A multivariate model, incorporating all key significant factors, is needed to underpin an eating quality system to deliver consistently high quality pork to consumers, in both export and domestic markets.Ī Monte Carlo simulation approach, an alternative to meta-analysis, was used as an attempt to quantify the size, effect and variability in pork eating quality traits in response to different factors imposed from production to consumption ( Channon et al., 2016b). However, an approach to quantitatively review, and analyze, the size of different treatment effects within a factor on pork eating quality attributes to establish a cuts-based predictive model for pork eating quality has not been reported. These analyses generated very useful outcomes on the size of individual factors on pork eating quality.
The use of porcine somatotropin, ractopamine and conjugated linoleic acid ( Dunshea et al., 2005) and gender ( Trefan et al., 2013) on tenderness and juiciness has also been quantified using a meta-analysis approach. It has enabled generalized treatment effects to be obtained when outcomes from independent, randomized studies are conflicting ( Thompson and Higgins, 2002). Meta-analyses methodology has been applied to obtain single summarized effect sizes of various experimental treatments applied to pigs, carcases and/or pork, primarily for technological quality attributes ( Dunshea et al., 2005 Salmi et al., 2010 Trefan et al., 2011 Trefan, 2011 Pauly et al., 2012 Salmi et al., 2012). In the development of any eating quality system, it must be flexible and non-prescriptive in its approach to support its adoption by industry and allow individual supply chains to select those factors that will enable eating quality targets to be met. This has been extensively described in qualitative reviews, both in Australia and overseas (including D'Souza and Mullan, 2001 Taverner, 2001 Rosenvold and Andersen, 2003 Ngapo and Gariepy, 2008 Channon and Warner, 2011). This presents significant challenges to industry as production, processing, post-slaughter, and cooking parameters can, singularly or in combination, influence tenderness, juiciness and flavor of pork. The ability of pork supply chains to consistently provide high quality pork products to their customers is paramount to improve consumer satisfaction and increase pork consumption frequency ( Channon and Warner 2011).
It was not possible to develop algorithms to reliably estimate the effects of multiple factors on pork eating quality attributes to a cuts-based level due to limited studies reporting data for treatment interactions. Cooking loins to an endpoint temperature of ≥ 80☌ negatively impacted both tenderness ( P = 0.022) and juiciness ( P < 0.001) scores compared with 70 to 74☌. This analysis identified that moisture infusion ( P < 0.001), ageing for more than 2 d post-slaughter ( P = 0.006) and tenderstretching ( P = 0.006) each resulted in significant improvements in tenderness. Due to a lack of data for interactions between factors investigated, only single factors were included as fixed terms in the REML models. Estimated means of explanatory variables were obtained for those pathway factors where sufficient published studies met the criteria for inclusion in the analysis.
This was done to inform the development of a pathway based eating quality system for pork. Random effects meta-regression techniques, analyzed using a restricted maximum likelihood (REML) approach, was used to determine the influence of various factors that may be experienced or imposed on pigs, carcases and pork on pork eating quality attributes and shear force of the M.
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