Online monitoring of sourdough fermentation using a gas sensor array with multivariate data analysis

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dc.contributor.authorAnker, Marvin
dc.contributor.authorYousefi-Darani, Abdolrahim
dc.contributor.authorZettel, Viktoria
dc.contributor.authorPaquet-Durand, Olivier
dc.contributor.authorHitzmann, Bernd
dc.contributor.authorKrupitzer, Christian
dc.date.accessioned2024-09-03T07:30:27Z
dc.date.available2024-09-03T07:30:27Z
dc.date.issued2023de
dc.description.abstractSourdough 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.en
dc.identifier.swb1860123007
dc.identifier.urihttps://hohpublica.uni-hohenheim.de/handle/123456789/16198
dc.identifier.urihttps://doi.org/10.3390/s23187681
dc.language.isoengde
dc.rights.licensecc_byde
dc.source1424-8220de
dc.sourceSensors; Vol. 23, No. 18 (2023) 7681de
dc.subjectGas sensor
dc.subjectMachine learning
dc.subjectProcess analytics
dc.subjectProcess modeling
dc.subjectFood monitoring
dc.subjectSourdough
dc.subject.ddc660
dc.titleOnline monitoring of sourdough fermentation using a gas sensor array with multivariate data analysisen
dc.type.diniArticle
dcterms.bibliographicCitationSensors, 23 (2023), 18, 7681. https://doi.org/10.3390/s23187681. ISSN: 1424-8220
dcterms.bibliographicCitation.issn1424-8220
dcterms.bibliographicCitation.issue18
dcterms.bibliographicCitation.journaltitleSensors
dcterms.bibliographicCitation.volume23
local.export.bibtex@article{Anker2023, url = {https://hohpublica.uni-hohenheim.de/handle/123456789/16198}, doi = {10.3390/s23187681}, author = {Anker, Marvin and Yousefi-Darani, Abdolrahim and Zettel, Viktoria et al.}, title = {Online Monitoring of Sourdough Fermentation Using a Gas Sensor Array with Multivariate Data Analysis}, journal = {Sensors}, year = {2023}, volume = {23}, number = {18}, }
local.export.bibtexAuthorAnker, Marvin and Yousefi-Darani, Abdolrahim and Zettel, Viktoria et al.
local.export.bibtexKeyAnker2023
local.export.bibtexType@article

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