Perception of cultured “meat” by Italian, Portuguese and Spanish consumers

The aim of this study was to investigate how consumers (n = 2,171) originated from South-Western Europe (Italy, Portugal, and Spain) perceive cultured “meat” (CM) and if their demographic characteristics (origin, gender, age, education, occupation, and meat consumption) are related to their willingness to try (WTT), to regularly eat (WTE) and to pay (WTP) for CM. We found the current respondents had an initially positive attitude towards CM: 49% of them perceived CM as “promising and/or acceptable” and 23% “fun and/or intriguing” whereas 29% considered it as “absurd and/or disgusting”. In addition, 66 and 25% would be willing and not willing to try CM, respectively. However, 43% had no WTE for CM and, 94% would not pay more for CM compared to conventional meat. Age and especially occupation were good indicators of consumer acceptance of CM. Respondents of 18–30 years of age had the highest acceptance. Respondents outside the meat sector had the highest WTE and people working within the meat sector had the lowest WTE, scientists (within or outside the meat sector) had the highest WTT, people not scientists but within the meat sector had the lowest WTT. Additionally, we found that men are more likely to accept CM than women, Spanish-speaking consumers had the highest WTT and WTE, people with vegan and vegetarian diets may pay more for CM but generally no more than for conventional meat. The perceptions that CM may be more eco-friendly, ethical, safe and healthy than conventional meat, and to a lower extent, the perception that current meat production causes ethical and environmental problems are likely to be major motives for the current respondents to try, regularly eat and pay for CM. On the opposite, lower perceptions of CM benefits and of conventional meat weaknesses more generally, plus emotional resistance towards CM are main barriers to accept CM

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

Publication history: Accepted - 8 April 2020; Published - 22 April 2020.The beef industry is organized around di erent stakeholders, each with their own expectations, sometimes antagonistic. This article first outlines these di ering 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. Di erent 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 di erent parameters of interest. Finally, some principles for the management of quality trade-o s are presented and the Meat Standards Australia model is discussed. The “Pareto front” is an interesting approach to deal jointly with the di erent sets of expectations and to propose a method that could optimize all expectations togethe

Current situation and future prospects for beef production in Europe — A review

Publication history: Accepted - 26 April 2018; Published online - 24 May 2018.The European Union (EU) is the world’s third largest producer of beef. This contributes to the economy, rural development, social life, culture and gastronomy of Europe. The diversity of breeds, animal types (cows, bulls, steers, heifers) and farming systems (intensive, extensive on permanent or temporary pastures, mixed, breeders, feeders, etc) is a strength, and a weakness as the industry is often fragmented and poorly connected. There are also societal concerns regarding animal welfare and environmental issues, despite some positive environmental impacts of farming systems. The EU is amongst the most efficient for beef production as demonstrated by a relative low production of greenhouse gases. Due to regional differences in terms of climate, pasture availability, livestock practices and farms characteristics, productivity and incomes of beef producers vary widely across regions, being among the lowest of the agricultural systems. The beef industry is facing unprecedented challenges related to animal welfare, environmental impact, origin, authenticity, nutritional benefits and eating quality of beef. These may affect the whole industry, especially its farmers. It is therefore essential to bring the beef industry together to spread best practice and better exploit research to maintain and develop an economically viable and sustainable beef industry. Meeting consumers’ expectations may be achieved by a better prediction of beef palatability using a modelling approach, such as in Australia. There is a need for accurate information and dissemination on the benefits and issues of beef for human health and for environmental impact. A better objective description of goods and services derived from livestock farming is also required. Putting into practice “agroecology” and organic farming principles are other potential avenues for the future. Different future scenarios can be written depending on the major driving forces, notably meat consumption, climate change, environmental policies and future organization of the supply chain

Contributions of tenderness, juiciness and flavor liking to overall liking of beef in Europe

peer-reviewedThis 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%) (P 0.94). 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.European Commissio

La race a-t-elle un effet sur la qualité sensorielle de la viande de jeune bovin?

