Effect of residual feed intake on meat quality in fattening Charolais bulls fed two contrasting diets.

The selection of more efficient animals for breeding is of both economic and environmental interest to the industry. The aim of this study was to evaluate the influence of the animals' residual feed intake (RFI) ranking in interaction with the type of diet on the meat quality of Charolais beef cattle. Indeed, several biological mechanisms are associated with RFI, especially when animals are fed high starch-diets. It is therefore possible that quality parameters may show greater changes due to RFI in the context of high starch diets compared to high forage diets. An 84-day feed efficiency trial followed immediately by a second 112-day feed efficiency trial was conducted with a total of 100 animals fed either maize- or grass-diets for 196-days. At the end of the 84-day period, the 32 most divergent RFI animals (16 extreme RFI animals per diet, 8 RFI+ and 8 RFI-) were identified. They were slaughtered after 112-days of finishing. The Longissimus thoracis was characterised in terms of nutritional and sensory quality. RFI had no effect on lab colour, muscle shear force, total fat, fatty acid ratios and most of the total fatty acid content (especially n-3) irrespective of the diet. However, more efficient animals (RFI-) showed higher CLA contents compared to less efficient animals (RFI+) regardless of the diet and also a lower n6/n3 ratio only in animals fed the maize diets. Diet also had a significant effect on lipid and FA content as well as on FA composition.

Review: On-farm and processing factors affecting bovine carcass and meat quality.

This paper reviews the current state of knowledge on beef carcass and meat quality, with particular emphasis on on-farm and processing factors associated with its high and inconsistent variability. The diversity of livestock systems comes from the diversity of breeds (dairy or beef), ages and gender (bulls, steers, heifers, cull cows) used to produce either mainly beef or beef and milk. In addition, there are factors linked to farming practices (including diet, especially grazing) which significantly influence the sensory, nutritional, technological and extrinsic (such as image) quality attributes of meat. These can become factors of positive differentiation when controlled by the application and certification of technical specifications. Finally, preslaughter (such as stress), slaughter (such as the chilling and hanging method of carcasses) and postslaughter (such as ageing, packaging and cooking) conditions have a strong influence on the microbiological, sensory, technological and image quality attributes of beef. In this review, potential synergisms or antagonisms between the different quality attributes are highlighted. For example, finishing cattle on grass, compared to indoor fattening on a high concentrate diet, has the advantage of producing leaner meat with a higher proportion of omega-3 fatty acids while exhibiting superior oxidative stability, but with the consequence of a darker meat colour and lower productivity, as well as higher seasonality and land surface requirements. Moreover, the control of on-farm factors is often guided by productivity (growth rate, feed conversion ratio) and carcass quality attributes (weight, conformation and fatness). Genetic selection has often been oriented in this direction, without taking other quality attributes into account. Finally, the interactions between all these factors (and especially between on-farm and slaughter or processing factors) are not considered in the quality grading schemes in European countries. This means that positive efforts at farm level may be mitigated or even eliminated by poor slaughtering or processing conditions. All these considerations explain why between-animal variability in quality can be high, even when animals come from the same farming system. The ability to predict the sensory and nutritional properties of meat according to production factors has become a major objective of the supply chain.

What is the impact of the rearing management applied during the heifers' whole life on the toughness of five raw rib muscles in relation with carcass traits?

The aims of this study were, analysing the effects of rearing managements, carcass traits, and muscle type (M. complexus [CP], M. infraspinatus [IF], M. longissimus [LM], M. rhomboideus [RH], and M. serratus ventralis [SV]) on toughness of raw meat; developing prediction models to act on their toughness. According to our results obtained on the data of 77 heifers, the IF raw muscle was the toughest and appeared the most sensitive to a change in the rearing management. The four other raw muscles had a similar toughness within heifers from the same rearing management. The five raw muscles were less tough when the carcass was heavier and had higher dressing percentage and conformation. The 3 models explained about 40% of the variability observed. Our models showed that it is possible to improve the potential tenderness of raw meat, acting on: age of the heifer's mother, growth rate during the growth and fattening periods, slaughter age, carcass weight and temperature 24 h post-mortem.

Statistical model choice including variable selection based on variable importance: A relevant way for biomarkers selection to predict meat tenderness.

In this paper, we describe a new computational methodology to select the best regression model to predict a numerical variable of interest Y and to select simultaneously the most interesting numerical explanatory variables strongly linked to Y. Three regression models (parametric, semi-parametric and non-parametric) are considered and estimated by multiple linear regression, sliced inverse regression and random forests. Both the variables selection and the model choice are computational. A measure of importance based on random perturbations is calculated for each covariate. The variables above a threshold are selected. Then a learning/test samples approach is used to estimate the Mean Square Error and to determine which model (including variable selection) is the most accurate. The R package modvarsel (MODel and VARiable SELection) implements this computational approach and applies to any regression datasets. After checking the good behavior of the methodology on simulated data, the R package is used to select the proteins predictive of meat tenderness among a pool of 21 candidate proteins assayed in semitendinosus muscle from 71 young bulls. The biomarkers were selected by linear regression (the best regression model) to predict meat tenderness. These biomarkers, we confirm the predominant role of heat shock proteins and metabolic ones.

