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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.
Highlights
Food, Water, Waste Research Group, Faculty of Engineering, University Park, University of Nottingham, i2CAT Foundation, Calle Gran Capita, 2-4 Edifici Nexus (Campus Nord Upc), 08034 Barcelona, Spain; School of Computer Science, Jubilee Campus, University of Nottingham, Nottingham NG8 1BB, UK; Abstract: Beer fermentation is typically monitored by periodic sampling and off-line analysis
Real-time data is key to the Fourth Industrial Revolution, which will implement industrial digital technologies such as the Internet of Things, cloud computing, and machine learning (ML) to integrate entire processes, automatically make decisions, and improve manufacturing productivity, efficiency, and sustainability [6]
The most accurate model (Model 1) uses features from both the 1st and 2nd reflections and the process temperature. This shows the potential of US sensors to predict the endpoint of fermentation and, as demonstrated in Figure 5a,b, accurately predict the alcohol concentration throughout the fermentation process
Summary
Water, Waste Research Group, Faculty of Engineering, University Park, University of Nottingham, i2CAT Foundation, Calle Gran Capita, 2-4 Edifici Nexus (Campus Nord Upc), 08034 Barcelona, Spain; School of Computer Science, Jubilee Campus, University of Nottingham, Nottingham NG8 1BB, UK; Abstract: Beer fermentation is typically monitored by periodic sampling and off-line analysis. The fermentation is conventionally monitored through off-line wort density measurements until a predetermined ethanol concentration is reached [3], after which the process is continued for a predefined time for development of flavour compounds [4]. This requires manual sampling, takes time, and wastes resources by disposing of the measured sample. By providing real-time, automatic alcohol concentration measurements, in-line and on-line techniques would ensure product quality through early detection of anomalous batches, allow effective scheduling of production equipment by predicting fermentation endpoint, and reduce the burden of manual sampling by operators. Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations
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