@article { , title = {Predicting Alcohol Concentration during Beer Fermentation Using Ultrasonic Measurements and Machine Learning}, 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 ac-curacy 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.}, doi = {10.3390/fermentation7010034}, issn = {2311-5637}, issue = {1}, journal = {Fermentation}, publicationstatus = {Published}, publisher = {MDPI}, url = {https://nottingham-repository.worktribe.com/output/5335780}, volume = {7}, year = {2021}, author = {Bowler, Alexander and Escrig, Josep and Pound, Michael and Watson, Nicholas} }