Across countries implementation of handheld near-infrared spectrometer for the on-line prediction of beef marbling in slaughterhouse - INRAE - Institut national de recherche pour l’agriculture, l’alimentation et l’environnement Accéder directement au contenu
Article Dans Une Revue Meat Science Année : 2023

Across countries implementation of handheld near-infrared spectrometer for the on-line prediction of beef marbling in slaughterhouse

Arianna Goi
  • Fonction : Auteur
Matteo Santinello
  • Fonction : Auteur
Nicola Rampado
  • Fonction : Auteur
Stefka Atanassova
  • Fonction : Auteur
Jingjing Liu
  • Fonction : Auteur
Pascal Faure
  • Fonction : Auteur
Laure Thoumy
  • Fonction : Auteur
Alix Neveu
  • Fonction : Auteur
Massimo de Marchi
  • Fonction : Auteur

Résumé

Only few studies have used Near-Infrared (NIR) spectroscopy to assess meat quality traits directly in the chiller. The aim of this study was therefore to investigate the ability of a handheld NIR spectrometer to predict marbling scores on intact meat muscles in the chiller. A total of 829 animals from 2 slaughterhouses in France and Italy were involved. Marbling was assessed according to the 3G (Global Grading Guaranteed) protocol using 2 different scores. NIR measurements were collected by performing 5 scans at different points of the Longissimus thoracis. An average MSA marbling score of 330-340 was obtained in the two countries. The prediction models provided a R2 in external validation between 0.46 and 0.59 and a standard error of prediction between 83.1 and 105.5. Results did provide a moderate prediction of the marbling scores but can be useful in the European in-dustry context to predict classes of MSA marbling.

Dates et versions

hal-04224807 , version 1 (02-10-2023)

Identifiants

Citer

Moïse Kombolo-Ngah, Arianna Goi, Matteo Santinello, Nicola Rampado, Stefka Atanassova, et al.. Across countries implementation of handheld near-infrared spectrometer for the on-line prediction of beef marbling in slaughterhouse. Meat Science, 2023, 200, pp.109169. ⟨10.1016/j.meatsci.2023.109169⟩. ⟨hal-04224807⟩

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