Addressing the elephant in the room: A comprehensive framework to resolve selectivity, multicollinearity, and...

Raman spectroscopy offers significant potential for real-time bioprocess monitoring, but adoption in biologics manufacturing remains limited due to challenges in developing robust chemometric models. Key barriers include poor selectivity between structurally similar metabolites, multicollinearity among correlated analytes that confound model predictions, and insufficient training data ranges for critical quality attributes (CQAs). To overcome these barriers, a novel workflow called Spiking and Pure Analyte Characterization for Raman Chemometrics (SPARC) was developed. SPARC addresses these challenges through three innovations: using pure analytes to identify signature spectral regions for enhanced selectivity, spiking pure analytes into cell culture samples to mitigate multicollinearity, and using purified and enriched CQA material to spike into cell culture samples to broaden the CQA training data range. These protocols were implemented on the ambr250 high-throughput (HT) system, using an integrated liquid handler for automated spiked sample preparation. SPARC successfully modeled six analytes: glucose, lactate, glutamine, glutamate, monoclonal antibody (mAb), and CQA: High Molecular Weight (HMW) species. Pure analyte characterization identified 8-22 signature spectral regions per analyte using Variable Importance in Projection (VIP) scores. Cross-scale validation demonstrated successful model transfer from the ambr250 system (single-flow-cell probe) to 3 L bioreactors (dedicated in-situ immersion probe). SPARC consistently outperformed baseline methods with significant reductions in prediction errors: glucose (48%), lactate (49%), glutamine (69%), glutamate (57%), mAb (77%) and uniquely enabled modeling of HMW species where baseline methods failed. SPARC provides a systematic workflow for implementing Raman chemometrics in cell culture that overcomes technical barriers, automates data generation, and accelerates model development.