Raman spectroscopy is a suitable monitoring technique for CHO cultivations. However, a thorough discussion of peaks, bands, and region assignments to key metabolites and culture attributes, and the interpretability of produced calibrations is scarce. That understanding is vital for the long-term predictive ability of monitoring models, and to facilitate lifecycle management that comply with regulatory guidelines. Several fed-batch lab-scale mAb mammalian cultivations were carried out, with in situ Raman spectroscopy used for process state estimation and attribute monitoring. The goal was to evaluate its use as a process analytical technology (PAT) tool to detect residual glucose and lactate levels, understand their dynamics and interconversion, and eventually estimate key performance culture and product quality attributes. Glucose and lactate models were optimized up to 0.31 g L with 3 Latent Variables (LVs) and 0.19 g L (2 LVs) accuracy, respectively. Glutamine and product titer models, were not specific and accurate enough, even though indirect calibrations were obtained with a RMSEP of 0.12 g L (4 LVs) and 0.29 g L (5 LVs), respectively. A critical discussion and details about the extensive work done in calibration development and optimization are provided. Namely, considering a risk-based selection of variability sources impacting sample spectra, executing designed experiments with spiked cultivations, and using advanced chemometric procedures for variable selection and model cross validation. A strategy is presented to evaluation Raman spectroscopy as a reliable PAT technology fit-for industrial use. © 2018 American Institute of Chemical Engineers Biotechnol. Prog., 34:659-670, 2018.

Alcoholic fermentation under Saccharomyces cerevisiae yeasts is governed largely by glucose uptake, biomass formation, ethanol and glycerin production, and acidification. In this work, PLS calibration models were developed with a view to determining these analytical parameters from near infrared spectra and analytical data provided by the corresponding reference methods. The models were applied to a set of samples obtained from various fermentation processes. The glucose, ethanol, and biomass values predicted by the models exhibited a high correlation with those provided by the reference method.

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