数字农科院2.0

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： Donglin Cai;Xueqing Li;Huifang Liu;Liankui Wen;Di Qu

作者机构：

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

期刊名称： Trends in Food Science and Technology

ISSN： 0924-2244

年卷期： 2024 年 154 卷

页码：

收录情况： SCIE（2024版） ; ; EI（2024版）

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.

分类号：

  • 相关文献

[1]Mass spectrometry in food authentication and origin traceability. Dou, Xinjing,Zhang, Liangxiao,Yang, Ruinan,Wang, Xiao,Yu, Li,Yue, Xiaofeng,Ma, Fei,Mao, Jin,Wang, Xiupin,Zhang, Wen,Li, Peiwu. 2022

[2]Application and Possible Mechanism of Microbial Fermentation and Enzyme Catalysis in Regulation of Food Flavour. Feng Wang,Mingtong Wang,Ling Xu,Jingya Qian,Baoguo Xu,Xianli Gao,Zhongyang Ding,Kai Cui. 2025

[3]The Effect of Imidacloprid on the Volatile Organic Compound Profile of Strawberries: New Insights from Flavoromics. Ning Yue,Hongping Wang,Chunmei Li,Chen Zhang,Simeng Li,Jing Wang,Fen Jin. 2023

[4]Analysis of flavor and widely metabolomics differences in black sesame before and after processing. Yini Yang,Linhai Wang,Yunhai Wang,Yuting An,Qi Zhou,Xia Xiang. 2024

[5]Unravelling the formation of characteristic aroma of traditional braised pork through untargeted and targeted flavoromics. Yao, Yishun,Huang, Meigui,Liao, Qiuhong,Wang, Xiaomin,Yu, Jingyang,Hayat, Khizar,Zhang, Xiaoming,Ho, Chi-Tang. 2024

[6]Exploring the contribution of phosphatidylcholine and triglyceride on the formation of beef aroma-active compounds with thermal oxidation system. Yujie Shi,Jing Li,Longzhu Zhou,Junmin Zhang,Xiaohui Feng,Weihai Xing,Chaohua Tang,Yueyu Bai. 2025

[7]Evolutionarily Informed Deep Learning Methods F.or Predicting Relative Transcript A bundance From Dna Sequence. Washburn, Jacob D.,Wang, Hai,Wang, Hai,Wang, Hai,Valluru, Ravi,Ramstein, Guillaume,Mejia-Guerra, Maria Katherine,Kremling, Karl A.,Wang, Hai,Buckler, Edward S.,Buckler, Edward S.. 2019

[8]Using Machine Learning in Environmental Tax Reform Assessment for Sustainable Development: A Case Study of Hubei Province, China. Zheng, Yinger,Zheng, Yinger,Zheng, Haixia,Zheng, Haixia,Ye, Xinyue. 2016

[9]Determination of internal qualities of Newhall navel oranges based on NIR spectroscopy using machine learning. Liu, Cong,Yang, Simon X.,Liu, Cong,Deng, Lie.

[10]A comparative study for least angle regression on NIR spectra analysis to determine internal qualities of navel oranges. Liu, Cong,Yang, Simon X.,Liu, Cong,Deng, Lie. 2015

[11]Using evolutionary machine learning to characterize and optimize co-pyrolysis of biomass feedstocks and polymeric wastes. Shahbeik H.,Shafizadeh A.,Nadian M.H.,Jeddi D.,Mirjalili S.,Yang Y.,Lam S.S.,Pan J.,Tabatabaei M.,Aghbashlo M.. 2023

[12]Virtual screening strategy for anti-DPP-IV natural flavonoid derivatives based on machine learning. Lu, Gen,Pan, Fei,Li, Xiaotong,Zhu, Zehui,Zhao, Lei,Wu, Ya,Tian, Wenli,Peng, Wenjun,Liu, Jinling. 2023