Effect of age and carcass weight on quality traits of m. rectus abdominis from Charolais heifers.

In practice cattle may be slaughtered at different combinations of age and weight. As each of these factors could affect meat quality traits, the present work aimed to identify which combination can be expected to increase overall meat quality of m. rectus abdominis of Charolais heifers. Totally, 40 heifers were slaughtered either at 26±1 or at 36±1 months of age. Young heifers were sampled at two different carcass weights (349±12 and 394±8 kg). Old heifers were also sampled at two different carcass weights (397±6 and 451±9 kg). The m. rectus abdominis was excised 24 h postmortem to determine metabolic enzyme activities, myosin heavy-chain isoform proportions, lipid contents, collagen content and collagen solubility. Shear force measurements were evaluated on raw and broiled meat after 14 days of ageing. Meat quality traits scored between 0 and 10 by sensory analysis. Increasing slaughter age from 26 to 36 months had no impact on either raw/broiled shear force (0.31⩽P⩽0.47) and/or meat quality traits (0.62⩽P⩽0.91) or on physicochemical properties of heifer's meat samples. Increasing carcass weight for a similar slaughter age of 26 months had also impact neither on meat quality traits (0.52⩽P⩽0.91) nor on muscular properties. On the contrary, increasing carcass weight for a similar slaughter age of 36 months had induced a decrease of muscular shear force (raw muscle; P=0.009) and a concomitant decrease of total collagen content (P=0.03). Nevertheless, no significant impact on meat quality traits was revealed by the sensorial panel (0.13⩽P⩽0.49). Metabolic enzyme activities (0.13⩽P⩽0.86) and myosin heavy-chain proportions (0.13⩽P⩽0.96) were not significantly impacted by slaughter age and carcass weight. Thus, the impact of increasing carcass weight and/or slaughter age in young Charolais heifers has a limited impact on meat quality traits and associated muscular characteristics. Modulating heifer's cycles (age and/or carcass weight in the studied range) appears to be a way to answer to the numerous marketing chains, without penalising meat quality traits.

An innovative approach combining Animal Performances, nutritional value and sensory quality of meat.

This work sets out a methodological approach to assess how to simultaneously control together Animal Performances, nutritional value, sensory quality of meat. Seventy-one young bulls were characterized by 97 variables. Variables of each element were arranged into either 5 homogeneous Intermediate Scores (IS) or 2 Global Indices (GI) via a clustering of variables and analysed together by Principal Component Analysis (PCA). These 3 pools of 5 IS (or 2 GI) were analysed together by PCA to established the links existing among the triptych. Classification on IS showed no opposition between Animal Performances and nutritional value of meat, as it seemed possible to identify animals with a high butcher value and intramuscular fat relatively rich in polyunsaturated fatty acids. Concerning GI, the classification indicated that Animal Performances were negatively correlated with sensory quality. This method appeared to be a useful contribution to the management of animal breeding for an optimal trade-off between the three elements of the triptych.

Relationships between the assessment of "grain of meat" and meat tenderness of Charolais cattle.

Beef industry operators often use a "grain of meat" assessment to aid in selecting carcasses according to their tenderness potential. The first aim of this study was to formalise this empirical notion by building a scoring scale for grain of meat. Two experts, regular users of this notion, were called upon to build the assessment grade. A group of 16 criteria was identified on the carcass as being effective in predicting an overall "grain of meat" score. The second aim of this study was to establish the relationship between the meat grain score estimated on the carcass and the tenderness of the longissimus thoracis muscle estimated by sensory evaluations. Some individual criteria such as "nerve presence", "marbling", and "touch of muscles" appeared to be linked to muscular characteristics such as collagen content, lipid content and shear force. No significant relationship was identified between grain of meat and either tenderness (initial: p=0.58; overall: p=0.50), shear force (p=0.33), or collagen content and collagen solubility (p=0.23; p=0.33).

Interrelationships between meat quality traits, texture measurements and physicochemical characteristics of M. rectus abdominis from Charolais heifers.

Ninety-nine Charolais heifers were used to study the variability of meat quality traits in relation to the physicochemical characteristics of M. rectus abdominis. The heifers of the same trade class were slaughtered at 33months of age (±4months) and 381kg carcass weight (±31kg). Muscle and bone development scores were evaluated before slaughter. Carcass weight, slaughter age and life average daily gain were recorded. Shear force measurements and meat quality traits were evaluated after 14days of aging. Some physicochemical characteristics were measured 24h post-slaughter. Tenderness was correlated with slaughter age (r=-0.31), bone development (r=-0.22) and life average daily gain (r=+0.37). Tenderness was significantly related to total collagen content (r=-0.24), lipid content (r=+0.27) and I myosin heavy chain proportion (r=+0.24). Juiciness was positively correlated with lipid content (r=+0.31) and I myosin heavy chain proportion (r=+0.20). Flavor intensity was correlated with lipid content (r=+0.26) and mean fiber area (r=+0.24). Shear force was correlated with total collagen, lipid and 27K proteasome sub-unit contents. Taking animal characteristics and muscle properties together in a multiple regression analysis increased the explained tenderness variability to 33%. The independent variables listed in order of importance were life average daily gain, total collagen content, bone development, lipid content, I myosin heavy chain isoform proportion, shear force of broiled meat and slaughter age.