Next Article in Journal
The Food Systems in the Era of the Coronavirus (COVID-19) Pandemic Crisis
Next Article in Special Issue
Proximate Composition, Amino Acid Profile, and Oxidative Stability of Slow-Growing Indigenous Chickens Compared with Commercial Broiler Chickens
Previous Article in Journal
Korean Red Ginseng Suppresses the Expression of Oxidative Stress Response and NLRP3 Inflammasome Genes in Aged C57BL/6 Mouse Ovaries
Previous Article in Special Issue
Effect of Rearing System on the Straight and Branched Fatty Acids of Goat Milk and Meat of Suckling Kids
Open AccessArticle

Various Statistical Approaches to Assess and Predict Carcass and Meat Quality Traits

1
Bordeaux Science Agro, 1 cours du Général de Gaulle, CS 40201, 33175 Gradignan, France
2
INRAE, UMR1213 Herbivores, 63122 Saint Genès Champanelle, France
3
Clermont Université, VetAgro Sup, UMR1213 Herbivores, BP 10448, 63000 Clermont-Ferrand, France
4
Isara Agro School for Life, 23 rue Jean Baldassini, 69364 Lyon CEDEX 07, France
5
Université de Bordeaux, UMR5251, INRIA, 33400 Talence, France
6
Agri-Food and Biosciences Institute, 18a Newforge Lane, Belfast BT9 5PX, UK
*
Author to whom correspondence should be addressed.
Foods 2020, 9(4), 525; https://doi.org/10.3390/foods9040525
Received: 25 March 2020 / Revised: 5 April 2020 / Accepted: 8 April 2020 / Published: 22 April 2020
(This article belongs to the Special Issue Impact of Pre-Mortem Factors on Meat Quality)
The beef industry is organized around different stakeholders, each with their own expectations, sometimes antagonistic. This article first outlines these differing perspectives. Then, various optimization models that might integrate all these expectations are described. The final goal is to define practices that could increase value for animal production, carcasses and meat whilst simultaneously meeting the main expectations of the beef industry. Different models previously developed worldwide are proposed here. Two new computational methodologies that allow the simultaneous selection of the best regression models and the most interesting covariates to predict carcass and/or meat quality are developed. Then, a method of variable clustering is explained that is accurate in evaluating the interrelationships between different parameters of interest. Finally, some principles for the management of quality trade-offs are presented and the Meat Standards Australia model is discussed. The “Pareto front” is an interesting approach to deal jointly with the different sets of expectations and to propose a method that could optimize all expectations together. View Full-Text
Keywords: optimization; meat quality; trade-off; meat standards Australia; carcass; bovine optimization; meat quality; trade-off; meat standards Australia; carcass; bovine
Show Figures

Figure 1

MDPI and ACS Style

Ellies-Oury, M.-P.; Hocquette, J.-F.; Chriki, S.; Conanec, A.; Farmer, L.; Chavent, M.; Saracco, J. Various Statistical Approaches to Assess and Predict Carcass and Meat Quality Traits. Foods 2020, 9, 525. https://doi.org/10.3390/foods9040525

AMA Style

Ellies-Oury M-P, Hocquette J-F, Chriki S, Conanec A, Farmer L, Chavent M, Saracco J. Various Statistical Approaches to Assess and Predict Carcass and Meat Quality Traits. Foods. 2020; 9(4):525. https://doi.org/10.3390/foods9040525

Chicago/Turabian Style

Ellies-Oury, Marie-Pierre; Hocquette, Jean-François; Chriki, Sghaier; Conanec, Alexandre; Farmer, Linda; Chavent, Marie; Saracco, Jérôme. 2020. "Various Statistical Approaches to Assess and Predict Carcass and Meat Quality Traits" Foods 9, no. 4: 525. https://doi.org/10.3390/foods9040525

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop