org></tutelle></tutelles></hal:affiliation><country>France</country></affiliation></author><author><name sortKey="Lefevre, Florence" sort="Lefevre, Florence" uniqKey="Lefevre F" first="Florence" last="Lefèvre">Florence Lefèvre</name><affiliation wicri:level="1"><hal:affiliation type="laboratory" xml:id="struct-187317" status="VALID"> <orgName>Laboratoire de Physiologie et Génomique des Poissons</orgName> <orgName type="acronym">LPGP</orgName> <desc> <address> <country key="FR"></country> </address> <ref type="url">http://www.rennes.inra.fr/lpgp</ref> </desc> <listRelation> <relation name="UR1037" active="#struct-92114" type="direct"></relation> <relation active="#struct-453207" type="direct"></relation> </listRelation> <tutelles><tutelle name="UR1037" active="#struct-92114" type="direct"><org type="institution" xml:id="struct-92114" status="VALID"> <orgName>Institut National de la Recherche Agronomique</orgName> <orgName type="acronym">INRA</orgName> <desc> <address> <country key="FR"></country> </address> <ref type="url">http://www.inra.fr</ref> </desc> </org></tutelle><tutelle active="#struct-453207" type="direct"><org type="institution" xml:id="struct-453207" status="VALID"> <orgName>Structure Fédérative de Recherche en Biologie et Santé de Rennes </orgName> <orgName type="acronym"> Biosit : Biologie - Santé - Innovation Technologique </orgName> <desc> <address> <addrLine>Université Rennes 1 - Campus Santé Villejean - 2, avenue Professeur Léon Bernard 35043 Rennes cedex</addrLine> <country key="FR"></country> </address> </desc> </org></tutelle></tutelles></hal:affiliation><country>France</country></affiliation></author><author><name sortKey="Legrand, Isabelle" sort="Legrand, Isabelle" uniqKey="Legrand I" first="Isabelle" last="Legrand">Isabelle Legrand</name><affiliation wicri:level="1"><hal:affiliation type="laboratory" xml:id="struct-518754" status="INCOMING"> <orgName>Service Qualité des viandes</orgName> <desc> <address> <country key="FR"></country> </address> </desc> <listRelation> <relation active="#struct-309185" type="direct"></relation> </listRelation> <tutelles><tutelle active="#struct-309185" type="direct"><org type="institution" xml:id="struct-309185" status="INCOMING"> <orgName>Institut de l'Elevage</orgName> <desc> <address> <country key="FR"></country> </address> </desc> </org></tutelle></tutelles></hal:affiliation><country>France</country></affiliation></author><author><name sortKey="Hocquette, Jean Francois" sort="Hocquette, Jean Francois" uniqKey="Hocquette J" first="Jean-François" last="Hocquette">Jean-François Hocquette</name><affiliation wicri:level="1"><hal:affiliation type="laboratory" xml:id="struct-37762" status="VALID"> <orgName>Unité Mixte de Recherches sur les Herbivores</orgName> <orgName type="acronym">UMRH 1213 Herbivores</orgName> <desc> <address> <addrLine>INRA / Site de Theix / 63122 Saint-Genès-Champanelle</addrLine> <country key="FR"></country> </address> <ref type="url">http://umrh-bioinfo.clermont.inra.fr/Intranet/web/UMRH</ref> </desc> <listRelation> <relation name="UMR1213" active="#struct-92114" type="direct"></relation> <relation name="UMR1213" active="#struct-415469" type="direct"></relation> <relation name="UMR1213" active="#struct-301767" type="direct"></relation> </listRelation> <tutelles><tutelle name="UMR1213" active="#struct-92114" type="direct"><org type="institution" xml:id="struct-92114" status="VALID"> <orgName>Institut National de la Recherche Agronomique</orgName> <orgName type="acronym">INRA</orgName> <desc> <address> <country key="FR"></country> </address> <ref type="url">http://www.inra.fr</ref> </desc> </org></tutelle><tutelle name="UMR1213" active="#struct-415469" type="direct"><org type="institution" xml:id="struct-415469" status="VALID"> <orgName>Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement</orgName> <orgName type="acronym">AgroSup Dijon</orgName> <date type="start">2009-03-01</date> <desc> <address> <addrLine>26 Boulevard du Dr