摘要
建立BP神经网络模型模拟啤酒酿造过程中糖度变化和乙醇浓度变化。将啤酒酿造过程中的发酵温度、麦汁浓度、接种量及发酵时间作为输入数据,将糖度变化和乙醇浓度的变化作为输出数据,运用BP神经网络建立啤酒酿造过程的模型。使用此模型模拟了主酵温度8℃、麦汁浓度11°P、接种量为2×107个/mL时糖度变化和乙醇浓度变化,结果糖度预测的均方根误差为2.66%,乙醇浓度预测的均方根误差为14.60%。结果表明,使用此模型能够准确预测啤酒酿造过程糖度变化和乙醇浓度的变化。
The back-propagation (BP) neural network was used to predict sugar density and alcohol content during beer fermentation. A BP neural net- work model of beer fermentation was established using fermentation temperature, sugar density of wort, inoculum and fermentation time as input val- ues, and sugar density and alcohol content during beer fermentation as output values. After the model was trained, the sugar density and alcohol con- tent were predicted for the beer fermentation conducted at 8~C with 1 l^P wort and an inoculum of 2~107cells/ml. The root mean square error of pre- diction of sugar density and alcohol content were 2.66% and 14.60%, respectively. The results showed that the model could be applied for the predic- tion of sugar density and alcohol content during beer fermentation.
出处
《中国酿造》
CAS
2013年第1期25-28,共4页
China Brewing
基金
国家重点基础研究发展计划‘973计划’(No.2010CB735706)
啤酒生物发酵工程国家重点实验室开放基金(No.K2012006)
关键词
糖度
乙醇浓度
BP神经网络
sugar density
alcohol content
BP neural networks