Influence of extracellular pH on growth, viability, cell size, acidification activity, and intracellular pH of Lactococcus lactis in batch fermentations.

In this study, we investigated the influence of three extracellular pH (pHex) values (i.e., 5.5, 6.5, and 7.5) on the growth, viability, cell size, acidification activity in milk, and intracellular pH (pHi) of Lactococcus lactis subsp. lactis DGCC1212 during pH-controlled batch fermentations. A universal parameter (e.g., linked to pHi) for the description or prediction of viability, specific acidification activity, or growth behavior at a given pHex was not identified. We found viability as determined by flow cytometry to remain high during all growth phases and irrespectively of the pH set point. Furthermore, regardless of the pHex, the acidification activity per cell decreased over time which seemed to be linked to cell shrinkage. Flow cytometric pHi determination demonstrated an increase of the averaged pHi level for higher pH set points, while the pH gradient (pHi-pHex) and the extent of pHi heterogeneity decreased. Cells maintained positive pH gradients at a low pHex of 5.5 and even during substrate limitation at the more widely used pHex 6.5. Moreover, the strain proved able to grow despite small negative or even absent pH gradients at a high pHex of 7.5. The larger pHi heterogeneity at pHex 5.5 and 6.5 was associated with more stressful conditions resulting, e.g., from higher concentrations of non-dissociated lactic acid, while the low pHi heterogeneity at pHex 7.5 most probably corresponded to lower concentrations of non-dissociated lactic acid which facilitated the cells to reach the highest maximum active cell counts of the three pH set points.

Sensor combination and chemometric variable selection for online monitoring of Streptomyces coelicolor fed-batch cultivations.

Fed-batch cultivations of Streptomyces coelicolor, producing the antibiotic actinorhodin, were monitored online by multiwavelength fluorescence spectroscopy and off-gas analysis. Partial least squares (PLS), locally weighted regression, and multilinear PLS (N-PLS) models were built for prediction of biomass and substrate (casamino acids) concentrations, respectively. The effect of combination of fluorescence and gas analyzer data as well as of different variable selection methods was investigated. Improved prediction models were obtained by combination of data from the two sensors and by variable selection using a genetic algorithm, interval PLS, and the principal variables method, respectively. A stepwise variable elimination method was applied to the three-way fluorescence data, resulting in simpler and more accurate N-PLS models. The prediction models were validated using leave-one-batch-out cross-validation, and the best models had root mean square error of cross-validation values of 1.02 g l(-1) biomass and 0.8 g l(-1) total amino acids, respectively. The fluorescence data were also explored by parallel factor analysis. The analysis revealed four spectral profiles present in the fluorescence data, three of which were identified as pyridoxine, NAD(P)H, and flavin nucleotides, respectively.

On-line estimation of biomass, glucose and ethanol in Saccharomyces cerevisiae cultivations using in-situ multi-wavelength fluorescence and software sensors.

Batch bioreactor cultivations using Saccharomyces cerevisiae at high (190-305 gl(-1) glucose) or low (21-25 gl(-1) glucose) gravity conditions were monitored on-line using multi-wavelength fluorescence (MWF) and standard monitoring sensors. Partial least squares models were calibrated for the prediction of cell dry weight (CDW), ethanol and consumed glucose, using the two data types separately. The low gravity cultivations (LGCs) consisted of two phases (glucose consumption with concomitant ethanol production followed by ethanol consumption after glucose depletion), which proved difficult to model using one and the same model for both phases. Segmented modelling, using different models for the two phases, improved the predictions significantly. The prediction models calibrated on standard on-line process data displayed similar or lower root mean square error of prediction (RMSEP) compared to the fluorescence models. The best prediction models for high gravity cultivations (HGCs) had RMSEPs of 1.0 gl(-1) CDW, 1.8 gl(-1) ethanol and 5.0 gl(-1) consumed glucose, corresponding to 4%, 2% and 2% of the respective concentration intervals. Corresponding numbers in low gravity models were 0.3 gl(-1) CDW, 0.7 gl(-1) ethanol and 1.0 gl(-1) consumed glucose, corresponding to 4%, 8% and 4% of the respective concentration intervals.