Browsing by Author "Chriki, Sghaier"

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    Contributions of tenderness, juiciness and flavor liking to overall liking of beef in Europe
    (Elsevier, 2020-05-19) Liu, Jingjing; Ellies-Oury, Marie-Pierre; Chriki, Sghaier; Legrand, Isabelle; Pogorzelski, Grzegorz; Wierzbicki, Jerzy; Farmer, Linda J.; Troy, Declan; Polkinghorne, Rod; Hocquette, Jean-Francois
    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%) (P < .05; R2 > 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.
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    Various Statistical Approaches to Assess and Predict Carcass and Meat Quality Traits
    (MDPI, 2020-04-22) Ellies-Oury, Marie-Pierre; Hocquette, Jean-François; Chriki, Sghaier; Conanec, Alexandre; Farmer, Linda; Chavent, Marie; Saracco, Jérôme
    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 together