Zou Ye, Zhang Kun, Zhang Xin Xiao, Li Pengpeng, Zhang Muhan, Liu Fang, Sun Chong, Xu Weimin, Wang Daoying
Institute of Agricultural Products Processing, Jiangsu Academy of Agricultural Sciences, Nanjing, China.
Anim Sci J. 2018 Sep;89(9):1339-1347. doi: 10.1111/asj.13061. Epub 2018 Jun 29.
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.
本研究旨在通过响应面法（RSM）和人工神经网络（ANN）建立鹅胸肉嫩化预测模型。实验基于三水平、三变量（超声功率、超声时间和储存时间）的Box-Behnken实验设计进行。在RSM和ANN的最佳条件下，肉的实验梅勒内特-欧文斯剃刀剪切力（MORS）（分别为1862.6克和1869.9克）与预测值合理相符。然而，与RSM（分别为0.852和16.534）相比，ANN具有更高的R值（0.996）和更低的绝对平均偏差（4.257），证明其具有更好的预测能力。这些结果表明，在优化肉的嫩度方面，ANN比RSM模型更准确、更好。ANN给出的超声功率、超声时间和储存时间的最佳条件分别为812瓦、24.5分钟和25.7小时。在优化条件下，与未处理的肉相比，超声处理显著降低了肉的蒸煮损失。在最佳条件下较低的蒸煮损失和MORS有利于满足肉类加工厂消费者和生产者的满意度。
