Methodology for real-time, multianalyte monitoring of fermentations using an in-situ mid-infrared sensor

An in-situ, mid-IR sensor was used to monitor the major analyte concns. involved in the cultivation of Gluconacetobacter xylinus and the prodn. of gluconacetan, a food-grade exopolysaccharide. To predict the analyte concns., three different sets of std. spectra were used to develop calibration models, applying partial least-squares regression. It was possible to build a valid calibration model to predict the 700 spectra collected during the complete time course of the cultivation, using only 12 spectra collected every 10 h as stds. This model was used to reprocess the concn. profiles from 0 to 15 g/L of nine different analytes with a mean std. error of validation of 0.23 g/L. However, this calibration model was not suitable for real-time monitoring as it was probably based on non-specific spectral features, which were correlated only with the measured analyte concns. Valid calibration models capable of real-time monitoring could be established by supplementing the set of 12 fermn. spectra with 42 stds. of measured analytes. A pulse of 5 g/L ethanol showed the robustness of the model to sudden disturbances. The prediction of the models drifted, however, toward the end of the fermn. The most robust calibration model was finally obtained by the addn. of 34 std. spectra of non-measured analytes. Although the spectra did not contain analyte-specific information, it was believed that this addn. would increase the variability space of the calibration model. Therefore, an expanded calibration model contg. 88 spectra was used to monitor, in real time, the concn. profiles of fructose, acetic acid, ethanol and gluconacetan and allowed std. errors of prediction of 1.11, 0.37, 0.22, and 0.79 g/L, resp. [on SciFinder (R)]