文献类型：期刊文献

英文题名：Application of semi-supervised convolutional neural network regression model based on data augmentation and process spectral labeling in Raman predictive modeling of cell culture processes

作者：Min, Rui[1];Wang, Zhi[1];Zhuang, Yingping[1];Yi, Xiaoping[1]

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

年份：2023

卷号：191

外文期刊名：BIOCHEMICAL ENGINEERING JOURNAL

收录：;EI(收录号：20225313324021);WOS:【SCI-EXPANDED(收录号:WOS:000913611500001)】；

基金：Acknowledgments This work was supported by the National Key R&D Program ofChina, 2021YFC2101100. This work was also supported by the Scientific Research Think Tank of Biological Manufacturing Industry in Qingdao, QDSWZK202004.

语种：英文

外文关键词：Semi -supervised learning; Convolutional neural network regression; Data augmentation; Process spectra labeling

摘要：Raman spectroscopy coupled to chemometric model-based technology have a great potential for real-time monitoring of critical process parameters in animal cell culture. The measurement of off-line reference values required for calibration of monitoring procedures is complex, time-and resource-intensive, whereas the acqui-sition of spectra is a seconds-or minutes-based process. Usually, less than 5% spectra is labeled with off-line data during calibration steps, thus a large amount of spectra is wasted. In this study, we proposed a Semi-Supervised Convolutional Neural Network Regression (SSCNNR) model framework for Raman model establishment based on data augmentation and process spectra labeling. Compared with the original CNNR model, the size of the cali-bration dataset was expanded from 132 samples to nearly 8500, and the constructed SSCNNR models obtained a significantly improved accuracy in predicting glucose, glutamine, asparagine, and ammonium, with RMSEP values decreased by 29.1%, 37.3%, 38.3% and 7%, respectively. Compared with traditional modeling approach of PLSR and SVR, the SSCNNR models were proved to accurately predict the trend of substance concentration during frequent feeding process. Based on this framework, potential efforts included improving model prediction accuracy by hyperparameters optimization and accommodating scenarios of culture change by transfer learning.