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 (AI) is becoming popular
in every field, AI 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
AI concepts and learning methods have emerged in the past few years.
This review illustrates several well-known food databases, focusing
on their primary contents, interfaces, and other essential features.
We also introduce some of the most common machine learning and deep
learning methods. Furthermore, a few studies related to food databases
are given as examples, demonstrating their applications in food pairing,
food–drug interactions, and molecular modeling. Based on the
results of these applications, it is expected that the combination
of food databases and AI will play an essential role in food science
and food chemistry.
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