Petitjean - BP 87999 - 21079 Dijon cedex</addrLine> <country key="FR"></country> </address> <ref type="url">http://www.agrosupdijon.fr/</ref> </desc> </org></tutelle><tutelle name="UMR1213" active="#struct-301767" type="direct"><org type="institution" xml:id="struct-301767" status="VALID"> <orgName>VetAgro Sup</orgName> <desc> <address> <country key="FR"></country> </address> </desc> </org></tutelle></tutelles></hal:affiliation><country>France</country></affiliation></author></analytic><series><title level="j">Viandes et Produits Carnés</title><idno type="ISSN">0241-0389</idno><imprint><date type="datePub">2016</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="mix" xml:lang="en"><term>marqueur biologique</term><term>meat</term><term>modèle phénotypique</term><term>prédiction</term><term>qualité</term><term>spectroscopie</term></keywords><keywords scheme="mix" xml:lang="fr"><term>perspective avancee</term><term>perspective d'application</term><term>produit carné</term><term>qualité de la viande</term><term>viande</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">This review was aimed at providing an overview of recent advancements obtained in the field of meat quality prediction. The differentmethods used in the production sectors for the development of equations and the tools used based on different types of biological markers(genomic or phenotypic) or physical (spectroscopy) are discussed. Through the different examples that are presented, it appears that theidentification of biological markers is confronted with the complex determinism of quality parameters. This makes the development of genericmolecular tests to be used on the field even more difficult. However, in recent years, progress has been made, benefitting from genomics,proteomics and metabolomics technological developments. The first equations that predict the sensorial quality and the technological potentialof meat hint to possible applications in the near future. Concerning spectroscopy, the main results were obtained using Near Infrared Spectroscopy(NIR) with developments obtained for the prediction of the composition and nutritional value of meats. The prediction of the technologicalpotential of meats using this method and mainly the sensorial quality is, however, more difficult. Finally, the example of the MSA (« MeatStandards Australia ») phenotypic model which predicts the sensorial quality of bovine meat based on a combination of upstream and downstreamdata and whose added-value for the sector has been identified in Australia, presents a genericness that has already been proven in severalcountries.</div></front></TEI><hal api="V3"> <titleStmt> <title xml:lang="fr">Prédire la qualité des viandes : mythe ou réalité ? Etat des lieux des avancées récentes réalisées dans le domaine de la prédiction des qualités des viandes ; réflexion sur les perspectives d’application</title> <author role="aut"> <persName> <forename type="first">Cécile</forename> <surname>Berri</surname> </persName> <email type="md5">6ba23f2979b26d1aa8a8fa879be1712c</email> <email type="domain">inra.fr</email> <idno type="halauthorid">1477306</idno> <affiliation ref="#struct-37692"></affiliation> </author> <author role="aut"> <persName> <forename type="first">Brigitte</forename> <surname>Picard</surname> </persName> <email type="md5">474ddb0b9d927654bd12255fe810a344</email> <email type="domain">inra.fr</email> <idno type="halauthorid">1477307</idno> <affiliation ref="#struct-37762"></affiliation> </author> <author role="aut"> <persName> <forename type="first">Bénédicte</forename> <surname>Lebret</surname> </persName> <email type="md5">40722ee0b599fe0b2632411f12fd2337</email> <email type="domain">inra.fr</email> <idno type="halauthorid">1477308</idno> <affiliation ref="#struct-187318"></affiliation> </author> <author role="aut"> <persName> <forename type="first">Jesus Donato</forename> <surname>Andueza Urra</surname> </persName> <email