Skip to main content
ARS Home » Midwest Area » Peoria, Illinois » National Center for Agricultural Utilization Research » Mycotoxin Prevention and Applied Microbiology Research » Research » Publications at this Location » Publication #399318
Research Project: Innovative Approaches to Monitor, Predict, and Reduce Fungal Toxins

Location: Mycotoxin Prevention and Applied Microbiology Research

Title: When machine learning and deep learning come to the big data in food chemistry

Author
item TSENG, YUFENG JANE - National Taiwan University
item CHUANG, PEI-JIUN - National Taiwan University
item Appell, Michael

Submitted to: ACS Omega
Publication Type: Review Article
Publication Acceptance Date: 4/7/2023
Publication Date: 4/25/2023
Citation: Tseng, Y.J., Chuang, P.-J., Appell, M. 2023. When machine learning and deep learning come to the big data in food chemistry. ACS Omega. 8:15854-15864. https://doi.org/10.1021/acsomega.2c07722?urlappend=%3Fref%3DPDF&jav=VoR&rel=cite-as.
DOI: https://doi.org/10.1021/acsomega.2c07722?urlappend=%3Fref%3DPDF&jav=VoR&rel=cite-as

Interpretive Summary:

Technical Abstract: Since the first food database was released over one hundred years ago, food databases have become more diversified, including food composition databases, food flavor databases, and food chemical compound databases. These databases provide detailed information about the nutritional compositions, flavor molecules, and chemical properties of various food compounds. As artificial intelligence is becoming popular in every field, artificial intelligence methods can also be applied to food industry research and molecular chemistry. Machine learning and deep learning are valuable tools for analyzing big data sources such as food databases. Studies investigating food compositions, flavors, and chemical compounds with artificial intelligence concepts and learning methods have emerged in the past few years. This review illustrated several well-known food databases, focusing on their primary contents, interfaces, and other essential features. We also introduced some of the most common machine learning and deep learning methods. Furthermore, a few studies related to food databases were given as examples, demonstrating their applications in food pairing, food-drug interaction, and molecular modeling. Based on the results of these applications, it is expected that the combination of food databases and artificial intelligence will play an essential role in food science and food chemistry.