Online Monitoring of Sourdough Fermentation Using a Gas Sensor Array with Multivariate Data Analysis

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 acidi...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2023-09, Vol.23 (18), p.7681
Main Authors: Anker, Marvin, Yousefi-Darani, Abdolrahim, Zettel, Viktoria, Paquet-Durand, Olivier, Hitzmann, Bernd, Krupitzer, Christian
Format: Article
Language:English
Subjects:
Acidification
Acids
Analysis
Automation
Data analysis
Fermentation
Flour
food monitoring
gas sensor
Gases
Gluten
Instrument industry
machine learning
Microorganisms
Neural networks
Pharmaceutical industry
process analytics
process modeling
Scientific equipment and supplies industry
Sensors
Sensory perception
sourdough
Spectrum analysis
Support vector machines
Tin
Variables
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Summary: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.
ISSN:1424-8220
1424-8220
DOI:10.3390/s23187681