Collaborative Innovation Center of Fragrance Flavour and Cosmetics, School of Perfume and Aroma Technology, Shanghai Institute of Technology, Shanghai 201418, China.
Laboratory for Physical Chemistry, ETH Zürich, 8093 Zürich, Switzerland.
J Agric Food Chem. 2023 May 10;71(18):6789-6802. doi: 10.1021/acs.jafc.3c00909. Epub 2023 Apr 27.
Flavor molecules are commonly used in the food industry to enhance product quality and consumer experiences but are associated with potential human health risks, highlighting the need for safer alternatives. To address these health-associated challenges and promote reasonable application, several databases for flavor molecules have been constructed. However, no existing studies have comprehensively summarized these data resources according to quality, focused fields, and potential gaps. Here, we systematically summarized 25 flavor molecule databases published within the last 20 years and revealed that data inaccessibility, untimely updates, and nonstandard flavor descriptions are the main limitations of current studies. We examined the development of computational approaches (e.g., machine learning and molecular simulation) for the identification of novel flavor molecules and discussed their major challenges regarding throughput, model interpretability, and the lack of gold-standard data sets for equitable model evaluation. Additionally, we discussed future strategies for the mining and designing of novel flavor molecules based on multi-omics and artificial intelligence to provide a new foundation for flavor science research.
风味分子在食品工业中被广泛应用于提高产品质量和消费者体验，但它们也与潜在的人类健康风险有关，这凸显了寻找更安全替代品的必要性。为了解决这些与健康相关的挑战并促进合理应用，已经构建了多个风味分子数据库。然而，目前尚无研究根据质量、重点领域和潜在差距对这些数据资源进行全面总结。在这里，我们系统地总结了过去 20 年中发表的 25 个风味分子数据库，揭示了当前研究的主要局限性在于数据不可访问、更新不及时以及风味描述不规范。我们还研究了用于识别新型风味分子的计算方法（例如机器学习和分子模拟）的发展，并讨论了它们在通量、模型可解释性以及缺乏公平模型评估的黄金标准数据集方面面临的主要挑战。此外，我们还讨论了基于多组学和人工智能挖掘和设计新型风味分子的未来策略，为风味科学研究提供新的基础。