type="md5">ca7ad45d0c5db5b04dfa4890b4c70b23</email> <email type="domain">inra.fr</email> <idno type="halauthorid">1477309</idno> <affiliation ref="#struct-37762"></affiliation> </author> <author role="ctb"> <persName> <forename type="first">Antoine</forename> <surname>Vautier</surname> </persName> <email type="md5">00a18cdf01146a530b35b238652c6cac</email> <email type="domain">ifip.asso.fr</email> <idno type="halauthorid">1644545</idno> <affiliation ref="#struct-326361"></affiliation> </author> <author role="aut"> <persName> <forename type="first">Pascal</forename> <surname>Chartrin</surname> </persName> <email type="md5">91c898ea05dcfab7952781139ab77c68</email> <email type="domain">inra.fr</email> <idno type="halauthorid">1477310</idno> <affiliation ref="#struct-37692"></affiliation> </author> <author role="aut"> <persName> <forename type="first">Stéphane</forename> <surname>Beauclercq</surname> </persName> <email type="md5">c964e639e734313c785e1bf7a2b40355</email> <email type="domain">inra.fr</email> <idno type="halauthorid">1477311</idno> <affiliation ref="#struct-37692"></affiliation> </author> <author role="aut"> <persName> <forename type="first">Florence</forename> <surname>Lefèvre</surname> </persName> <email type="md5">f3ba12d1474701b8a6c9760a64d2b038</email> <email type="domain">inra.fr</email> <idno type="halauthorid">1370429</idno> <affiliation ref="#struct-187317"></affiliation> </author> <author role="ctb"> <persName> <forename type="first">Isabelle</forename> <surname>Legrand</surname> </persName> <email type="md5">278dcd49a9a5378ea63edf7d070e8c60</email> <email type="domain">idele.fr</email> <idno type="halauthorid">1644546</idno> <affiliation ref="#struct-518754"></affiliation> </author> <author role="aut"> <persName> <forename type="first">Jean-François</forename> <surname>Hocquette</surname> </persName> <email type="md5">c64cf4109afddc1b5a4eb749ac2e10c7</email> <email type="domain">inra.fr</email> <idno type="halauthorid">1477312</idno> <affiliation ref="#struct-37762"></affiliation> </author> <editor role="depositor"> <persName> <forename>Archive Ouverte</forename> <surname>ProdInra</surname> </persName> <email type="md5">859d2cba2c64f5bbc2add42f2b9992d1</email> <email type="domain">inra.fr</email> </editor> </titleStmt> <editionStmt> <edition n="v1" type="current"> <date type="whenSubmitted">2017-10-03 01:48:45</date> <date type="whenModified">2017-10-10 13:49:51</date> <date type="whenReleased">2017-10-03 01:48:45</date> <date type="whenProduced">2016</date> </edition> <respStmt> <resp>contributor</resp> <name key="132775"> <persName> <forename>Archive Ouverte</forename> <surname>ProdInra</surname> </persName> <email type="md5">859d2cba2c64f5bbc2add42f2b9992d1</email> <email type="domain">inra.fr</email> </name> </respStmt> </editionStmt> <publicationStmt> <distributor>CCSD</distributor> <idno type="halId">hal-01608716</idno> <idno type="halUri">https://hal.archives-ouvertes.fr/hal-01608716</idno> <idno type="halBibtex">berri:hal-01608716</idno> <idno type="halRefHtml">Viandes et Produits Carnés, 2016, pp.1-14</idno> <idno type="halRef">Viandes et Produits Carnés, 2016, pp.1-14</idno> </publicationStmt> <seriesStmt> <idno type="stamp" n="INRA">INRA - Institut national de la recherche agronomique</idno> <idno type="stamp" n="UNAM">l'unam - université nantes angers le mans</idno> <idno type="stamp" n="IFR140">SFR Biosit - Biologie et Santé CNRS UMS 3480</idno> <idno type="stamp" n="LPGP"> Laboratoire de Physiologie et Génomique des Poissons Inra UR 1037</idno> <idno type="stamp" n="PEGASE">Physiologie, Environnement et Génétique pour l’Animal et les Systèmes d’Elevage</idno> <idno type="stamp" n="BIOSIT">SFR Biosit - Biologie et Santé CNRS UMS 3480</idno> <idno type="stamp" n="AGROCAMPUS-OUEST">Agrocampus Ouest</idno> <idno type="stamp" n="UR1-SDV">UR1 - publications SDV</idno> <idno type="stamp" n="AGROSUP-DIJON">AGROSUP-DIJON</idno> </seriesStmt> <notesStmt> <note type="audience" n="1">Not set</note> <note