Zong Xuyan, Zhou Xianjiang, Cao Xinyue, Gao Shun, Zhang Dongyang, Zhang Haoran, Qiu Ran, Wang Yi, Wu Jianhang, Li Li
Liquor Brewing Biotechnology and Application Key Laboratory of Sichuan Province, Sichuan University of Science and Engineering, Yibin, 644000, Sichuan, China.
College of Bioengineering, Sichuan University of Science and Engineering, Yibin, 644000, Sichuan, China.
Food Chem X. 2024 Jul 18;23:101673. doi: 10.1016/j.fochx.2024.101673. eCollection 2024 Oct 30.
Craft beer brewers need to learn process control strategies from traditional industrial production to ensure the consistent quality of the finished product. In this study, FT-IR combined with deep learning was used for the first time to model and analyze the Plato degree and total flavonoid content of Qingke beer during the mashing and boiling stages and to compare the effectiveness with traditional chemometrics methods. Two deep learning neural networks were designed, the effect of variable input methods on the effectiveness of the models was discussed. The experimental results showed that the CARS-LSTM model had the best predictive performance, not only as the best quantitative model for Plato in the mashing (Rp = 0.9368) and boiling (Rp = 0.9398) phases but also as the best model for TFC in the boiling phase (Rp = 0.9154). This study demonstrates the great potential of deep learning and provides a new approach to quality control analysis in beer brewing.
精酿啤酒酿造者需要从传统工业生产中学习过程控制策略，以确保成品质量的一致性。在本研究中，首次将傅里叶变换红外光谱（FT-IR）与深度学习相结合，对青稞啤酒糖化和煮沸阶段的柏拉图度和总黄酮含量进行建模和分析，并与传统化学计量学方法的有效性进行比较。设计了两个深度学习神经网络，讨论了变量输入方法对模型有效性的影响。实验结果表明，CARS-LSTM模型具有最佳的预测性能，不仅是糖化阶段（Rp = 0.9368）和煮沸阶段（Rp = 0.9398）柏拉图度的最佳定量模型，也是煮沸阶段总黄酮含量（Rp = 0.9154）的最佳模型。本研究展示了深度学习的巨大潜力，并为啤酒酿造中的质量控制分析提供了一种新方法。
