Optimization of goose breast meat tenderness by rapid ultrasound treatment using response surface methodology and artificial neural network

Abstract

The aim of this study was to develop a prediction model on tenderization of goose breast meat by response surface methodology (RSM) and artificial neural network (ANN). The experiments were operated on the basis of a three-level, three-variable (ultrasound power, ultrasound time, and storage time) Box-Behnken experimental design. Under RSM and ANN optimum conditions, experimental Meullenet-Owens razor shear (MORS) of meat (1862.6 g and 1869.9 g) was in reasonable agreement with predicted one. Nevertheless, better prediction capability of ANN was proved by higher R (0.996) and lower absolute average deviation = 4.257) compared to those for RSM (0.852 and 16.534), respectively. These results revealed that ANN was more accurate and much better than RSM model for the optimization of tenderness of meat. The optimum conditions of ultrasound power, ultrasound time, and storage time given by ANN were 812 W, 24.5 min and 25.7 hr, respectively. Under the optimized condition, the cooking loss of meat significantly decreased by ultrasound treatment compared with untreated meat. Lower cooking loss and MORS at the optimal condition were beneficial to meet the satisfaction of consumer and producers for meat factory.

Modelling inactivation of Staphylococcus spp. on sliced Brazilian dry-cured loin with thermosonication and peracetic acid combined treatment

“…The implosion process generates (i) micro-jets capable of breaking the cell wall, (ii) free radicals and (iii) hot-spots capable of inactivating microorganisms (Awad et al 2012;Gogate and Kabadi, 2009). Recently, several studies have evaluated the effect of US on meat quality and processing (Barekat and Soltanizadeh, 2018;Inguglia et al, 2018;Ojha et al, 2016;Zou et al, 2018a;Zou et al, 2018b). In addition, high and moderate temperatures in combination with US (thermosonication) were extensively studied Evelyn et al 2017;Evelyn and Silva, 2015a;Evelyn and Silva, 2018).…”