Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

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

Version 1 : Received: 16 February 2021 / Approved: 18 February 2021 / Online: 18 February 2021 (09:31:43 CET)

A peer-reviewed article of this Preprint also exists.

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. Fermentation 2021, 7, 34.

Journal reference: Fermentation 2021, 7, 34
DOI: 10.3390/fermentation7010034

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.

Subject Areas

Machine learning; Ultrasonic measurements; Long Short-Term Memory; Industrial Digital technologies

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