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Review
. 2023 Apr 25;8(18):15854-15864.
doi: 10.1021/acsomega.2c07722. eCollection 2023 May 9.

When Machine Learning and Deep Learning Come to the Big Data in Food Chemistry

Yufeng Jane Tseng  1 , Pei-Jiun Chuang  1 , Michael Appell  2
Affiliations
Review

When Machine Learning and Deep Learning Come to the Big Data in Food Chemistry

Yufeng Jane Tseng et al. ACS Omega. .

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 (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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Conflict of interest statement

The authors declare no competing financial interest.

Figures

Figure 1
Figure 1
Big data in food. Note: this graphic was created with BioRender.com.
Figure 2
Figure 2
Concept of machine learning and deep learning in food data.
See this image and copyright information in PMC

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