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

被引：0
|
作者：
Wang N. [1 ,2 ]
Li X. [1 ,2 ]
Huang Y. [2 ]
Wang Y. [2 ,3 ]
Lai H. [2 ]
Yang M. [2 ]
Tang C. [4 ]
Ge L. [1 ]
Zhao N. [2 ]
机构：
[1] College of Life Sciences, Sichuan Normal University, Chengdu
[2] Institute of Agricultural Products Processing, Sichuan Academy of Agriculture Sciences, Chengdu
[3] College of Food and Bioengineering, Chengdu University, Chengdu
[4] College of Computer Science, Sichuan University, Chengdu
来源：
Science and Technology of Food Industry | 2024年 / 45卷 / 13期
关键词：
data processing; flavor; machine learning; microbial structure; personalized consumption; traditional fermented food;
D O I：
10.13386/j.issn1002-0306.2023070288
中图分类号：
学科分类号：
摘要：
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. © The Author(s) 2024.
引用
收藏
页码：360 / 367
页数：7
相关论文
共 75 条
  • [1] DONG X M, RUAN Z Q, JIANG X W., Relationship between microorganisms and flavor formation in traditional fermented food with flavor of soy sauce[J], Chinese Journal of Food Science, 22, 7, (2022)
  • [2] STAVROS P., The rendering of traditional fermented foods in human diet:Distribution of health benefits and nutritional benefits [J], Fermentation, 8, 12, (2022)
  • [3] OSCAR Z, IFAGBEMI B C, YENOUKOUNME E K, Et al., Traditional fermented foods of benin republic:Microbiological safety and health benefits[J], Discover Food, 3, 1, (2023)
  • [4] LIAO Y M, AO X L, KANG H Y, Et al., Research progress on interaction mechanism between lactic acid bacteria and yeast in traditional fermented food[J], Food and Fermentation Industry, 49, 3, (2023)
  • [5] CHEN J R, BIAN X, YANG Y, Et al., Research progress on microbial diversity of traditional fermented foods in china[J], Chinese Seasoning, 47, 2, (2022)
  • [6] XU M, SU S, ZHANG Z, Et al., Two sides of the same coin: Meta-analysis uncovered the potential benefits and risks of traditional fermented foods at a large geographical scale[J], Frontiers in Microbiology, 13, (2022)
  • [7] WU Q, ZHU Y, FANG C, Et al., Can we control microbiota in spontaneous food fermentation?–Chinese liquor as a case example[J], Trends in Food Science & Technology, 110, pp. 321-331, (2021)
  • [8] YUE X Y., Modeling and optimization of fermentation process based on machine learning, (2021)
  • [9] WIKANDARI R, KINANTI D A, PERMATASARI R D, Et al., Correlations between the chemical, microbiological characteristics and sensory profile of fungal fermented food[J], Fermentation, 7, 4, (2021)
  • [10] LUCA R, MICHELE C, CONCETTA B M., Chromatographic determination of biogenic amines in four typical Italian cheeses: Correlations with processing and nutritional characteristics through a chemometric approach[J], Journal of the Science of Food and Agriculture, 99, 11, (2019)
12345678