食品科学 ›› 2024, Vol. 45 ›› Issue (10): 28-37.doi: 10.7506/spkx1002-6630-20231123-181

• 机器学习专栏 • 上一篇    下一篇

机器学习在食品风味领域的研究进展与未来趋势

陈靓,阳佳红,田星   

  1. (1.湖南中医药大学药学院,湖南 长沙 410208;2.湖南中医药大学 湖南省中医药民族医药国际联合实验室,湖南 长沙 410208)
  • 出版日期:2024-05-25 发布日期:2024-06-08
  • 基金资助:
    湖南省自然科学基金面上项目(2023JJ30445);湖南省教育厅优秀青年项目(22B0378); 湖南省中医药大学“双一流”建设学科“揭榜挂帅”项目(22JB2053);湖南省中医药管理局科研项目(B2024012)

Research Progress and Future Trends of Machine Learning in the Field of Food Flavor

CHEN Liang, YANG Jiahong, TIAN Xing   

  1. (1. School of Pharmacy, Hunan University of Chinese Medicine, Changsha 410208, China; 2. TCM and Ethnomedicine Innovation & Development International Laboratory, Hunan University of Chinese Medicine, Changsha 410208, China)
  • Online:2024-05-25 Published:2024-06-08

摘要: 随着生活水平不断提高,人们除了关心食物是否美味外,其健康要素和良好风味的结合也越来越受到重视。食品风味成分不仅是影响食品感官品质的重要因素,也是影响食品营养水平的关键指标。目前,传统方法对食品风味成分进行分析及预测费时费力,且无法处理大量数据。相比之下,机器学习是人工智能的核心,在区分差异性以及寻找共同性上具有传统分析技术难以比拟的优势,在食品风味分析领域已取得了良好的应用。基于此,本文围绕机器学习在食品风味领域的研究现状,介绍常用的机器学习方法的原理和优点,及其在食品风味预测及调节中的最新应用与前景。重点探讨现代智能感官检测技术结合机器学习在食品风味分析领域研究的优势与未来趋势,以期为食品风味分析与预测领域研究提供新思路和理论基础。

关键词: 机器学习;食品风味;风味分析与预测;智能感官检测技术;研究进展

Abstract: With the continuous improvement of living standards, people are concerned about not only whether foods are tasty or not, but also the combination of health elements and good flavor. Food flavor components are not only important factors in sensory quality, but also key indicators of the nutritional level of foods. At present, the traditional methods to evaluate and predict food flavor components are time-consuming and labor-intensive, and unable to handle large amounts of data. In contrast, machine learning (ML), the core of artificial intelligence, has incomparable advantages over traditional analytical techniques in distinguishing differences and finding commonalities, and has found good application in the field of food flavor analysis. In this context, this paper focuses on the current research status of ML in the field of food flavor, and introduces the principles and advantages of commonly used ML methods, as well as their latest applications and prospects in food flavor prediction and regulation. It also focuses on the advantages and future trends of modern intelligent sensory evaluation techniques combined with ML in the field of food flavor analysis, with a view to providing new ideas and theoretical foundations for food flavor analysis and prediction.

Key words: machine learning; food flavor; flavor analysis and prediction; intelligent sensory evaluation techniques; research progress

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