From raw to refined: Quality-metric-guided preprocessing optimization for Raman spectroscopy in biopharmaceutical downstream processing

The application of Raman spectroscopy is increasingly applied in biopharmaceutical manufacturing for real-time process monitoring and control. In downstream bioprocessing, it offers high potential for in-line quantification of CQAs directly from spectral data using chemometric and machine learning models. Successful implementation, however, strongly depends on robust spectral preprocessing to remove noise, baseline drifts, and interfering signals. Baseline correction remains particularly challenging due to signal variability across various process conditions. Current preprocessing optimization practices typically emphasize minimizing model prediction errors but often disregard the chemical validity of the resulting spectra. To address this gap, we introduce a regression-oriented metric that assesses preprocessing quality based on chemically meaningful criteria by quantifying baseline correction artifacts and spectral intensity-concentration linearity. Paired with an empirically identified preprocessing sequence, this metric is integrated into an automated workflow for systematic baseline correction optimization. Application across datasets comprising mAb and downstream buffer samples demonstrates that the metric reliably ranks preprocessing performance and consistently selects configurations that suppress artifacts and preserve analyte‑specific spectral features. ... mehrWithin the evaluated datasets and baseline correction algorithms, benchmarking against model-centric optimization highlights the trade-off between prediction accuracy and spectral interpretability when relying solely on model RMSE. In contrast, the quality metric-driven approach not only aligns with modeling objectives but also safeguards chemically relevant spectral information, yielding sufficiently high predictive power and improved robustness under changing sample conditions. The quality-metric-guided workflow provides a practical methodology for developing preprocessing pipelines for quantitative analysis that supports robust Raman‑based PAT in downstream bioprocessing.