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Online Monitoring of Sourdough Fermentation Using a Gas Sensor Array with Multivariate Data Analysis.
Anker, Marvin; Yousefi-Darani, Abdolrahim; Zettel, Viktoria; Paquet-Durand, Olivier; Hitzmann, Bernd; Krupitzer, Christian.
Afiliación
  • Anker M; Department of Food Informatics and Computational Science Hub, University of Hohenheim, 70599 Stuttgart, Germany.
  • Yousefi-Darani A; Department of Process Analytics and Cereal Science, University of Hohenheim, 70599 Stuttgart, Germany.
  • Zettel V; Department of Process Analytics and Cereal Science, University of Hohenheim, 70599 Stuttgart, Germany.
  • Paquet-Durand O; Department of Process Analytics and Cereal Science, University of Hohenheim, 70599 Stuttgart, Germany.
  • Hitzmann B; Department of Process Analytics and Cereal Science, University of Hohenheim, 70599 Stuttgart, Germany.
  • Krupitzer C; Department of Food Informatics and Computational Science Hub, University of Hohenheim, 70599 Stuttgart, Germany.
Sensors (Basel) ; 23(18)2023 Sep 06.
Article en En | MEDLINE | ID: mdl-37765737
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.
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Texto completo: 1 Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Sensors (Basel) Año: 2023 Tipo del documento: Article País de afiliación: Alemania

Texto completo: 1 Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Sensors (Basel) Año: 2023 Tipo del documento: Article País de afiliación: Alemania