Predicting the Quality of Meat: Myth or Reality?

Abstract

This review is aimed at providing an overview of recent advances made in the field of meat quality prediction, particularly in Europe. The different methods used in research labs or by the production sectors for the development of equations and tools based on different types of biological (genomic or phenotypic) or physical (spectroscopy) markers are discussed. Through the various examples, it appears that although biological markers have been identified, quality parameters go through a complex determinism process. This makes the development of generic molecular tests even more difficult. However, in recent years, progress in the development of predictive tools has benefited from technological breakthroughs in genomics, proteomics, and metabolomics. Concerning spectroscopy, the most significant progress was achieved using near-infrared spectroscopy (NIRS) to predict the composition and nutritional value of meats. However, predicting the functional properties of meats using this method—mainly, the sensorial quality—is more difficult. Finally, the example of the MSA (Meat Standards Australia) phenotypic model, which predicts the eating quality of beef based on a combination of upstream and downstream data, is described. Its benefit for the beef industry has been extensively demonstrated in Australia, and its generic performance has already been proven in several countries.

Decision tree, a learning tool for the prediction of beef tenderness using rearing factors and carcass characteristics

“…During the last three decades and to better meet consumer expectations, especially in terms of perceived sensory eating quality, many different strategies have been developed. Thus, several research groups have tried to identify indicators explaining the uncontrolled variability with respect to predicting the tenderness potential of carcasses soon after slaughter . This has allowed numerous tenderness prediction tools to be proposed that were based on subjective or objective evaluations.…”

“…Thus, several research groups have tried to identify indicators explaining the uncontrolled variability with respect to predicting the tenderness potential of carcasses soon after slaughter. 6,11,12 This has allowed numerous tenderness prediction tools to be proposed that were based on subjective or objective evaluations. Several grading systems of carcasses (EUROP classification system, Australian Meat Standards, Canadian beef grading system, Japanese grading standards and United States Department of Agriculture grading system) have also been developed worldwide, 13 aiming to describe the quality and yield of carcasses and to ensure consistent meat quality and consumer satisfaction.…”

Near-Infrared Spectroscopy as a Beef Quality Tool to Predict Consumer Acceptance

“…The abilities of reflectance and transmittance NIRS to predict the intrinsic attributes of meat [22], and meat classifications during the adulteration/authentication process [57,58], have been widely documented. In comparison, the ability of NIRS to predict consumer sensory parameters related to beef has been less well documented, and contradictory results have been reported [59]. Until now, no other studies have examined the application of NIRS for the prediction of consumers' visual perceptions of beef attributes.…”

Current Advances in Meat Nutritional, Sensory and Physical Quality Improvement

“…In the second topic grouping of studies within the prediction of meat qualities, two papers by Berri et al [9] and Sahar et al [10] are published. The former is a review titled "Predicting the Quality of Meat: Myth or Reality?".…”

“…In the third topic that groups papers dealing with statistical approaches for meat quality prediction/management, three original papers are published [11][12][13] and they are complimentary to the previous topic by addressing some of the objectives reported by Berri et al [9] for the development of prediction/management tools of beef qualities. The first study by Ellies-Oury et al [11] presenta a new methodology for the selection of protein biomarkers of tenderness in five different bovine muscles using a multi-block model: the data-driven sparse partial least square.…”