This study aims to optimize the processing technology and quality of prepared pork. Ultrasonic-vacuum(USV)synergy technique was used to improve the texture characteristics and final product yield of the pork. Based on the single factor experiments and response surface methodology Box-Behnken design, surface analysis method and BP neural network-particle swarm optimization method were compared to observe their influence on final product yield. The results showed that the optimal process of ultrasonic-vacuum synergistic treatment by response surface method was: a marinade/meat ratio of 48.10 %, 246.04 W ultrasonic power, 61.11 kPa vacuum. Also, the theoretical prediction value of final product yield, the verification test value and the relative error value was 94.82 %, 92.84 % and 2.09 %, respectively. On the other hand, the optimal process by BP neural network-particle swarm optimization method was: a marinade/meat ratio of 51.00 %, 235.36 W ultrasonic power, 66.11 kPa vacuum. Furthermore, the theoretical prediction value of final product yield, the verification test value and the relative error value was 95.48 %, 94.96 % and 0.54 %, respectively. Overall, BP neural network-particle swarm optimization algorithm has higher yield and less error, indicating that the method has more extensive application prospects in the field of food processing, and also provides a new idea for food research.
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