Variable-specific preprocessing enables cross-scale transferable Raman models from high-throughput bioreactor data

Abstract

Abstract Raman spectroscopy is increasingly used as a process analytical technology (PAT) for real-time monitoring of bioprocesses. However, chemometric models developed using high-throughput (HT) mini-bioreactor systems often show limited predictive performance when applied to larger-scale processes, reflecting an out-of-distribution (OOD) challenge in cross-scale model transfer. In this study, we investigated whether variable-specific data preprocessing strategies can improve the cross-scale predictive performance of Raman chemometric models developed solely from HT cell culture data. Multiple preprocessing approaches were systematically evaluated for key cell culture variables, and the optimal preprocessing pipeline for each variable was selected based on its ability to minimize cross-scale prediction error. The results demonstrate that appropriate variable-specific preprocessing can substantially improve the cross-scale predictive performance of models developed from a single HT scale. Importantly, the improvement was not dependent on a specific preprocessing technique but rather on selecting preprocessing strategies suited to the spectral characteristics of each variable. These findings suggest that intelligent preprocessing selection can mitigate out-of-distribution effects in Raman chemometric models and enable more reliable prediction across scales using only HT datasets. This approach highlights the potential of Raman spectroscopy as a practical and scalable PAT tool for industrial bioprocess development.