@article{ZHU2024, 
author = {Ming ZHU and Dequan ZHANG and Shaobo LI and Li CHEN and Chengli HOU and Chengpeng CHENG and Jiangying YU and Wenqiang GUAN},
title = {Optimization of High-Pressure Ultrasound-Assisted Enzymatic Extraction of Collagen from Sheep Skin Using Genetic Algorithm-Back Propagation Neural Network},
year = {2024},
journal = {Meat Research},
volume = {38},
number = {6},
pages = {42-50},
keywords = {sheep skin, sheep skin collagen, high-pressure ultrasound-assisted enzymatic extraction, genetic algorithm-back propagation neural network, response surface methodology},
url = {https://www.sciopen.com/article/10.7506/rlyj1001-8123-20240510-111},
doi = {10.7506/rlyj1001-8123-20240510-111},
abstract = {This study compared the effectiveness of genetic algorithm-back propagation neural network (GA-BPNN) and response surface methodology (RSM) in optimizing the high-pressure ultrasound-assisted enzymatic extraction of collagen from sheep skin to determine the optimal process parameters. The results showed that GA-BPNN had superior performance in model fitting and prediction compared to RSM. The optimal extraction parameters were as follows: high pressure holding time of 23 min, ultrasound time of 22 min, enzyme dosage of 3.2%, and hydrolysis time of 222 min. Under these conditions, the extraction rate of collagen from sheep skin was (80.5 ± 1.6)%, which is 40% higher than that of the traditional papain method. The results of ultraviolet-visible (UV-Vis) spectroscopy and Fourier transform infrared (FTIR) spectroscopy demonstrated that the structure of the extract collagen was complete.}
}