Bowler, A. orcid.org/0000-0003-3209-2774, Escrig, J., Pound, M. et al. (1 more author) (2021) Predicting Alcohol Concentration during Beer Fermentation Using Ultrasonic Measurements and Machine Learning. Fermentation, 7 (1). 34. ISSN 2311-5637
Abstract
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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
Keywords: | machine learning; ultrasonic measurements; long short-term memory; industrial digital technologies |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Environment (Leeds) > School of Food Science and Nutrition (Leeds) > FSN Nutrition and Public Health (Leeds) The University of Leeds > Faculty of Environment (Leeds) > School of Food Science and Nutrition (Leeds) > FSN Colloids and Food Processing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 12 Jul 2024 09:24 |
Last Modified: | 12 Jul 2024 09:24 |
Status: | Published |
Publisher: | MDPI |
Identification Number: | 10.3390/fermentation7010034 |
Sustainable Development Goals: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:214622 |