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

文献类型： 外文期刊

作者： Zou, Ye 1 ; Zhang, Kun 1 ; Zhang, Xin Xiao 1 ; Li, Pengpeng 1 ; Zhang, Muhan 1 ; Liu, Fang 1 ; Sun, Chong 1 ; Xu, Weimin; 1 ;

作者机构： 1.Jiangsu Acad Agr Sci, Inst Agr Prod Proc, Nanjing 210014, Jiangsu, Peoples R China

关键词： artificial neural network; optimization; response surface methodology; tenderness; ultrasound treatment

期刊名称：ANIMAL SCIENCE JOURNAL （ 影响因子：1.749； 五年影响因子：1.909 ）

ISSN： 1344-3941

年卷期： 2018 年 89 卷 9 期

页码：

收录情况： SCI

摘要： 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 (1862.6g and 1869.9g) was in reasonable agreement with predicted one. Nevertheless, better prediction capability of ANN was proved by higher R-2 (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 812W, 24.5min and 25.7hr, 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.

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