数字农科院2.0

Personalized diets based on multi-objective optimization of nutrition and sensory characteristics: A digital strategy for enhancing food quality

文献类型: 外文期刊

作者: Wang, Zhangtie;Huang, Qinle;Ji, Shengyang;Amrouche, Amel Thanina;Zhu, Yuhang;Li, Xiang;Shen, Jianfu;Xiao, Hang;Li, Peiwu;Lu, Baiyi

作者机构:

关键词: Individuation;Precision nutrition;Food perception;Computational methods;Food technologies;Machine learning

期刊名称: TRENDS IN FOOD SCIENCE & TECHNOLOGY

ISSN: 0924-2244

年卷期: 2025 年 156 卷

页码:

收录情况: SCIE(2025版) ; ; EI(2025版)

摘要: Background: Personalized diets aim at designing dietary interventions for individuals and satisfying sensory preferences. An increasing interest is how to balance the deliciousness and nutrition rather than focus on one side of them. With the development of technology and interdisciplinary integration, a review of the current situation and future trends is necessary. Scope and approach: In this paper, we introduced the food nutrition and sensory digitization, respectively. The computational methods and food technologies in multi-objective optimization (MOO) of nutrition and sensory characteristics were reviewed comprehensively. The application of food technology in improving the nutrition and sensory quality of food was summarized as well. Key findings and conclusions: This review indicated the methods of digital representation in precision nutrition, food perception science, and MOO. We highlighted the fact that the MOO would improve nutrition and sensory qualities simultaneously in personalized dietary design. The application of MOO emphasized the importance of diversified demands with higher food quality. Advances in big data and machine learning promoted the development of personalized diets with sensory enjoyment and health benefits. This review focused on assisting the field of precision nutrition more scientifically, intelligently, and characteristically. The personalized strategy based on MOO will provide guidance for the development of healthy food. It is believed that the personalized strategy of nutrition and sensory MOO driven by artificial intelligence will be a popular trend in future food.

分类号:

  • 相关文献

[1]Using music to perceiving food: New musicalization algorithms and application in adulteration goat milk. Yang, Huihui,Wang, Yutang,Li, Ping,Li, Zhixiang,Li, Long,Huang, Yatao,Luo, Bowen,Zhao, Jinyong,Wang, Pengyue,Guo, Qi,Wang, Fengzhong. 2025

[2]The discovery approaches and detection methods of microRNAs. Huang, Yong,Wang, Sheng Peng,Tang, Shun Ming,Zhang, Guo Zheng,Shen, Xing Jia,Huang, Yong,Wang, Sheng Peng,Tang, Shun Ming,Zhang, Guo Zheng,Shen, Xing Jia,Zou, Quan. 2011

[3]Integrating massive RNA-seq data to elucidate transcriptome dynamics in Drosophila melanogaster. Qian, Sheng Hu,Shi, Meng-Wei,Wang, Dan-Yang,Fear, Justin M.,Chen, Lu,Tu, Yi-Xuan,Liu, Hong-Shan,Zhang, Yuan,Zhang, Shuai-Jie,Yu, Shan-Shan,Oliver, Brian,Chen, Zhen-Xia. 2023

[4]Identification of characteristic lipids of Tan lamb and their potential health benefits. Le Xu,Shaobo Li,Pengyu Chen,Minghui Gu,Qi Yang,Li Chen,Dequan Zhang. 2025

[5]A Review of the Latest Advances in Aquaculture Nutrition Research. Ai, Chunxiang,Leng, Xiangjun,Luo, Zhi,Zhou, Zhigang,Ai, Qinghui. 2025

[6]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

[7]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

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

[9]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

[10]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

[11]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

[12]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

[13]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

[14]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

[15]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

[16]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

[17]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

[18]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

[19]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

[20]Comparing Machine Learning Algorithms for Pixel/Object-Based Classifications of Semi-Arid Grassland in Northern China Using Multisource Medium Resolution Imageries. Wu N.,Crusiol L.G.T.,Liu G.,Wuyun D.,Han G.. 2023

作者其他论文 更多>>