Adaptive control of a G. xylinus fed-batch fermentation using in situ mid-IR spectroscopy

Bacterial exopolysaccharides (EPS) have unique rheol. properties due to their high purity and regular structure. Thus, the food industry frequently uses EPS as thickening, gelling or stabilizing agents. The strain Gluconacetobacter xylinus I 2281 is capable of producing with a very high yield a new sol. EPS named gluconacetan, which is composed of rhamnose, glucose, mannose and glucuronic acid. Growth of these bacteria presents many features that represent a real challenge for both monitoring and control. High EPS concns. in the culture broth cannot be filtered and thus, classical online monitoring tools, such as \"Flow Injection Anal.\" or chromatog. methods, are no longer suitable to follow the fermn. Another problem is related to the ethanol concn. in the reactor. This compd. is the main substrate for biomass prodn. Cells convert it to acetate and metabolize this last to form biomass. When ethanol concn. is above a certain threshold, the enzymic system that digests acetate is inhibited. As a result, biomass growth is stopped and acetate accumulates in the reactor. It is thus necessary to maintain the ethanol concn. in the reactor at a very low concn. In this work fed-batch fermn. of G. xylinus was performed. A concd. ethanol soln. was used as the feed soln. A very simple and novel adaptive control strategy was used to maintain the acetate concn. in the reactor const. Consequently ethanol concn. was forced to remain very low. In situ MIR spectroscopy, a non-invasive optical sensor system that is insensitive to the viscosity changes in the broth was used to monitor simultaneously ethanol, acetate, ammonium, phosphates and fructose concns. Both the control strategy and the exptl. results will be presented. [on SciFinder (R)

Experimental versus simulated calibration mdels for in situ FTIR monitoring of bioprocesses

Over the past decade the interest for bioprocess monitoring using non-invasive IR spectroscopic sensors has increased considerably. This is mainly due to their rapid simultaneous multi-analyte detn. ability, in-situ sterilizability, and low need for maintenance during operation. Most of the components in culture media absorb in the MID-IR region. The complex spectra obtained can be deconvoluted using numerical methods such as Partial Least-Squares. In order to do this, a calibration model for each component needs to be generated. This model is based on an exptl. obtained calibration matrix obtained by taking the spectra of mixts. contg. different concns. of the pure components involved in the reaction. According to ASTM guidelines [1], the no. of different mixts. used in such calibration models must be at least 6 times the no. of absorbing components. This is usually very time-consuming for bioprocesses, since the no. of spectra that need to be collected is very large. One of the main advantages of using Mid-IR spectra compared to NIR is in the linear features the first present. According to Beer's law, it is possible to scale the spectrum of a pure component to obtain its spectra for different concns. Furthermore, the spectrum of a mixt. of components can be theor. achieved by adding up the spectrum of the different pure components involved. It is thus interesting to study if a calibration matrix such as the one proposed by the ASTM guidelines can be generated artificially. In this work the predictions obtained using two different calibration matrixes will be studied. The first calibration matrix contains spectra of different mixts. collected manually, while in the second matrix the spectra were generated artificially from the different pure components present in the reaction system. The two models were used to monitor a fed-batch fermn. of G. xylinus. The results obtained as well as a discussion on the role of noise will be presented. [1] Std. practices for IR quant. anal. (e 1655-97). Annual book of ASTM stds. 1999. [on SciFinder (R)

The influence of correlated calibration samples on the prediction performance of multivariate models based on mid-infrared spectra of animal cell cultures

The effect of the presence of metabolism-induced concentration correlations in the calibration samples on the prediction performance of partial least-squares regression (PLSR) models and mid-infrared spectra from Chinese hamster ovary cell cultures was investigated. Samples collected from batch cultures contained highly correlated metabolite concentrations as a result of metabolic relations. Calibrations based on such samples could only be used to predict concentrations in new samples if a similar correlation structure was present and failed when the new samples were randomly spiked with the analytes. On the other hand, such models were able to predict glucose correctly even if they were based on a spectral range in which glucose does not absorb, provided that the correlations in the calibration and in the new samples were similar. If however, samples from a calibration culture were randomly spiked with the main analytes, much more robust PLSR models resulted. It was possible to predict analyte concentrations in new samples irrespective of whether the correlation structure was maintained or not. Validity of all established models for any given use could be predicted a priori by computing the space inclusion and observer conditions. Predictions from these computations agreed in all cases with the experimental test of model validity. [on SciFinder (R)