[13]Effects of different floral periods and environmental factors on royal jelly identification by stable isotopes and machine learning analyses during non-migratory beekeeping. Zhaolong Liu,Xin Yin,Hongxia Li,Dong Qiao,Lanzhen Chen. 2023

[14]Several models combined with ultrasound techniques to predict breast muscle weight in broilers. Zhengda Li,Jumei Zheng,Bingxing An,Xiaochun Ma,Fan Ying,Fuli Kong,Jie Wen,Guiping Zhao. 2023

[15]Ecosystem responses dominate the trends of annual gross primary productivity over terrestrial ecosystems of China during 2000–2020. Xian Jin Zhu,Gui Rui Yu,Zhi Chen,Wei Kang Zhang,Lang Han,Qiu Feng Wang,Hua Qi,Meng Yang,Zhao Gang Liu,Xiao Jun Dou,Le Xin Ma,Shi Ping Chen,Shao Min Liu,Hui Min Wang,Jun Hua Yan,Jun Lei Tan,Fa Wei Zhang,Feng Hua Zhao,Ying Nian Li,Yi Ping Zhang,Pei Li Shi,Jiao Jun Zhu,Jia Bing Wu,Zhong Hui Zhao,Yan Bin Hao,Li Qing Sha,Yu Cui Zhang,Shi Cheng Jiang,Feng Xue Gu,Zhi Xiang Wu,Yang Jian Zhang,Li Zhou,Ya Kun Tang,Bing Rui Jia,Yu Qiang Li,Qing Hai Song,Gang Dong,Yan Hong Gao,Zheng De Jiang,Dan Sun,Jian Lin Wang,Qi Hua He,Xin Hu Li,Fei Wang,Wen Xue Wei,Zheng Miao Deng,Xiang Xiang Hao,Xiao Li Liu,Xi Feng Zhang,Zhi Lin Zhu. 2023

[16]Advances in the Study of Biochemical, Morphological and Physiological Traits of Wheat and Sorghum Crops in Australia Using Hyperspectral Data and Machine Learning. A. B. Potgieter,C. Camino,T. Poblete,X. Zhi,S. Reynolds-Massey-Reed,Y. Zhao,A. Belwalkar,J. Ruizhu,B. George-Jaeggli,S. Chapman,D. Jordan,A. Wu,G. L. Hammer,P. J. Zarco-Tejada. 2023

[17]Rapid and Non-Invasive Assessment of Texture Profile Analysis of Common Carp (Cyprinus carpio L.) Using Hyperspectral Imaging and Machine Learning. Yi Ming Cao,Yan Zhang,Shuang Ting Yu,Kai Kuo Wang,Ying Jie Chen,Zi Ming Xu,Zi Yao Ma,Hong Lu Chen,Qi Wang,Ran Zhao,Xiao Qing Sun,Jiong Tang Li. 2023

[18]Improving Genomic Prediction with Machine Learning Incorporating TPE for Hyperparameters Optimization. Liang, Mang,An, Bingxing,Li, Keanning,Du, Lili,Deng, Tianyu,Cao, Sheng,Du, Yueying,Xu, Lingyang,Gao, Xue,Zhang, Lupei,Li, Junya,Gao, Huijiang. 2022

[19]Enhancing leaf area index and biomass estimation in maize with feature augmentation from unmanned aerial vehicle-based nadir and cross-circling oblique photography. Shuaipeng Fei,Shunfu Xiao,Qing Li,Meiyan Shu,Weiguang Zhai,Yonggui Xiao,Zhen Chen,Helong Yu,Yuntao Ma. 2023

[20]Biomass microwave pyrolysis characterization by machine learning for sustainable rural biorefineries. Yadong Yang,Hossein Shahbeik,Alireza Shafizadeh,Nima Masoudnia,Shahin Rafiee,Yijia Zhang,Junting Pan,Meisam Tabatabaei,Mortaza Aghbashlo. 2022

作者其他论文 更多>>