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Direct methods for measuring microbial populations in a culture are essential tools in microbiology, providing quantitative data for various applications. Among these, microscopic counts, plate counts, and serial dilution are widely used techniques, each with unique principles and applications.Microscopic CountsMicroscopic counting involves the use of a Petroff-Hausser chamber, a specialized microscope slide with a grid and defined depth. By observing a liquid culture under a microscope,...
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Updated: Jul 15, 2025

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

Marvin Anker1, Abdolrahim Yousefi-Darani2, Viktoria Zettel2

  • 1Department of Food Informatics and Computational Science Hub, University of Hohenheim, 70599 Stuttgart, Germany.

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|September 28, 2023
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Summary
This summary is machine-generated.

Online gas sensors can predict sourdough fermentation quality. This method efficiently monitors pH and total titratable acidity (TTA), improving traditional time-consuming offline measurements for better bakery products.

Keywords:
food monitoringgas sensormachine learningprocess analyticsprocess modelingsourdough

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Area of Science:

  • Food Science and Technology
  • Biotechnology
  • Analytical Chemistry

Background:

  • Sourdough fermentation enhances bakery product quality, including shelf life and nutritional value.
  • Accurate monitoring of sourdough fermentation is crucial for consistent quality.
  • Traditional offline measurements of pH and total titratable acidity (TTA) are time-consuming and costly.

Purpose of the Study:

  • To develop and validate an online monitoring system for sourdough fermentation using gas sensor arrays (GSA).
  • To correlate exhaust gas data from GSA with offline measurements of pH and TTA.
  • To establish robust prediction models for key fermentation parameters.

Main Methods:

  • Utilized a gas sensor array (GSA) system to monitor sourdough fermentation in real-time.
  • Extracted features from GSA exhaust gas data.
  • Developed prediction models using Principal Component Analysis (PCA) and combined fermentation data.

Main Results:

  • Achieved robust prediction models for pH and TTA with high coefficients of determination (R2: 0.94-0.998 for pH, 0.947-0.994 for TTA).
  • Reported percentage root mean square errors (RMSE) between 1.4%-12% for pH and 2.7%-9.3% for TTA.
  • Demonstrated the effectiveness of PCA and combined fermentation data for model accuracy.

Conclusions:

  • Online monitoring of sourdough fermentation exhaust gas using GSA is a cost-effective and efficient method.
  • This approach enables accurate prediction of critical process variables like pH and TTA.
  • The developed GSA system offers a valuable tool for quality control in sourdough production.