Dans le cadre du consortium européen Gemqual, 436 jeunes bovins issus de 15 races bovines différentes ont été conduits dans des systèmes d'élevage similaires afin d'évaluer l'impact de la race sur la qualité organoleptique de la viande déterminée par analyse sensorielle. Une comparaison de deux méthodes statistiques pour traiter les données de l'analyse sensorielle a tout d'abord été réalisée. L'analyse de variance avec ou sans effet dégustateur a abouti à des résultats similaires indiquant que ce choix méthodologique n'est pas décisif pour l'interprétation des résultats. Une classification non supervisée (classification ascendante hiérarchique) a ensuite permis de classer les races en fonction de trois profils sensoriels sur la base de 4 descripteurs (tendreté, jutosité, intensité de flaveur et flaveur anormale). Elle permet de mettre en évidence 5 associations de races. : Les races Aberdeen Angus, Highland et Jersey, qui ont une teneur élevée en lipides dans le muscle étudié (Longissimus thoracis), se sont distinguées des autres races par une flaveur de bœuf plus élevée. Les races mixtes et rustiques, Simmental, Casina et Marchigiana, ont produit une viande significativement moins juteuse et moins tendre que celle des races sélectionnées pour la production de viande. Les trois autres associations suivantes rassemblent les races Limousine et Charolaise dont le profil semble assez proche, les races Pirenaica et Avilena tendres et fortement appréciées par les panélistes, les races Asturiana de los Valles et Piemontaise caractérisées par une flaveur anormale plus intense. Dans l'ensemble, malgré des différences significatives de caractéristiques de l'animal, de la carcasse et du muscle, les différences de qualité sensorielle entre la plupart des races étaient faibles, avec seulement des différences significatives entre les quelques races qui présentaient des profils sensoriels extrêmes (comme la Simmental et la Pirenaica).PublishedCet article correspond à une étude publiée dans Livestock Science 250 (2021) 104548. Elle démontre qu’il existe peu de différences de qualité sensorielle entre races bovines bien que les races rustiques ou mixtes tendent à produire une viande légèrement moins tendre et moins juteuse et les races les plus grasses une viande avec plus de goû

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

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

Sélection de biomarqueurs pour prédire la tendreté de la viande de boeuf basée sur l'importance des variables

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Une méthodologie computationnelle pour faire de l’optimisation multi-objectifs en élevage de précision

International audienceIn precision rearing, optimization problems are multi-objective and stochastic because the requirements of decision-makers are multiple and because objective functions cannot be modeled in an analytical form due to the inherent complexity of biological systems. Our method consists in use a nonparametrically estimated quantile regression, associated with a $\alpha$ risk level decided by the decision maker, to deal with the model uncertainty. Then, the NSGAII genetic algorithm allows us to find the Pareto Front, associated with a $\alpha$ risk level, which carries the set of possible trade-offs within which the decision-maker can choose. The good numerical behavior of the proposed approach is illustrated on simulated data.En élevage de précision, les problèmes d’optimisation sont multi-objectifs et stochastiques. En effet, les exigences des décideurs sont multiples et les fonctions objectifs ne peuvent être modélisées sous une forme analytique dû à la complexité inhérente des systèmes biologiques. La méthodologie proposée consiste à utiliser des quantiles conditionnels, estimés non paramétriquement, associés à différents niveaux de risque $\alpha$ choisis parle décideur, pour intégrer l’incertitude du modèle. Pour un risque $\alpha$ donné, l’algorithme génétique NSGAII permet ensuite de déterminer le Front de Pareto, qui porte l’ensemble des compromis possibles au sein duquel le décideur peut alors choisir. Le bon comportement numérique de l’approche développée est illustré sur une simulation numérique

Une méthodologie computationnelle pour faire de l’optimisation multi-objectifs en élevage de précision

En élevage de précision, les problèmes d’optimisation sont multi-objectifs et stochastiques. En effet, les exigences des décideurs sont multiples et les fonctions objectifs ne peuvent être modélisées sous une forme analytique dû à la complexité inhérente des systèmes biologiques. La méthodologie proposée consiste à utiliser des quantiles conditionnels, estimés non paramétriquement, associés à différents niveaux de risque $\alpha$ choisis parle décideur, pour intégrer l’incertitude du modèle. Pour un risque $\alpha$ donné, l’algorithme génétique NSGAII permet ensuite de déterminer le Front de Pareto, qui porte l’ensemble des compromis possibles au sein duquel le décideur peut alors choisir. Le bon comportement numérique de l’approche développée est illustré sur une simulation numérique.In precision rearing, optimization problems are multi-objective and stochastic because the requirements of decision-makers are multiple and because objective functions cannot be modeled in an analytical form due to the inherent complexity of biological systems. Our method consists in use a nonparametrically estimated quantile regression, associated with a $\alpha$ risk level decided by the decision maker, to deal with the model uncertainty. Then, the NSGAII genetic algorithm allows us to find the Pareto Front, associated with a $\alpha$ risk level, which carries the set of possible trade-offs within which the decision-maker can choose. The good numerical behavior of the proposed approach is illustrated on simulated data

Une approche permettant de maîtriser le niveau de confiance en optimisation multi-objectifs « data-driven »

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