Bowler, A.; Escrig, J.; Pound, M.; Watson, N. Predicting Alcohol Concentration during Beer Fermentation Using Ultrasonic Measurements and Machine Learning. Fermentation2021, 7, 34.
Bowler, A.; Escrig, J.; Pound, M.; Watson, N. Predicting Alcohol Concentration during Beer Fermentation Using Ultrasonic Measurements and Machine Learning. Fermentation 2021, 7, 34.
Bowler, A.; Escrig, J.; Pound, M.; Watson, N. Predicting Alcohol Concentration during Beer Fermentation Using Ultrasonic Measurements and Machine Learning. Fermentation2021, 7, 34.
Bowler, A.; Escrig, J.; Pound, M.; Watson, N. Predicting Alcohol Concentration during Beer Fermentation Using Ultrasonic Measurements and Machine Learning. Fermentation 2021, 7, 34.
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, MAE=0.265, 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.
Keywords
Machine learning; Ultrasonic measurements; Long Short-Term Memory; Industrial Digital technologies
Subject
Engineering, Automotive Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
