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International audience; For decades, research has focused on predicting beef palatability using muscle biochemical traits and various bio-markers. In these approaches, a precise definition of the variable to predict (tenderness assessed by panelists, untrained consumers , or shear force) and repeatability of the measurements are crucial for creating significant data resources for the derivation of robust predictive models, as well as rigorous validation testing. This "big data" approach as well as the rapid evolution of new measurement technologies require careful definition of traits and transparent principles for data sharing and management. In this context, AgroPortal is a common platform to receive and host existing description systems, such as Animal Trait Ontology for Livestock. Furthermore, as in other fields, meat science researchers should improve the Findability, Accessibility, Interoperability, and Reusability of data (known as the FAIR Data Principles). For beef, strategic choices have been made in order to consider real consumers' expectations, not well estimated correctly by lab approaches. Thus, Australia set up the Meat Standards Australia grading scheme based on beef assessment by untrained consumers. The ambitions of the International Meat Research 3G Foundation is to develop beef ontology, to set up an international database with a large number of consumers' scores related to beef palatability and collected according to standard protocols of the Meat Standards Australia methodology. The Foundation also aims to support the beef industry by offering an international predictive model of beef palatability, flexible enough to take into account any local livestock characteristics or regional consumer specificity. This approach is supported by the United Nations Economic Commission for Europe, which promotes development of regulations and norms, technical cooperation, and exchange of best expertise and practices. This will substantially improve the transparency of data flow and price signaling between all participants of the value chain, from beef producers through to consumers at retail.

International audience; Intramuscular connective tissue (IMCT) is mainly composed of several fibrils (known as total collagen (TCol)) linked between each other by different chemical cross-links (CLs), the whole being embedded in a matrix of proteoglycans (PGs). In the field of beef quality, there is limited information on the role of CLs and PGs. Accordingly, several authors suggest that, to investigate the role of IMCT, it is important to investigate them just like TCol and insoluble collagen (ICol). In muscle, there are two other components, the muscle fibres and intramuscular fat (IMF) content. There are limited data on the relationships between these three components of muscle and then on possibility to independently manipulate these characteristics in order to control the final quality of meat. The present study aimed to investigate whether consistent relationships exist between these different components of muscle. Therefore, the present study compared four muscles of two cattle types (dairy and beef) to determine associations between TCol, ICol, CLs and PGs. Data were analysed across and within muscle (M) and animal type (AT) based on residuals. There was a strong M and AT effect for all muscle characteristics and an interaction M × AT for type I muscle fibres and IMF. Correlations between TCol, ICol and their CLs were M- and AT-independent. Total proteoglycans were positively correlated with TCol and ICol in a muscle-dependent manner irrespective of AT, but no correlation was found with CLs. On the contrary, CLs were negatively correlated with the ratio TPGs : TCol in an M-dependent manner, irrespective of AT. TCol, ICol and CLs were positively and negatively correlated with type IIA and IIBþX muscle fibres only in longissimus thoracis (LT) muscle, regardless the AT. Insoluble collagen was the only parameter of IMCT to be correlated with type I muscle fibres but only in LT muscle, irrespective of AT. There was no correlation between PGs and muscle fibre types, but PGs were the only IMCT component to be related with IMF in an M-dependent manner, irrespective of AT. Finally, there was no correlation between muscle fibre types and IMF content within M and AT. This study revealed that there is a strong relationship between IMCT components irrespective of M, an M-dependent relationship between the IMCT components and muscle fibre types and few (only with PGs) or no relationship between IMF and IMCT and muscle fibres.

This study evaluated the contributions of sensory traits to overall liking in Europe. Perceptions by untrained consumers of tenderness, juiciness, flavor liking and overall liking were determined using the Meat Standards Australia protocols. According to European consumer testing with European beef samples, flavor liking was the most important contributor (39%) to beef overall liking, followed by tenderness (31%) and juiciness (24%) (P0.94). The improvement in tenderness over the last decades may explain the highest contribution of flavor liking nowadays. Flavor liking is therefore the main driver of variability in overall liking. Juiciness is the least robust trait which could be influenced by other traits during consumer perception. For outstanding steaks, each sensory trait should have excellent scores and high contributions to overall liking. For medium cuts, one sensory trait with a low score has the potential to be compensated by other traits with higher scores and more emphasis will be placed on the trait with the lowest perception.

