Optimization of α-Glucosidase Inhibitors Recovery from Rainbow Trout Hydrolysate Using GA-BP Neural Network
Yingke Chu1, Yanling Dong2, Qingfeng Rong2, Kun Yang2 & Lanlan Zhu1 Show affiliations
- College of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo, 255100, Shandong Province, China
- Test Center, Zichuan District Inspection and Test Center, Zibo, 255100, Shandong Province, China
American Journal of Biochemistry and Biotechnology, Volume 21, Issue 3, pp. 401–411 · doi.org/10.3844/ajbbsp.2025.401.411
Abstract
To enhance the value of by-products from rainbow trout processing, the inhibition rate of α-glucosidase (AGIR) is used as indicators. Optimization of rainbow trout hydrolysate extraction by comparing Response Surface Methodology (RSM) and BP Neural Network (BPNN) models. The RSM results: the AGIR is 55.02%. BPNN models predicted the optimal extraction conditions: 51℃ for temperature,1:2.3 for solid-liquid ratio, 4.15 h for time and 0.2334% for enzyme dosage. The reactions obtained under optimized conditions are as follows: the α-glucosidase inhibitory rate increased to 58.14%. This proves that the BPNN model can simultaneously improve the hydrolysis degree and α-glucosidase inhibition rate of the rainbow trout hydrolysate.
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Chu, Y., Dong, Y., Rong, Q., Yang, K. & Zhu, L. (2025). Optimization of α-Glucosidase Inhibitors Recovery from Rainbow Trout Hydrolysate Using GA-BP Neural Network. American Journal of Biochemistry and Biotechnology, 21(3), 401–411. https://doi.org/10.3844/ajbbsp.2025.401.411
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