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

Abstract

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 acquisition 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 calibration 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.