type="popular" n="1">Yes</note> <note type="peer" n="0">No</note> </notesStmt> <sourceDesc> <biblStruct> <analytic> <title xml:lang="fr">Prédire la qualité des viandes : mythe ou réalité ? Etat des lieux des avancées récentes réalisées dans le domaine de la prédiction des qualités des viandes ; réflexion sur les perspectives d’application</title> <author role="aut"> <persName> <forename type="first">Cécile</forename> <surname>Berri</surname> </persName> <email type="md5">6ba23f2979b26d1aa8a8fa879be1712c</email> <email type="domain">inra.fr</email> <idno type="halauthorid">1477306</idno> <affiliation ref="#struct-37692"></affiliation> </author> <author role="aut"> <persName> <forename type="first">Brigitte</forename> <surname>Picard</surname> </persName> <email type="md5">474ddb0b9d927654bd12255fe810a344</email> <email type="domain">inra.fr</email> <idno type="halauthorid">1477307</idno> <affiliation ref="#struct-37762"></affiliation> </author> <author role="aut"> <persName> <forename type="first">Bénédicte</forename> <surname>Lebret</surname> </persName> <email type="md5">40722ee0b599fe0b2632411f12fd2337</email> <email type="domain">inra.fr</email> <idno type="halauthorid">1477308</idno> <affiliation ref="#struct-187318"></affiliation> </author> <author role="aut"> <persName> <forename type="first">Jesus Donato</forename> <surname>Andueza Urra</surname> </persName> <email type="md5">ca7ad45d0c5db5b04dfa4890b4c70b23</email> <email type="domain">inra.fr</email> <idno type="halauthorid">1477309</idno> <affiliation ref="#struct-37762"></affiliation> </author> <author role="ctb"> <persName> <forename type="first">Antoine</forename> <surname>Vautier</surname> </persName> <email type="md5">00a18cdf01146a530b35b238652c6cac</email> <email type="domain">ifip.asso.fr</email> <idno type="halauthorid">1644545</idno> <affiliation ref="#struct-326361"></affiliation> </author> <author role="aut"> <persName> <forename type="first">Pascal</forename> <surname>Chartrin</surname> </persName> <email type="md5">91c898ea05dcfab7952781139ab77c68</email> <email type="domain">inra.fr</email> <idno type="halauthorid">1477310</idno> <affiliation ref="#struct-37692"></affiliation> </author> <author role="aut"> <persName> <forename type="first">Stéphane</forename> <surname>Beauclercq</surname> </persName> <email type="md5">c964e639e734313c785e1bf7a2b40355</email> <email type="domain">inra.fr</email> <idno type="halauthorid">1477311</idno> <affiliation ref="#struct-37692"></affiliation> </author> <author role="aut"> <persName> <forename type="first">Florence</forename> <surname>Lefèvre</surname> </persName> <email type="md5">f3ba12d1474701b8a6c9760a64d2b038</email> <email type="domain">inra.fr</email> <idno type="halauthorid">1370429</idno> <affiliation ref="#struct-187317"></affiliation> </author> <author role="ctb"> <persName> <forename type="first">Isabelle</forename> <surname>Legrand</surname> </persName> <email type="md5">278dcd49a9a5378ea63edf7d070e8c60</email> <email type="domain">idele.fr</email> <idno type="halauthorid">1644546</idno> <affiliation ref="#struct-518754"></affiliation> </author> <author role="aut"> <persName> <forename type="first">Jean-François</forename> <surname>Hocquette</surname> </persName> <email type="md5">c64cf4109afddc1b5a4eb749ac2e10c7</email> <email type="domain">inra.fr</email> <idno type="halauthorid">1477312</idno> <affiliation ref="#struct-37762"></affiliation> </author> </analytic> <monogr> <idno type="halJournalId" status="INCOMING">102210</idno> <idno type="issn">0241-0389</idno> <title level="j">Viandes et Produits Carnés</title> <imprint> <biblScope unit="pp">1-14</biblScope> <date type="datePub">2016</date> </imprint> </monogr> <idno type="prodinra">398753</idno> </biblStruct> </sourceDesc> <profileDesc> <langUsage> <language ident="fr">French</language> </langUsage> <textClass> <keywords scheme="author"> <term xml:lang="en">prédiction</term> <term xml:lang="en">qualité</term> <term xml:lang="en">marqueur