导 读
成果介绍
个性化饮食旨在为个人设计膳食干预措施，同时满足感官偏好。消费者越来越关注如何同时满足美味和营养需求，而不是将其独立。随着食品加工技术的发展和多学科的交叉融合，基于营养与感官多目标优化的个性化膳食策略将指导食品加工向数字化设计转型升级。
该研究团队在这篇综述中详细回顾了数字化表征在精确营养、食品感知科学和多目标优化中的现有方法。随着大数据构建与分析技术的发展，食品营养成分数据库和表征方法得到了长足进步，仪器分析技术与人工智能也推动了食品感官数字化表征的快速发展。食品营养与感官的数字化表征技术为多目标优化提供了前提条件。该综述重点介绍了“食品营养与感官多目标优化”的设计架构，并综述了现代食品加工技术在提升食品品质中的重要作用。
多目标优化旨在为个性化饮食设计过程同时改善营养和感官品质。多目标优化的应用强调了多样化需求和更高质量食品品质的重要性。该论文旨在帮助精准营养领域更加科学化、智能化和特色化。基于多目标优化的个性化策略将为健康食品的创制提供指导，相信将是未来食品设计的趋势。
图文赏析
Figure 1 Food composition database and dietary nutrition evaluation. China Food Composition Tables (CFCT) and Food and Nutrient Database for Dietary Studies (FNDDS) represent the authoritative food composition database in China and USA, respectively. Food-Based Dietary Guidelines in Spain, Chinese Food Guide Pagoda, Dietary Guidelines for Americans, Food-Based Dietary Guidelines for South Africa, Australian Guide to Healthy Eating, and Food Guide for the Brazilian Population represent dietary guidelines of six continents. Healthy Eating Index (HEI), Mediterranean Dietary Score (MDS), Dietary Approaches to Stop Hypertension (DASH), and Dietary Inflammation Index (DII) are the classical dietary evaluation index model. Seven dietary nutrients have prominent healthy function. Small molecules in food, including phytochemicals, ethanol, and choline, also show significant nutritional effects. Chinese Food Guide Pagoda (CFGP) is an important basis for the dietary intake of Chinese residents.
Figure 2 Research methods of precision nutrition. There are some major methods for precision nutrition research, including organoids and organ chips, bioinformatics, epigenetics, metabolomics, proteomics, ribonucleic acid (RNA) sequencing, deoxyribonucleic acid (DNA) sequencing, and others.
Figure 3 Methods and developments of food perception science. The food senses go through three main stages, including sensory study, flavor study, and perception study. Sensory evaluation is a traditional food perception method. Flavor study reveals the key flavor substances. Artificial intelligence and digitization with big data is the future direction for perception study.
Figure 4 Basic theory and methods of multi-objective optimization (MOO) design. Varying food processing parameters will get results for characterizing the relationship between nutrition or sensory. Digitizing these aspects enables MOO to yield optimal solution sets. Individuals can then select personalized diets from these sets, generating interactive data for enhanced big data analysis.
原文链接
https://doi.org/10.1016/j.tifs.2024.104842
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本文转载自“科学私享”微信公众号，原标题“浙江大学陆柏益教授团队食品顶刊发表“基于营养与感官多目标优化的个性化膳食策略”重要综述”。转载仅用于学术分享，若有侵权，请后台留言联系修改或删除！
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