Development and testing of a cell-free predictive model against Clostridium acetobutylicum batch fermentation

Previous work has shown Raman spectroscopy, together with statistical modeling, is effective for real-time data acquisition of consumable sugar (glucose) and accumulating products (butyric acid, acetic acid, and butanol) in Clostridium acetobutylicum cultures. Developed partial-least squares (PLS) models were applied to both agitated and static cultures with the former showing preferred modeling parameter values (R2Y = 0.99 and Q2Y = 0.98). Model outputs were comparable to off-line analyzed data from traditional HPLC for new clostridial experimental data through cross-validation. In this study, a cell-free system is explored in which experimental data from HPLC analyzed data for reaction components is used to simulate an 'artificial' fermentation culture devoid of cell activity or enzymes. Immersion probe data is assumed to not account for cell presence or associated activity in the cultures. Raman spectra of specific reaction components: (i) glucose, (ii) butyric acid, (iii) acetic acid, and (iv) butanol, in specified proportions were acquired for corresponding time points. The acquired spectra, together with known concentrations of reaction components, were used to build new sets of PLS models. Original cell-containing models and new cell-free models were run concurrently on new C. acetobutylicum fermentations. Comparison of model output results, suggest better predictability (e.g. Q2Y of 0.98 > Q2Y of 0.79) and less error (RMSECV of 0.98 < RMSECV of 2.76) in butyric acid concentrations for cell-containing models.