biologique</term> <term xml:lang="en">spectroscopie</term> <term xml:lang="en">modèle phénotypique</term> <term xml:lang="en">meat</term> <term xml:lang="fr">viande</term> <term xml:lang="fr">produit carné</term> <term xml:lang="fr">perspective d'application</term> <term xml:lang="fr">perspective avancee</term> <term xml:lang="fr">qualité de la viande</term> </keywords> <classCode scheme="halDomain" n="sdv">Life Sciences [q-bio]</classCode> <classCode scheme="halTypology" n="ART">Journal articles</classCode> </textClass> <abstract xml:lang="en">This review was aimed at providing an overview of recent advancements obtained in the field of meat quality prediction. The differentmethods used in the production sectors for the development of equations and the tools used based on different types of biological markers(genomic or phenotypic) or physical (spectroscopy) are discussed. Through the different examples that are presented, it appears that theidentification of biological markers is confronted with the complex determinism of quality parameters. This makes the development of genericmolecular tests to be used on the field even more difficult. However, in recent years, progress has been made, benefitting from genomics,proteomics and metabolomics technological developments. The first equations that predict the sensorial quality and the technological potentialof meat hint to possible applications in the near future. Concerning spectroscopy, the main results were obtained using Near Infrared Spectroscopy(NIR) with developments obtained for the prediction of the composition and nutritional value of meats. The prediction of the technologicalpotential of meats using this method and mainly the sensorial quality is, however, more difficult. Finally, the example of the MSA (« MeatStandards Australia ») phenotypic model which predicts the sensorial quality of bovine meat based on a combination of upstream and downstreamdata and whose added-value for the sector has been identified in Australia, presents a genericness that has already been proven in severalcountries.</abstract> <abstract xml:lang="fr">Cette revue a pour objectif de faire un point sur les avancées récentes obtenues dans le domaine de la prédiction de la qualité des viandes.Elle aborde différentes démarches menées au sein des principales filières de production pour développer des équations puis des outils prédictifsbasés sur différents types de marqueurs biologiques (génomiques ou phénotypiques) ou physiques (spectroscopiques). Au travers des différentsexemples présentés, il apparaît que l’identification de marqueurs biologiques se confronte au déterminisme complexe des paramètres de qualitéqui rend encore difficile la mise au point de tests moléculaires génériques utilisables sur le terrain. Toutefois, les avancées ont été notables cesdernières années bénéficiant des récents développements technologiques en génomique, protéomique et métabolomique. Les premières équationsde prédiction de la qualité sensorielle et du potentiel technologique des viandes laissent entrevoir des possibilités d’applications dans les annéesà venir. Concernant la spectroscopie, les principaux résultats ont été obtenus dans le domaine de la Spectroscopie Proche Infrarouge (SPIR) avecdes développements aboutis pour prédire la composition et la valeur nutritionnelle des viandes. La prédiction du potentiel technologique desviandes à l’aide de cette méthode et surtout de la qualité sensorielle est en revanche plus difficile. Enfin, l’exemple du modèle phénotypiqueMSA (« Meat Standards Australia ») de prévision de la qualité sensorielle de la viande bovine basée sur une combinaison de données d’amontet d’aval et dont la plus-value pour la filière a été démontrée en Australie, présente une généricité qui a déjà été éprouvée dans plusieurs pays.</abstract> </profileDesc> </hal></record>Pour manipuler ce document sous Unix (Dilib)
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