Chemometric models for on-line process monitoring have become well established in pharmaceutical bioprocesses. The main drawback is the required calibration effort and the inflexibility regarding system or process changes. So, a recalibration is necessary whenever the process or the setup changes even slightly. With a large and diverse Raman dataset, however, it was possible to generate generic partial least squares regression models to reliably predict the concentrations of important metabolic compounds, such as glucose-, lactate-, and glutamine-indifferent CHO cell cultivations. The data for calibration were collected from various cell cultures from different sites in different companies using different Raman spectrophotometers. In testing, the developed “generic” models were capable of predicting the concentrations of said compounds from a dilution series in FMX-8 mod medium, as well as from an independent CHO cell culture. These spectra were taken with a completely different setup and with different Raman spectrometers, demonstrating the model flexibility. The prediction errors for the tests were mostly in an acceptable range (<10% relative error). This demonstrates that, under the right circumstances and by choosing the calibration data carefully, it is possible to create generic and reliable chemometric models that are transferrable from one process to another without recalibration.

Real‐time information about the concentrations of substrates and biomass is the key to accurate monitoring and control of bioprocess. However, on‐line measurement of these variables is a challenging task and new measurement systems are still required. An alternative are software sensors, which can be used for state and parameter estimation in bioprocesses. The software sensors predict the state of the process by using mathematical models as well as data from measured variables. The Kalman filter is a type of such sensors.In this paper, we have used the Unscented Kalman Filter (UKF) which is a nonlinear extension of the Kalman filter for on‐line estimation of biomass, glucose and ethanol concentration as well as for estimating the growth rate parameters in S. cerevisiae batch cultivation, based on infrequent ethanol measurements. The UKF algorithm was validated on three different cultivations with variability of the substrate concentrations and the estimated values were compared to the off‐line values.The results obtained showed that the UKF algorithm provides satisfactory results with respect to estimation of concentrations of substrates and biomass as well as the growth rate parameters during the batch cultivation.

Dough fermentation is an important step during the preparation of fermented baking goods. For the supervision of dough fermentation, a continuous-discrete extended Kalman filter was applied, which uses an image analysis system as the measurement. By estimation a fixed number of gas bubbles inside the dough, the radius of an average bubble was determined. A mathematical dough model was used by the extended Kalman filter to estimate the radius of the average bubble, the CO₂ concentration of the non-gas dough phase and the number of CO₂ molecules in the average bubble. During a fermentation of 50 min, the extended Kalman filter estimated that the average radius increased from 50 µm to 127 µm, the CO₂ concentration in the non-gas dough increased to 23 mol/m³, and the CO₂ amount in the bubble increased from 0.1 × 10⁻¹⁰ to 4 × 10⁻¹⁰ mol. Also, the specific CO₂ production rate was estimated to be in the range from 1.5 × 10⁻³ to more than 4 × 10⁻³ mol·m³/kg/s. The advantages of an extended Kalman filter for the supervision of the dough fermentation process are discussed.