International audience; The aim of this study was to compare visible-near-infrared spectroscopy (VIS/NIRS) models developed from fresh or freeze-dried samples for predicting the fatty acid (FA) composition of beef samples. The hypothesis tested is that the removal of water from samples could improve the VIS/NIRS model performance. A total of 454 beef samples obtained from different bovine muscles were used. No significant differences were found in the performance of VIS/NIRS models developed from fresh or freeze-dried samples for predicting both major individual FAs and families of FAs and for some FAs (16:0, 18:0, 18:1 n-9, 18:2 n-6, 20:4 n-6, 22:5 n-3, 22:6 n-3, saturated, mono-unsaturated FA, and total n-3 long chain poly-unsaturated FAs (PUFA)). In contrast, the standard error of predictions for total PUFAs, total n-3 PUFAs, total conjugated linoleic acid, 20:5 n-3, and 18:3 n-3 were improved (by 21% on average; P < .05) in freeze-dried samples compared with fresh samples.

International audience; Delivering beef of consistent quality to the consumer is vital for consumer satisfaction and will help to ensure demand and therefore profitability within the beef industry. In Australia, this is being tackled with Meat Standards Australia (MSA), which uses carcass traits and processing factors to deliver an individual eating quality guarantee to the consumer for 135 different 'cut by cooking methods' from each carcass. The carcass traits used in the MSA model, such as ossification score, carcass weight and marbling explain the majority of the differences between breeds and sexes. Therefore, it was expected that the model would predict with eating quality of bulls and dairy breeds with good accuracy. In total, 8128 muscle samples from 482 carcasses from France, Poland, Ireland and Northern Ireland were MSA graded at slaughter then evaluated for tenderness, juiciness, flavour liking and overall liking by untrained consumers, according to MSA protocols. The scores were weighted (0.3, 0.1, 0.3, 0.3) and combined to form a global eating quality (meat quality (MQ4)) score. The carcasses were grouped into one of the three breed categories: beef breeds, dairy breeds and crosses. The difference between the actual and the MSA-predicted MQ4 scores were analysed using a linear mixed effects model including fixed effects for carcass hang method, cook type, muscle type, sex, country, breed category and postmortem ageing period, and random terms for animal identification, consumer country and kill group. Bulls had lower MQ4 scores than steers and females and were predicted less accurately by the MSA model. Beef breeds had lower eating quality scores than dairy breeds and crosses for five out of the 16 muscles tested. Beef breeds were also over predicted in comparison with the cross and dairy breeds for six out of the 16 muscles tested. Therefore, even after accounting for differences in carcass traits, bulls still differ in eating quality when compared with females and steers. Breed also influenced eating quality beyond differences in carcass traits. However, in this case, it was only for certain muscles. This should be taken into account when estimating the eating quality of meat. In addition, the coefficients used by the Australian MSA model for some muscles, marbling score and ultimate pH do not exactly reflect the influence of these factors on eating quality in this data set, and if this system was to be applied to Europe then the coefficients for these muscles and covariates would need further investigation.

The Meat Standards Australia (MSA) grading scheme has the ability to predict beef eating quality for each ‘cut×cooking method combination’ from animal and carcass traits such as sex, age, breed, marbling, hot carcass weight and fatness, ageing time, etc. Following MSA testing protocols, a total of 22 different muscles, cooked by four different cooking methods and to three different degrees of doneness, were tasted by over 19 000 consumers from Northern Ireland, Poland, Ireland, France and Australia. Consumers scored the sensory characteristics (tenderness, flavor liking, juiciness and overall liking) and then allocated samples to one of four quality grades: unsatisfactory, good-every-day, better-than-every-day and premium. We observed that 26% of the beef was unsatisfactory. As previously reported, 68% of samples were allocated to the correct quality grades using the MSA grading scheme. Furthermore, only 7% of the beef unsatisfactory to consumers was misclassified as acceptable. Overall, we concluded that an MSA-like grading scheme could be used to predict beef eating quality and hence underpin commercial brands or labels in a number of European countries, and possibly the whole of Europe. In addition, such an eating quality guarantee system may allow the implementation of an MSA genetic index to improve eating quality through genetics as well as through management. Finally, such an eating quality guarantee system is likely to generate economic benefits to be shared along the beef supply chain from farmers to retailors, as consumers are willing to pay more for a better quality product.

