导 读
成果介绍
传统上依赖于实验方法的食品风味科学，如今在人工智能（AI）的帮助下进入了一个充满希望的时代。通过将现有技术与人工智能相结合，研究人员可以在数字化环境中探索和开发新的风味物质，从而节省时间和资源。越来越多的研究将利用人工智能和大数据来提升产品风味、改善产品质量、满足消费者需求，并推动整个行业走向更智能、更可持续的未来。
该研究团队将在这篇综述中阐述风味识别的机制及其对营养调控的潜在影响。随着数据积累的增加和互联网信息技术的发展，食品风味数据库和食品配料数据库取得了长足的进步。这些数据库提供了各种食品化合物的营养成分、风味分子和化学性质的详细信息，为风味成分的快速评价和筛选技术的构建提供了宝贵的数据支持。随着人工智能在各个领域的普及，食品风味领域也迎来了新的发展机遇。
本综述探讨了风味识别的机制，以及人工智能通过高通量组学数据和筛选技术在加强食品风味分析中的作用。人工智能算法为科学改进产品配方提供了一条途径，从而提升风味和定制化膳食。此外，它还讨论了将人工智能融入食品风味行业所面临的安全挑战。
图文赏析
FIGURE 1：(a) Structural similarity tree diagram of flavor sensing proteins among GPCR proteins; (b) schematic diagram of the distribution of taste receptors. Taste receptors are widely distributed in multiple organs and tissues; (c) applications of common sensors in food ingredient clustering and nutritional assessment; (d) a multi-step-based strategy for screening flavors from potential chemical space. Source: (a) Drawn by GPCRdb36.
FIGURE 2：Schematic diagram of QSAR modeling steps of flavor substances.
FIGURE 3：(a) Display of available data for multi-omics data and individual information. (b) Flowchart of extracting professional knowledge through large language models (LLMs) to mine the relationships among food, medicine, and diseases, and providing personalized dietary advice through the mined relationships combined with artificial intelligence (AI). Among them, NER (named entity recognition) and NEL (named entity linking) are inherent operations in natural language processing, which, respectively, link the entities mentioned in the text to the corresponding entities in the knowledge base and identify specific types of entities in the text and sort them into predefined categories. (c) Schematic diagram of the method of providing personalized dietary guidance based on the fusion of food intake, physical exercise, advanced medical knowledge, and omics data, and improving the method based on personal taste.
原文链接
https://doi.org/10.1111/1541-4337.70068
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本文转载自“科学私享”微信公众号，原标题“最新！上海交通大学刘源教授团队食品顶刊发表“人工智能与食品风味”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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