Advances in the Application of Machine Learning to Microbial Structure and Quality Control of Traditional Fermented Foods

WANG Ningxiaoxuan; LI Xin; HUANG Yuli; WANG Yali; LAI Haimei; YANG Menglu; TANG Chenwei; GE Lihong; ZHAO Nan

doi:10.13386/j.issn1002-0306.2023070288

Volume 45 Issue 13

Jun. 2024

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Science and Technology of Food Industry > 2024 > 45(13): 360-367. > DOI: 10.13386/j.issn1002-0306.2023070288

WANG Ningxiaoxuan, LI Xin, HUANG Yuli, et al. Advances in the Application of Machine Learning to Microbial Structure and Quality Control of Traditional Fermented Foods[J]. Science and Technology of Food Industry, 2024, 45(13): 360−367. (in Chinese with English abstract). doi: 10.13386/j.issn1002-0306.2023070288.

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Advances in the Application of Machine Learning to Microbial Structure and Quality Control of Traditional Fermented Foods

WANG Ningxiaoxuan^{1, 2,},
LI Xin^{1, 2},
HUANG Yuli²,
WANG Yali^{2, 3},
LAI Haimei²,
YANG Menglu²,
TANG Chenwei⁴,
GE Lihong^1, ,,
ZHAO Nan^2, ,

1.
College of Life Sciences, Sichuan Normal University, Chengdu 610101, China
2.
Institute of Agricultural Products Processing, Sichuan Academy of Agriculture Sciences, Chengdu 610066, China
3.
College of Food and Bioengineering, Chengdu University, Chengdu 610106, China
4.
College of Computer Science, Sichuan University, Chengdu 610065, China

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Received Date: August 01, 2023
Available Online: June 20, 2024

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Abstract

Abstract

The unique flavor properties and rich nutrients of traditional fermented food are closely related to its complex and variable microbial structure, which also makes it difficult to control the quality of final fermented product. In order to explore the changes of microbial structure and sensory property and nutritional property in the process of food fermentation and the internal relationship between them, the data analysis process is a key step. Therefore, it is necessary to establish a fast and accurate data analysis method for quality control of fermented food. Machine learning has the advantages of high-dimensional simplification rate, large data throughput and high prediction accuracy, showing great application potential in the field of quality control of fermented food. Hence, machine learning has become one of the research hotspots. This paper reviews the application of machine learning in the quality control of fermented food. On the basis of an overview of common models of machine learning, this paper systematically summarizes the application of machine learning in the prediction of microbial structure evolution, flavor compound composition analysis and customization of personalized consumption in the process of food fermentation. The problems and developmental trends in the application of machine learning to quality control of traditional fermented food are summarized and prospected. Although the application of machine learning in fermented food is still confined by the problems such as insufficient general applicability of the model, limited quality indicators, and limited personalized consumption scenario, etc., with the iterative update of the technical model, the adaptation for multi-factors and whole process, and the application expansion in the background of personalized consumption, machine learning will show a greater value for practical application in the field of fermented food. The purpose of this study is to provide guidance for the further application of machine learning in the standardized and controllable production of traditional fermented food.
- machine learning,
- traditional fermented food,
- data processing,
- microbial structure,
- flavor,
- personalized consumption

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References

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