Accurately quantifying a consumer’s willingness to pay (WTP) for beef of different eating qualities is intrinsically linked to the development of eating-quality-based meat grading systems, and therefore the delivery of consistent, quality beef to the consumer. Following Australian MSA (Meat Standards Australia) testing protocols, over 19 000 consumers from Northern Ireland, Poland, Ireland, France and Australia were asked to detail their willingness to pay for beef from one of four categories that best described the sample; unsatisfactory, good-every-day, better-than-every-day or premium quality. These figures were subsequently converted to a proportion relative to the good-every-day category (P-WTP) to allow comparison between different currencies and time periods. Consumers also answered a short demographic questionnaire. Consumer P-WTP was found to be remarkably consistent between different demographic groups. After quality grade, by far the greatest influence on P-WTP was country of origin. This difference was unable to be explained by the other demographic factors examined in this study, such as occupation, gender, frequency of consumption and the importance of beef in the diet. Therefore, we can conclude that the P-WTP for beef is highly transferrable between different consumer groups, but not countries.

International audience; Quantifying consumer responses to beef across a broad range of demographics, nationalities and cooking methods is vitally important for any system evaluating beef eating quality. On the basis of previous work, it was expected that consumer scores would be highly accurate in determining quality grades for beef, thereby providing evidence that such a technique could be used to form the basis of and eating quality grading system for beef. Following the Australian MSA (Meat Standards Australia) testing protocols, over 19 000 consumers from Northern Ireland, Poland, Ireland, France and Australia tasted cooked beef samples, then allocated them to a quality grade; unsatisfactory, good-every-day, better-than-every-day and premium. The consumers also scored beef samples for tenderness, juiciness, flavour-liking and overall-liking. The beef was sourced from all countries involved in the study and cooked by four different cooking methods and to three different degrees of doneness, with each experimental group in the study consisting of a single cooking doneness within a cooking method for each country. For each experimental group, and for the data set as a whole, a linear discriminant function was calculated, using the four sensory scores which were used to predict the quality grade. This process was repeated using two conglomerate scores which are derived from weighting and combining the consumer sensory scores for tenderness, juiciness,flavour-liking and overall-liking, the original meat quality 4 score (oMQ4) (0.4, 0.1, 0.2, 0.3) and current meat quality 4 score (cMQ4) (0.3, 0.1, 0.3, 0.3). From the results of these analyses, the optimal weightings of the sensory scores to generate an ideal meat quality 4 score (MQ4) ’ for each country were calculated, and the MQ4 values that reflected the boundaries between the four quality grades were determined. The oMQ4 weightings were far more accurate in categorising European meat samples than the cMQ4 weightings, highlighting that tenderness is more important than flavour to the consumer when determining quality. The accuracy of the discriminant analysis to predict the consumer scored quality grades was similar across all consumer groups, 68%, and similar to previously reported values. These results demonstrate that this technique, as used in the MSA system, could be used to predict consumer assessment of beef eating quality and therefore to underpin a commercial eating quality guarantee for all European consumers.

Ossification score and animal age are both used as proxies for maturity-related collagen crosslinking and consequently decreases in beef tenderness. Ossification score is strongly influenced by the hormonal status of the animal and may therefore better reflect physiological maturity and consequently eating quality. As part of a broader cross-European study, local consumers scored 18 different muscle types cooked in three ways from 482 carcasses with ages ranging from 590 to 6135 days and ossification scores ranging from 110 to 590. The data were studied across three different maturity ranges; the complete range of maturities, a lesser range and a more mature range. The lesser maturity group consisted of carcasses having either an ossification score of 200 or less or an age of 987 days or less with the remainder in the greater maturity group. The three different maturity ranges were analysed separately with a linear mixed effects model. Across all the data, and for the greater maturity group, animal age had a greater magnitude of effect on eating quality than ossification score. This is likely due to a loss of sensitivity in mature carcasses where ossification approached and even reached the maximum value. In contrast, age had no relationship with eating quality for the lesser maturity group, leaving ossification score as the more appropriate measure. Therefore ossification score is more appropriate for most commercial beef carcasses, however it is inadequate for carcasses with greater maturity such as cull cows. Both measures may therefore be required in models to predict eating quality over populations with a wide range in maturity.

