New publication in Sensors [20.09.23]
Marvin Anker und Christian Krupitzer from the Department of Food Informatics are Co-Authors of the publication "Online Monitoring of Sourdough Fermentation Using a Gas Sensor Array with Multivariate Data Analysis" in Sensors (Impact Factor: 3.9 (2022)).
The publication "Online Monitoring of Sourdough Fermentation Using a Gas Sensor Array with Multivariate Data Analysis" by Marvin Anker (Department of Food Informatics and Computational Science Hub, University of Hohenheim, Stuttgart, Germany) with the co-authors, Abdolrahim Yousefi-Darani (Department of Process Analytics and Cereal Science, University of Hohenheim, Stuttgart, Germany), Viktoria Zettel (Department of Process Analytics and Cereal Science, University of Hohenheim, Stuttgart, Germany), Olivier Paquet-Durand (Department of Process Analytics and Cereal Science, University of Hohenheim, Stuttgart, Germany), Bernd Hitzmann (Department of Process Analytics and Cereal Science, University of Hohenheim, Stuttgart, Germany), Christian Krupitzer (Department of Food Informatics and Computational Science Hub, University of Hohenheim, Stuttgart, Germany) was published in Sensors, MDPI (Impact Factor: 3.9 (2022)).
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 (R2) 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.
The publication is available at: www.mdpi.com/1424-8220/23/18/7681
