Balancing Prediction Performance and Spectral Interpretability: Multimetric Guided Design of Raman Spectrum Preprocessing Pipelines to Transfer Single Compound Models to Bioprocess Data

Raman spectroscopy is a widely explored in‐line process analytical technology in bioprocessing due to its high chemical specificity and multiplexed readouts through chemometric modelling. However, developing robust chemometric models requires investment in bioreactor runs for data collection, making single‐compound spectra a cost‐effective calibration alternative, in which signal preprocessing is crucial for transferring models to bioprocess data. This work presents a multimetric framework for selecting preprocessing pipelines to support model transfer, balancing prediction performance, chemical interpretability, and flexibility to generalize across process conditions. It considers four aspects of signal quality: signal preservation, noise reduction, spectral resolution, and model performance. Approximately 65,000 preprocessing combinations were systematically screened to identify pipelines exhibiting balance across all four aspects. Selected pipelines were used to calibrate partial least squares (PLS) regressions and predict ethanol and glucose concentrations in four batch and one fed‐batch aerobic Saccharomyces cerevisiae fermentation datasets. Compared to preprocessing pipelines selected based solely on prediction error, the multimetric framework yielded more robust and chemically interpretable models. Ethanol models matched literature performance using a single latent variable PLS. Moreover, this was achieved without a process‐specific internal standard or biomass spectra, supporting flexibility and applicability to other processes. However, inherent weaker spectral features of glucose reduced performance, highlighting the limitations of a single‐compound model developed solely on preprocessing optimization. Overall, this work shows that multiobjective preprocessing selection, combined with single‐compound calibration, reduces costs, improves interpretability and flexibility in chemometric model development, while highlighting the value of analyte‐specific strategies.