Mid-Infrared Spectroscopy and Multivariate Analysis to Characterize Fermentation Processes.

The ever-growing competition among global biotech industries has led to high demands on production consistency. A statistical strategy of performance mapping for production optimization is therefore of great economic significance. Process analytical technology (PAT)-based sensors such as mid-infrared (MIR) spectroscopy enable process monitoring through substrate and by-product concentrations that directly represent the physiology of cells. Combined with multivariate statistics, MIR can be employed as a strategy for production performance mapping. This study describes the use of at-line spectroscopy, chemometric modeling, and post-process fitting to characterize fermentations. The emphasis is on alternative arrangements of the data and chemometric methods principle component analysis (PCA), multivariate curve resolution (MCR), and parallel factor analysis (PARAFAC). Two key parameters, rate constant and time of inflection, are extracted by post-process fitting on the outcomes of these different models. Their use as process performance descriptors to characterize the dynamics of substrate consumption, product formation and batch-to-batch variations is suggested. The unconstrained PCA primarily described biomass change, while the constrained models PARAFAC and MCR (both the augmented and individual-run configurations) could model the decrease in sugars and increase in lactic acid over time. It was concluded that MCR on individual batch data, followed by post-process fitting, is the preferred strategy for MIR spectroscopic monitoring.