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

Journal: Critical reviews in food science and nutrition
PMID: 36310425

Abstract

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.

Authors

  • Huakun Yu
    National Engineering Research Center for Cereal Fermentation and Food Biomanufacturing, State Key Laboratory of Food Science and Technology, School of Food Science and technology, Jiangnan University, Wuxi, China.
  • Shuangping Liu
    Center for Bio-inspired Energy Science, Northwestern University, 2145 Sheridan Road, Evanston, IL 60208, USA.
  • Hui Qin
    Department of Intensive Care Medicine, The Affiliated Changzhou No.2 People's Hospital of Nanjing Medical University, The Third Affiliated Hospital of Nanjing Medical University, Nanjing Medical University, Changzhou, China.
  • Zhilei Zhou
    National Engineering Research Center for Cereal Fermentation and Food Biomanufacturing, State Key Laboratory of Food Science and Technology, School of Food Science and technology, Jiangnan University, Wuxi, China.
  • Hongyuan Zhao
    National Engineering Research Center for Cereal Fermentation and Food Biomanufacturing, State Key Laboratory of Food Science and Technology, School of Food Science and technology, Jiangnan University, Wuxi, China.
  • Suyi Zhang
    State Key Laboratory of Transducer Technology, Aerospace Information Research Institute. Chinese Academy of Sciences, Beijing 100190, China.
  • Jian Mao
    State Key Laboratory of Heavy Oil Processing and College of Chemistry and Chemical Engineering, China University of Petroleum (East China), Qingdao 266580, China.

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