您好，欢迎访问中国农业科学院 机构知识库！

全部

  • 全部
  • 标题
  • 学者
  • 机构
  • 关键词

Machine learning and flavoromics-based research strategies for determining the characteristic flavor of food: A review

文献类型： 外文期刊

作者： Cai, Donglin 1 ; Li, Xueqing 1 ; Liu, Huifang 1 ; Wen, Liankui 2 ; Qu, Di 1 ;

作者机构： 1.Chinese Acad Agr Sci, Inst Special Anim & Plant Sci, Changchun 130112, Jilin, Peoples R China

2.Jilin Agr Univ, Inst Food Sci & Engn, Changchun 130118, Jilin, Peoples R China

3.Jilin Agr Univ, Inst Chinese Med Mat, Changchun 130118, Jilin, Peoples R China

关键词： Food flavor; Machine learning; Flavoromics; Analytical technique

期刊名称：TRENDS IN FOOD SCIENCE & TECHNOLOGY （ 影响因子：15.4； 五年影响因子：18.4 ）

ISSN： 0924-2244

年卷期： 2024 年 154 卷

页码：

收录情况： SCI

摘要： Background: Flavor is an important indicator of food quality. In recent years, machine learning (ML) has been widely used in food feature flavor mining and analysis. However, case summaries and ML, and flavoromics analyses are lacking. Scope and approach: This paper highlights recent advances in the joint application of food flavoromics analysis and ML algorithms, including detection techniques commonly used in food flavor research and data analysis methods for different ML models. These techniques are analyzed and compared, and their advantages and limitations are discussed. Key findings and conclusions: The application of ML in the detection of food flavor compounds can produce strong analytical and predictive performance. Each ML model has its own advantages and disadvantages. Models such as k-nearest neighbor (KNN), support vector machine (SVM), decision tree (DT) and deep learning (DL) can handle complex and larger datasets but have high data volume requirements, require time-consuming training, and are prone to overfitting. Models such as principal component analysis (PCA), partial least squares (PLS), and random forest (RF) are relatively simple, have a low data volume requirement, and can be trained quickly but may suffer from underfitting when dealing with complex data. When multiple ML models are used together to predict flavor, the model with the highest accuracy or that is better suited for the prediction task can be quickly identified. In conclusion, the combination of food flavor analysis and ML has great potential for specialty food flavor mining, quality assessment, and authenticity.

  • 相关文献
作者其他论文 更多>>
置顶
购物车
反馈

© 京ICP备09089781号-29 主办单位：中国农业科学院

学术资源： 中国农业科学院 农业专业知识服务系统 农科联盟资源共建共享平台 中国农业科技文献与信息服务平台 中国农业期刊集成服务平台