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The potential of databases to improve beef quality

Through the analysis of metadata, the existence of the "cattle rearing practices – carcass – muscle – meat" continuum can enable best management of product quality.

The potential of databases to improve beef quality © Mohammed Gagaoua
Updated on 09/12/2018
Published on 05/03/2018

The expression "big data" reflects both the explosion in the number of data and metadata* available and the ability to process them rapidly. This now concerns all areas of activity. In animal production sciences, the exploitation of databases has been the subject of few in-depth studies when compared to other disciplines.

The sensory properties of meat (colour, tenderness, juiciness, flavour) and the rearing conditions of livestock (when they are known) are increasingly driving consumer preferences and purchasing. An INRA data warehouse** characterising each animal – from rearing practices to final meat qualities (continuum) – has been exploited in support of the “Auvergne-Rhône-Alpes Beef Fattening Sector” project (Filière Bovins Engraissement Auvergne-Rhône-Alpes), led by the regional beef sector both upstream and downstream. The study of the farmgate to meat "continuum" is a promising challenge to achieve optimum management of both cattle performance and the quality of their products through rearing factor practices. Thus, several statistical approaches were deployed to address this challenge.

Finishing is an important factor for both carcass and meat quality

In the first study, multivariate analyses (principal component analysis (PCA) combined with ak-means clustering approach) was able to discriminate different groups of cattle based on 16 livestock rearing practices applied during the lives of animals and the finishing period. In the case of the PDO Maine-Anjou, the PCA-k-means approach was able to identify three groups of practices: “grass”, “hay” and “haylage”. It seems that older cows with meat genetic, finished on grass with high physical activity at the farm and good dairy aptitude, have the carcass properties that are more economically advantageous for breeders. This analysis confirmed that finishing the cows on grass influenced the properties of muscle fibres, leading to more red-coloured meat but with tenderness and intramuscular fat contents similar to meat from animals finished on “hay” or “haylage” (Gagaoua et al., 2017a,b; 2018). Furthermore, the proportions of glycolytic IIX and oxidative IIA fibres in theLongissimus thoracismuscle ((ribeye steaks) were lower and higher, respectively, in cows from the “grass” group. It was thus proposed that the PCA-k-means method applied to the data on meat produced under the PDO Maine-Anjou label would be generalised to other breeds and animal types of French farmed livestock.

In a second study, 480 young cattle were analysed by exploiting 13 variables describing their rearing practices during finishing. The simultaneous use of unsupervised learning tools, decision trees, PCA-k-means multivariate analysis and linear regression models was able to link data on carcasses (fattening status, percentage muscle and percentage adipose tissue in carcasses), muscle (isocitrate dehydrogenase activity, ultimate pH, Lightness (L*), intramuscular fat content and total collagen) and the finishing period (initial body weight at the begining of the finishing period, fattening duration, dry matter intake, percentages of concentrate and forage). We thus demonstrated the potential for these data to achieve optimum management of final meat quality (Gagaoua et al.,2018 b,c).

All these data analyses relative to different levels – from the living animal (farmgate) to the final meat – have made it possible to propose certain recommendations to the actors of the beef sector based on the use of decision-based tools so they can jointly manage the carcass and meat properties they are seeking for (Gagaoua et al.,2018 d,e).

 *Metadata are data that serve to define or describe other data, whatever the type (paper or electronic). Metadata can retrieve all stored data. In our case, the most common metadata employed included individual information on animals, how they were managed, their dates of birth, weight before slaughter, carcass weight, weights of muscle or fat, the colour of their meat, the pH of their muscle or the tenderness of their meat, etc., all information that could identify and localise the data required at any given time (at birth, during rearing or finishing, at slaughter, in the laboratory or in the consumer’s home; or in other words, all types of stored information).

**A data warehouse is a database dedicated to the storage of all data used in the context of decision-making and decisional analysis.


Gagaoua M., Monteils V., Couvreur S. & Picard B. (2017a) Identification of Biomarkers Associated with the Rearing Practices, Carcass Characteristics, and Beef Quality: An Integrative Approach. Journal of Agricultural and Food Chemistry, 65, 8264-78. http://dx.doi.org/10.1021/acs.jafc.7b03239  

Gagaoua M., Picard B., Couvreur S., Le Bec G., Aminot G. & Monteils V. (2017b) Rearing practices and carcass and meat properties: a clustering approach in PDO Maine-Anjou cows. In: Proceedings of the 63rd International Congress of Meat Science and Technology (eds. by Troy D, McDonnell C, Hinds L & Kerry J), pp. 97-8. Wageningen Academic Publishers, Cork, Ireland. http://dx.doi.org/10.3920/978-90-8686-860-5

Gagaoua, M., Monteils, V., Couvreur, S., & Picard, B. (2018a). Mise en relation des pratiques d’élevage avec les propriétés des carcasses et de la viande.Viandes et Produits Carnés.VPC-2018-34-1-4, 1-9.

Gagaoua, M., Picard, B., Soulat, J. & Monteils, V. (2018b). Clustering of sensory eating qualities of beef: consistencies and differences within carcass, muscle, animal characteristics and rearing factors. Livestock Science,214,245-258. DOI: http://dx.doi.org/10.1016/j.livsci.2018.06.011  

Gagaoua, M., Picard, B., & Monteils, V. (2018c). Decision tree, a learning tool for the prediction of young bull’s beef tenderness using rearing factors and carcass characteristics.Journal of the Science of Food and Agriculture,Revision.

Gagaoua M., Monteils V. & Picard B. (2018d). Chemometrics and supervised learning for cows shear force prediction using the continuum data from farmgate to meat. In: Proceedings of the 64th International Congress of Meat Science and Technology, pp. 1-2,. 12th to 17th August, Melbourne, Australia.

Gagaoua M., Monteils V., J-F. Hocquette, & Picard B. (2018e). Understanding of beef tenderness variability based on the continuum data using Chemometrics: A proof-of-concept study. In: Proceedings of the 64th International Congress of Meat Science and Technology, pp. 1-2,. 12th to 17th August, Melbourne, Australia.