Predicting Alcohol Concentration during Beer Fermentation Using Ultrasonic Measurements and Machine Learning
Predicting Alcohol Concentration during Beer Fermentation Using Ultrasonic Measurements and Machine Learning
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DOI：
10.3390/fermentation7010034
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发表时间：
2021-02
期刊：
影响因子：
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通讯作者：
A. Bowler;J. Escrig;M. Pound;N. Watson
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文献类型：
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作者：
A. Bowler;J. Escrig;M. Pound;N. Watson
啤酒发酵通常通过定期取样和离线分析来监测。在线传感器将消除对耗时的手动操作的需要，并提供发酵培养基的实时评估。这项工作使用低成本的超声波传感器结合机器学习来预测啤酒发酵过程中的酒精浓度。最高精度模型（R2 = 0.952，平均绝对误差（MAE）= 0.265，均方误差（MSE）= 0.136）使用了基于传输的超声波传感技术沿着测量温度。然而，第二个最准确的模型（R2 = 0.948，MAE = 0.283，MSE = 0.146）使用了基于反射的技术，没有温度。基于反射的技术和省略的温度数据是新颖的这项研究，并证明了潜在的非侵入式传感器来监测啤酒发酵。
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.