Artificial intelligence-based approaches for traditional fermented alcoholic beverages' development: review and prospect

被引:10
作者
Yu, Huakun [1 ,2 ]
Liu, Shuangping [1 ,3 ,4 ]
Qin, Hui [4 ]
Zhou, Zhilei [1 ,3 ,5 ]
Zhao, Hongyuan [1 ,2 ,6 ]
Zhang, Suyi [4 ]
Mao, Jian [1 ,3 ,5 ]
机构
[1] Jiangnan Univ, Natl Engn Res Ctr Cereal Fermentat & Food Biomfg, Sch Food Sci & Technol, State Key Lab Food Sci & Technol, Wuxi, Jiangsu, Peoples R China
[2] Jiangnan Univ, Jiangsu Prov Res Ctr Bioact Prod Proc Technol, Wuxi, Jiangsu, Peoples R China
[3] Southern Marine Sci & Engn Guangdong Lab, Guangzhou, Peoples R China
[4] Luzhou Laojiao Grp Co Ltd, Luzhou, Peoples R China
[5] Jiangnan Univ Shaoxing Ind Technol Res Inst, Shaoxing Key Lab Tradit Fermentat Food & Human Hl, Shaoxing, Zhejiang, Peoples R China
[6] Jiangnan Univ, Sch Artificial Intelligence & Comp Sci, Wuxi, Jiangsu, Peoples R China
基金
中国国家自然科学基金;
关键词
Traditional fermented alcoholic beverages; artificial intelligence; big data; fermentation regulation; microbial community; WINE; CONSUMERS; BEHAVIOR; BEER;
D O I
10.1080/10408398.2022.2128034
中图分类号
TS2 [食品工业];
学科分类号
0832 ;
摘要
Traditional fermented alcoholic beverages (TFABs) have gained widespread acceptance and enjoyed great popularity for centuries. COVID-19 pandemics lead to the surge in health demand for diet, thus TFABs once again attract increased focus for the health benefits. Though the production technology is quite mature, food companies and research institutions are looking for transformative innovation in TFABs to make healthy, nutritious offerings that give a competitive advantage in current beverage market. The implementation of intelligent platforms enables companies and researchers to gather, store and analyze data in a more convenient way. The development of data collection methods contributed to the big data environment of TFABs, providing a fresh perspective that helps brewers to observe and improve the production steps. Among data analytical tools, Artificial Intelligence (AI) is considered to be one of the most promising methodological approaches for big data analytics and decision-making of automated production, and machine learning (ML) is an important method to fulfill the goal. This review describes the development trends and challenges of TFABs in big data era and summarize the application of AI-based methods in TFABs. Finally, we provide perspectives on the potential research directions of new frontiers in application of AI approaches in the supply chain of TFABs.
引用
收藏
页码:2879 / 2889
页数:11
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