Quantitative modelling of Plato and total flavonoids in Qingke wort at mashing and boiling stages based on FT-IR combined with deep learning and chemometrics

被引：2

作者：

Zong, Xuyan ^{[1
,2
]}

Zhou, Xianjiang ^{[1
,2
]}

Cao, Xinyue ^{[1
,2
]}

Gao, Shun ^{[1
,2
]}

Zhang, Dongyang ^{[1
,2
]}

Zhang, Haoran ^{[1
,2
]}

Qiu, Ran ^{[3
]}

Wang, Yi ^{[4
]}

Wu, Jianhang ^{[1
,2
]}

Li, Li ^{[1
,2
]}

机构：

[1] Sichuan Univ Sci & Engn, Liquor Brewing Biotechnol & Applicat Key Lab Sichu, Yibin 644000, Sichuan, Peoples R China

[2] Sichuan Univ Sci & Engn, Coll Bioengn, Yibin 644000, Sichuan, Peoples R China

[3] China Resources Snow Breweries Co Ltd, Beijing 100000, Peoples R China

[4] Wuliangye Grp Co Ltd, Yibin 644000, Sichuan, Peoples R China

来源：

FOOD CHEMISTRY-X | 2024年 / 23卷

关键词：

Craft beer; FT-IR spectroscopy; CNN; LSTM; Chemometrics;

D O I：

10.1016/j.fochx.2024.101673

中图分类号：

O69 [应用化学];

学科分类号：

081704 ;

摘要：

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 (R2p = 0.9368) and boiling (R2p = 0.9398) phases but also as the best model for TFC in the boiling phase (R2p = 0.9154). This study demonstrates the great potential of deep learning and provides a new approach to quality control analysis in beer brewing.

引用

页数：13