文献类型：期刊文献

英文题名：A Paradigm of Computer Vision and Deep Learning Empowers the Strain Screening and Bioprocess Detection

作者：Xu, Feng[1];Su, Lihuan[1];Wang, Yuan[1];Hu, Kaihao[1];Liu, Ling[1];Ben, Rong[1];Gao, Hao[1];Mohsin, Ali[1];Chu, Ju[1];Tian, Xiwei[1]

机构：[1]East China Univ Sci & Technol, Qingdao Innovat Inst, State Key Lab Bioreactor Engn, Shanghai, Peoples R China

年份：2025

卷号：122

期号：4

起止页码：817

外文期刊名：BIOTECHNOLOGY AND BIOENGINEERING

收录：;EI(收录号：20250317700942);WOS:【SCI-EXPANDED(收录号:WOS:001397389800001)】；

基金：This work was financially supported by the National Key Research Development Program of China (2022YFC2105403), the Taishan Scholars Program of Shandong Province (No. tsqn202312316), the Shanghai Pilot Program for Basic Research (22TQ1400100-14), the Natural Science Foundation of Shanghai (23ZR1416500), the Frontiers Science Center for Materiobiology and Dynamic Chemistry (JKVJ1231036). Thanks for the financial support from the Arawana Charity Foundation.

语种：英文

外文关键词：bioprocess detection; computer vision; deep learning; fluorescence intensity; gentamicin C1a; strain selection

摘要：High-performance strain and corresponding fermentation process are essential for achieving efficient biomanufacturing. However, conventional offline detection methods for products are cumbersome and less stable, hindering the "Test" module in the operation of "Design-Build-Test-Learn" cycle for strain screening and fermentation process optimization. This study proposed and validated an innovative research paradigm combining computer vision with deep learning to facilitate efficient strain selection and effective fermentation process optimization. A practical framework was developed for gentamicin C1a titer as a proof-of-concept, using computer vision to extract different color space components across various cultivation systems. Subsequently, by integrating data preprocessing with algorithm design, a prediction model was developed using 1D-CNN model with Z-score preprocessing, achieving a correlation coefficient (R2) of 0.9862 for gentamicin C1a. Furthermore, this model was successfully applied for high-yield strain screening and real-time monitoring of the fermentation process and extended to rapid detection of fluorescent protein expression in promoter library construction. The visual sensing research paradigm proposed in this study provides a theoretical framework and data support for the standardization and digital monitoring of color-changing bioprocesses.