文献类型：期刊文献

英文题名：Artificial intelligence-driven fermentation optimization for α-amylase hyperproduction enabled by Raman monitoring and metabolic network analysis

作者：Wang, Yuan[1,2];Wang, Yonghong[1,2];Xu, Feng[1,2,3]

机构：[1]East China Univ Sci & Technol, Qingdao Innovat Inst, State Key Lab Bioreactor Engn, Shanghai 200237, Peoples R China;[2]East China Univ Sci & Technol, Natl Ctr Bioengn & Technol Shanghai, Shanghai 200237, Peoples R China;[3]East China Univ Sci & Technol, Shanghai Collaborat Innovat Ctr Biomfg Technol, Shanghai 200237, Peoples R China

年份：2026

卷号：439

外文期刊名：BIORESOURCE TECHNOLOGY

收录：;EI(收录号：20253719127999);WOS:【SCI-EXPANDED(收录号:WOS:001583586600009)】；

基金：This work is supported by National Key R & D Plan Key Research Projects (2021YFC2100205) .

语种：英文

外文关键词：Aspergillus niger; alpha-amylase production; Machine learning; Transcriptomic analysis; Raman spectroscopy

摘要：alpha-Amylase is a high-value enzyme widely applied in food, feed, textile, and bioenergy industries, yet achieving stable high-level production in Aspergillus niger remains difficult due to nonlinear fermentation dynamics and limited real-time control. To this end, an AI-driven fermentation optimization framework was established by combining multivariate machine learning, Raman spectroscopy-based glucose monitoring, and time-series transcriptomics. Twelve algorithms were benchmarked, with Random Forest showing the best predictive power, while SHAP interpretation highlighted glucose as the dominant regulatory factor. Prospective validation across different yield levels confirmed the model's robustness. Moreover, closed-loop Raman feedback maintained glucose within the optimal range, increasing alpha-amylase yield by 46 % and reducing fermentation time by 28 h compared with manual feeding. The optimized process achieved the highest alpha-amylase yield reported to date (15,729.47 U/mL). Overall, the integrative framework provides a scalable, mechanistic, and data-driven strategy for intelligent fungal fermentation and can be extended to other microbial bioprocesses.