摘要
传统发酵食品独特的风味属性、丰富的营养成分与其复杂多变的微生物结构密切相关,这也使得发酵终产品的品质难以控制。为了探讨食品发酵过程中微生物结构、感官与营养品质变化规律及二者之间的内在联系,数据分析过程是关键步骤。因此,建立发酵食品品质控制的快速、准确数据分析方法非常必要。机器学习具有维度简化率高、数据通量大、预测精度高等优势,在发酵食品品质控制领域展现出巨大的应用潜力,已成为研究热点之一。本文综述了机器学习在发酵食品品质控制中的应用,在概述常用机器学习分类模型的基础上,系统阐述了机器学习在食品发酵过程中菌群结构演变预测、风味化合物组成分析以及个性化消费定制等方面的应用,并对机器学习应用于传统发酵食品品质控制中存在的问题及发展趋势进行了总结和展望。尽管目前机器学习在发酵食品中的应用仍受限于模型普适应不足、预测指标单一、个性化消费场景有限等问题,但随着技术模型的迭代更新、面向工艺全流程多因素的适应性改进以及在个性化消费背景下的应用拓展,机器学习在发酵食品领域将发挥出更大的实际应用价值。本研究旨在为机器学习在传统发酵食品的标准化、可控化生产中的进一步应用提供参考。
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 product
作者
王宁晓璇
李欣
黄玉立
王雅利
赖海梅
杨梦露
汤臣薇
葛黎红
赵楠
WANG Ningxiaoxuan;LI Xin;HUANG Yuli;WANG Yali;LAI Haimei;YANG Menglu;TANG Chenwei;GE Lihong;ZHAO Nan(College of Life Sciences,Sichuan Normal University,Chengdu 610101,China;Institute of Agricultural Products Processing,Sichuan Academy of Agriculture Sciences,Chengdu 610066,China;College of Food and Bioengineering,Chengdu University,Chengdu 610106,China;College of Computer Science,Sichuan University,Chengdu 610065,China)
出处
《食品工业科技》
CAS
北大核心
2024年第13期360-367,共8页
Science and Technology of Food Industry
基金
四川省自然科学基金(2022NSFSC1648)
四川省重点研发项目(2022YFN0017,2022YFN0014)
四川省农业科学院“1+9”揭榜挂帅项目(1+9KJGG007)。
关键词
机器学习
传统发酵食品
数据处理
微生物结构
风味
个性化消费
machine learning
traditional fermented food
data processing
microbial structure
flavor
personalized consumption
