Predicting Alcohol Concentration during Beer Fermentation Using Ultrasonic Measurements and Machine Learning

Beer fermentation is typically monitored by periodic sampling and off-line analysis. In-line sensors would remove the need for time-consuming manual operation and provide real-time evaluation of the fermenting media. This work uses a low-cost ultrasonic sensor combined with machine learning to predi...

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Bibliographic Details
Published inFermentation (Basel) Vol. 7; no. 1; p. 34
Main Authors Bowler, Alexander, Escrig, Josep, Pound, Michael, Watson, Nicholas
Format Journal Article
LanguageEnglish
Published MDPI AG 01.03.2021
Subjects
industrial digital technologies
long short-term memory
machine learning
ultrasonic measurements
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Summary:Beer fermentation is typically monitored by periodic sampling and off-line analysis. In-line sensors would remove the need for time-consuming manual operation and provide real-time evaluation of the fermenting media. This work uses a low-cost ultrasonic sensor combined with machine learning to predict the alcohol concentration during beer fermentation. The highest accuracy model (R2 = 0.952, mean absolute error (MAE) = 0.265, mean squared error (MSE) = 0.136) used a transmission-based ultrasonic sensing technique along with the measured temperature. However, the second most accurate model (R2 = 0.948, MAE = 0.283, MSE = 0.146) used a reflection-based technique without the temperature. Both the reflection-based technique and the omission of the temperature data are novel to this research and demonstrate the potential for a non-invasive sensor to monitor beer fermentation.
ISSN:2311-5637
2311-5637
DOI:10.3390/fermentation7010034