Prediction of meat quality traits in the abattoir using portable near-infrared spectrometers: heritability of predicted traits and genetic correlations with laboratory-measured traits.

Genetic parameters Meat quality Near-infrared spectroscopy Piemontese

Journal

Journal of animal science and biotechnology
ISSN: 1674-9782
Titre abrégé: J Anim Sci Biotechnol
Pays: England
ID NLM: 101581293

Informations de publication

Date de publication:
12 Mar 2021
Historique:
received: 08 08 2020
accepted: 12 01 2021
entrez: 12 3 2021
pubmed: 13 3 2021
medline: 13 3 2021
Statut: epublish

Résumé

The possibility of assessing meat quality traits over the meat chain is strongly limited, especially in the context of selective breeding which requires a large number of phenotypes. The main objective of this study was to investigate the suitability of portable infrared spectrometers for phenotyping beef cattle aiming to genetically improving the quality of their meat. Meat quality traits (pH, color, water holding capacity, tenderness) were appraised on rib eye muscle samples of 1,327 Piemontese young bulls using traditional (i.e., reference/gold standard) laboratory analyses; the same traits were also predicted from spectra acquired at the abattoir on the intact muscle surface of the same animals 1 d after slaughtering. Genetic parameters were estimated for both laboratory measures of meat quality traits and their spectra-based predictions. The prediction performances of the calibration equations, assessed through external validation, were satisfactory for color traits (R Results showed that NIRS predictions of color traits, pH, and purge losses could be used as indicator traits for the indirect genetic selection of the reference quality phenotypes. Results for cooking losses were less effective, while the NIR predictions of tenderness were affected by a relatively high uncertainty of estimate. Overall, genetic selection of some meat quality traits, whose direct phenotyping is difficult, can benefit of the application of infrared spectrometers technology.

Sections du résumé

BACKGROUND BACKGROUND
The possibility of assessing meat quality traits over the meat chain is strongly limited, especially in the context of selective breeding which requires a large number of phenotypes. The main objective of this study was to investigate the suitability of portable infrared spectrometers for phenotyping beef cattle aiming to genetically improving the quality of their meat. Meat quality traits (pH, color, water holding capacity, tenderness) were appraised on rib eye muscle samples of 1,327 Piemontese young bulls using traditional (i.e., reference/gold standard) laboratory analyses; the same traits were also predicted from spectra acquired at the abattoir on the intact muscle surface of the same animals 1 d after slaughtering. Genetic parameters were estimated for both laboratory measures of meat quality traits and their spectra-based predictions.
RESULTS RESULTS
The prediction performances of the calibration equations, assessed through external validation, were satisfactory for color traits (R
CONCLUSIONS CONCLUSIONS
Results showed that NIRS predictions of color traits, pH, and purge losses could be used as indicator traits for the indirect genetic selection of the reference quality phenotypes. Results for cooking losses were less effective, while the NIR predictions of tenderness were affected by a relatively high uncertainty of estimate. Overall, genetic selection of some meat quality traits, whose direct phenotyping is difficult, can benefit of the application of infrared spectrometers technology.

Identifiants

pubmed: 33706809
doi: 10.1186/s40104-021-00555-5
pii: 10.1186/s40104-021-00555-5
pmc: PMC7953783
doi:

Types de publication

Journal Article

Langues

eng

Pagination

29

Subventions

Organisme : Fondazione Cassa di Risparmio di Cuneo (IT)
ID : 2014/0249

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Auteurs

Simone Savoia (S)

Associazione Nazionale Allevatori Bovini di Razza Piemontese, strada provinciale Trinita' 32/A, 12061, Carrù, CN, Italy. simone.savoia@anaborapi.it.
Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova (Padua), viale dell'Università 16, 35020, Legnaro, PD, Italy. simone.savoia@anaborapi.it.

Andrea Albera (A)

Associazione Nazionale Allevatori Bovini di Razza Piemontese, strada provinciale Trinita' 32/A, 12061, Carrù, CN, Italy.

Alberto Brugiapaglia (A)

Department of Agricultural, Forest and Food Sciences, University of Torino, Via L. Da Vinci 44, 10095, Grugliasco, TO, Italy.

Liliana Di Stasio (L)

Department of Agricultural, Forest and Food Sciences, University of Torino, Via L. Da Vinci 44, 10095, Grugliasco, TO, Italy.

Alessio Cecchinato (A)

Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova (Padua), viale dell'Università 16, 35020, Legnaro, PD, Italy.

Giovanni Bittante (G)

Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova (Padua), viale dell'Università 16, 35020, Legnaro, PD, Italy.

Classifications MeSH