|
|
|
| Optimization of High-Pressure Ultrasound-Assisted Enzymatic Extraction of Collagen from Sheep Skin Using Genetic Algorithm-Back Propagation Neural Network |
| ZHU Ming, ZHANG Dequan, LI Shaobo, CHEN Li, HOU Chengli, CHENG Chengpeng, YU Jiangying, GUAN Wenqiang |
| 1. Tianjin Key Laboratory of Food Biotechnology, School of Biotechnology and Food Science, Tianjin University of Commerce, Tianjin 300134; 2. Key Laboratory of Agricultural Product Quality, Safety, Storage, Transportation and Control, Ministry of Agriculture and Rural Affairs, Institute of Agricultural Product Processing, Chinese Academy of Agricultural Sciences, Beijing 100193 |
|
|
|
|
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.
|
|
|
|
|
|
|
|
|
|
|
2024, Vol. 38 
