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Sourdough can improve bakery products' shelf life, sensory properties, and nutrient composition. To ensure high-quality sourdough, the fermentation has to be monitored. The characteristic process variables for sourdough fermentation are pH and the degree of acidity measured as total titratable acidity (TTA). The time- and cost-intensive offline measurement of process variables can be improved by utilizing online gas measurements in prediction models. Therefore, a gas sensor array (GSA) system was used to monitor the fermentation process of sourdough online by correlation of exhaust gas data with offline measurement values of the process variables. Three methods were tested to utilize the extracted features from GSA to create the models. The most robust prediction models were achieved using a PCA (Principal Component Analysis) on all features and combined two fermentations. The calibrations with the extracted features had a percentage root mean square error (RMSE) from 1.4% to 12% for the pH and from 2.7% to 9.3% for the TTA. The coefficient of determination ( R 2 ) for these calibrations was 0.94 to 0.998 for the pH and 0.947 to 0.994 for the TTA. The obtained results indicate that the online measurement of exhaust gas from sourdough fermentations with gas sensor arrays can be a cheap and efficient application to predict pH and TTA. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The figure illustrates a software sensor for on-line monitoring of substrate and biomass production in backers yeast cultivation. DOI: 10.1002/elsc.202000058 Successful operation, control and optimization of biotechnological process depend on reliable real-time available measurements of the process variables. [Extracted from the article]

The ethanol concentration in batch cultivation with the yeast Saccharomyces cerevisiae was predicted on-line using a gas sensor array. Headspace samples were pumped past the gas sensors array for 10 s every five minutes and the voltage changes of the sensors were measured. For the calibration procedure no off-line sampling was used. Instead, a theoretical model of the process has been applied to simulate the ethanol production at any given time. However, the kinetic parameters of the simulation model are unknown at the beginning of the calibration. It is demonstrated that these kinetic parameters of the theoretical process model can be acquired from the response of the gas sensor array alone. The calculated parameters result in a simulation model that is at least as accurate as a model whose parameters have been acquired by least squares fitting to off-line measurements. The root mean square error of calibration as well as the percentage error for validation sets were below 0.2 g L−1 and 7%, respectively. The obtained results indicate that the model-based calibrated gas sensor array can be a cheap alternative to other tools, such as spectroscopy-based methods, that are used for monitoring yeast cultivations. • A design of a gas sensor array and a headspace sampling system is presented. • Ethanol concentration in batch cultivation with Saccharomyces cerevisiae is predicted. • No off-line data is used for calibrating the gas sensor array. • The calibration is performed using a theoretical model of the process. • The gas sensor array exhibits high predictive ability for ethanol concentration. [ABSTRACT FROM AUTHOR]