Showing papers by "John Mitchell Thompson published in 2021"

Does a single adjustment in the meat standards Australia beef grading model cater for different hormonal growth promotant formulations

Abstract: This paper investigated whether a single Hormonal Growth Promotant (HGP) adjustment in the Meat Standards Australia (MSA) beef grading model adequately predicted consumer eating quality of beef from cattle treated with different HGP formulations. This paper used consumer sensory data from two experiments. In experiment one, a total of 300 steers were allocated to three treatments; control (CON-100-F), 100 day oestradiol only HGP (OES-100-F), or a combination of trenbolone acetate and oestradiol HGP (TBA+OES-100-F) and finished in a feedlot for 73 days. In experiment two, a total of 200 steers were allocated either control or 400 day oestradiol only HGP treatments and finished on pasture for 389 days. Steers were slaughtered by finishing regime and carcass traits recorded. The anterior and posterior portions of the m. longissimus lumborum (LL-A and LL-P, respectively) and m. gluteus medius (GM) were collected and aged for five or 35 days. Grilled meat samples were scored for tenderness, juiciness, liking of flavour and overall acceptability using untrained consumers. Sensory scores were weighted by 0.3. 0.1, 0.3 and 0.3, respectively and summed to calculate a meat quality (MQ4) score. Residual MQ4 scores were calculated (observed MQ4 minus the predicted MQ4 score). The MSA model accounts for varied impacts of different HGPs on eating quality through a single HGP adjustment, and indirect impacts on carcass traits. For the majority of the HGP treatment samples, the residual MQ4 scores were not different to zero (5/18), or were positive i.e. the MSA model under-predicted these samples (11/18). Under-prediction was predominately for 35 day aged (7/9) and GM HGP treatment samples (6/6) and was considered low, with the majority less than ±5 MQ4 units. Under-prediction could be considered as advantageous through providing an additional safeguard to protect the interests of the consumers, rather than if the model had over-predicted and resulted in a more negative eating quality experience than expected. Some over-prediction was observed in the CON-100-F and TBA+OES-100-F treatment samples, which may be due to factors such as genetic variation and/or production environment. Minimal bias was observed when residual MQ4 was regressed against predicted MQ4 for the range of feeding regimes, muscles, ageing periods and treatment groups. This study showed that a single HGP adjustment in the MSA beef grading model, combined with the indirect effects of the different HGP formulations on carcass traits, provided a reasonable prediction of meat eating quality for different HGP formulations.