International audience; Despite efforts by the industry to control the eating quality of beef, there remains a high level of variability in palatability, which is one reason for consumer dissatisfaction. In Europe, there is still no reliable on-line tool to predict beef quality and deliver consistent quality beef to consumers. Beef quality traits depend in part on the physical and chemical properties of the muscles. The determination of these properties (known as muscle profiling) will allow for more informed decisions to be made in the selection of individual muscles for the production of value-added products. Therefore, scientists and professional partners of the ProSafeBeef project have brought together all the data they have accumulated over 20years. The resulting BIF-Beef (Integrated and Functional Biology of Beef) data warehouse contains available data of animal growth, carcass composition, muscle tissue characteristics and beef quality traits. This database is useful to determine the most important muscle characteristics associated with a high tenderness, a high flavour or generally a high quality. Another more consumer driven modelling tool was developed in Australia: the Meat Standards Australia (MSA) grading scheme that predicts beef quality for each individual muscle×specific cooking method combination using various information on the corresponding animals and post-slaughter processing factors. This system has also the potential to detect variability in quality within muscles. The MSA system proved to be effective in predicting beef palatability not only in Australia but also in many other countries. The results of the work conducted in Europe within the ProSafeBeef project indicate that it would be possible to manage a grading system in Europe similar to the MSA system. The combination of the different modelling approaches (namely muscle biochemistry and a MSA-like meat grading system adapted to the European market) is a promising area of research to improve the prediction of beef quality. In both approaches, the volume of data available not only provides statistically sound correlations between various factors and beef quality traits but also a better understanding of the variability of beef quality according to various criteria (breed, age, sex, pH, marbling etc.).

Eighty eight lambs were used in a 2 x 2 factorial arrangement 1) to investigate the effect of maternal dietary linseed supplementation and /or lamb linseed supplemented concentrate on growth performance, carcass fat quality and fatty acid (FA) composition of muscle and dorsal adipose tissue of indoor lambs 2) to study the relationships between subcutaneous fat quality and FA composition. Feeding linseed to ewes increased C18:3 n-3 (ALA) proportion in milk and therefore the ALA supply to suckling lambs. However, ALA and n-3 polyunsaturated FA (n-3 PUFA) proportions in lamb tissues were not affected. Feeding linseed to lambs during the post-weaning period significantly increased the proportions of ALA and n-3 PUFA in tissues. Softer and more coloured fat was associated with a decrease in even medium-chain saturated FA and increases in odd and methyl FA proportions but not with ALA proportion in subcutaneous adipose tissue.

The BIF-BEEF (Beef Integrated and Functional Biology) data warehouse for muscle biology to predict beef quality gathers data related to bovines, their carcasses and their beef. These data come mainly from three sources: the INRA database named FiLiCol, the European GEMQUAL program and the French QUALVIGENE program databases plus other minor sources. At the beginning of 2011, the BIF-BEEF data warehouse contained 331 745 measurements for 621 variables. Measurements were obtained on eight muscles and/or from 5197 animals (mainly young bulls) belonging to 20 different breeds (mainly Charolais, Limousin, Blonde d’Aquitaine, the three major French beef breeds) from experiments carried out over a 10-year period. A web interface was developed to extract data and to analyse them using basic statistical tools (correlation, variance analysis, etc) with R software. Clearly, since the various experiments were not designed initially to ultimately link together, it appeared very difficult to integrate some data which differ a lot by units, scales or laboratory methods. Ontology will help to address these issues. However, the usefulness of the BIF-BEEF data warehouse is described by studying the relationship in M. longissimus thoracis between intramuscular fat content (IMF) and flavour assessed by sensory panels. When data from different sources or different sensory panels were used, they were corrected for these fixed factors in the regression model. They were also corrected for known sources of variation (sex, breed and age of the animals). On average, the relationship between IMF and flavour was low (partial correlation coefficient r = 0.11) but significant. This relationship was no more significant for breeds with low IMF levels (such as Blonde d’Aquitaine) or for animals with the highest IMF such as steers or females.