Comparison of multivariate data analysis techniques to improve glucose concentration prediction in mammalian cell cultivations by Raman spectroscopy.

In-situ Raman spectroscopy is frequently applied to monitor and even control the glucose concentration of monoclonal antibody producing mammalian cell cultivations. Previous studies used the PLSR algorithm only, however other multivariate algorithms were applied successfully for different protein production processes. In this study, four mammalian cell cultivation runs were followed with Raman spectroscopy and the spectra were analysed quantitatively and qualitatively as well. The PCA analysis showed that one of the most dominant factors in the Raman spectra were the concentration of glucose, which strongly correlated with the score values of the eighth principal component. This observation further substantiated that Raman spectroscopy is an excellent tool for bioprocess monitoring and induced the test of the Multivariate Linear Regression (MLR), Principal Component Regression (PCR) and Partial Least Squares Regression (PLSR) algorithms, using the results of the PCA as one of the variable selection techniques, to determine the glucose concentration during cultivation. However, the novel variable selection technique of PCA correlation enhanced only the model accuracy when it was applied with MLR and only model robustness was increased when it was used with PCR and PLSR because the relatively strong Raman signal of glucose concentration seemed to be enough to build an accurate model on. Therefore, PLSR, the most advanced algorithm of the three, delivered the lowest 2.21 mM RMSEP but it was demonstrated that in certain cases PCR could also produce satisfactorily results.