Variable-specific Preprocessing Enables Cross-Scale Transferable Raman Models from High-Throughput Bioreactor Data

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 prediction performance of Raman chemometric models calibrated using HT cell culture data. Multiple preprocessing approaches were systematically evaluated for key cell culture target outputs, and the optimal preprocessing pipeline for each output was selected based on its ability to minimize cross-scale prediction error. The optimized variable-specific preprocessing pipelines reduced cross-scale prediction errors by 14.0–56.1