打破苦味障碍:结合计算机筛选、感官科学和双路径除苦技术的前沿策略

IF 15.4 1区 农林科学 Q1 FOOD SCIENCE & TECHNOLOGY
Xiao Chen , Yiyue Chen , Kang Chen , Liyan Zhao , Leiqing Pan , Zixuan Gu , Zhi Cheng , Weijie Lan
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引用次数: 0

摘要

苦味是影响消费者接受度和饮食选择的五种基本味觉之一。食品中的苦味成分来源广泛、结构多样、味觉机制复杂。准确发现和鉴别食品中的苦味成分是提高食品适口性的重要前提。本文综述了利用计算方法,即数据库检索、机器学习和分子对接/模拟等方法在苦味分子鉴定方面的进展。尽管基于计算机的方法加速了苦味化合物的筛选,但它们的预测往往缺乏生物学和感官背景。本综述通过提出一个闭环框架,将计算工具与感官引导的验证协同起来,系统地解决了这一差距。至关重要的是,我们强调了新兴的抑制苦味的策略,包括技术引导的去苦味策略(超声、热/微波处理和发酵)和感知修饰方法(针对味觉途径的苦味阻断剂和通过不一致的香气进行香气-味觉相互作用)。这种双路径策略为设计美味的功能性食品提供了可行的见解。主要发现和结论本综述的发现突出了计算机驱动筛选的快速发现,同时解决了其局限性,例如无法捕获矩阵效应和依赖现有数据质量。感官分析弥补了计算预测和现实世界感知之间的差距。人工智能、分子对接/模拟和感官验证的集成创建了一个苦味化合物识别的闭环系统。对食物苦味的进一步研究应集中在收集苦味受体的结构数据,开发针对关键苦味分子的定制化脱苦味方法,并绘制香气-苦味相互作用的确切神经通路,以广泛应用多感觉整合脱苦味策略。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Breaking the bitterness barrier: Cutting-Edge strategies combining computer screening, sensory science, and dual-path debittering technologies

Background

Bitterness is one of the five basic taste sensations influencing consumer acceptance and dietary choices. The bitter ingredients in food are characterized by wide sources, diverse structures, and complex taste sensation mechanisms. Accurately discovering and identifying bitter ingredients in food is an important prerequisite for improving food palatability.

Scope and approach

This review explored advancements in bitter molecule identification using computational approaches, i.e., database searching, machine learning, and molecular docking/simulation. Though computer-based approaches have accelerated bitter compound screening, their predictions often lack biological and sensory context. This review systematically addresses this gap by proposing a closed-loop framework that synergizes computational tools with sensory-guided validation. Crucially, we highlight emerging strategies to suppress bitterness, including technique-guided debittering strategies (ultrasonication, thermal/microwave treatment, and fermentation) and perception-modifying approaches (bitter-blockers targeting the taste pathway and aroma-taste interactions by incongruent aromas). This dual-path strategy offers actionable insights for designing palatable functional foods.

Key findings and conclusions

The findings of this review highlighted the fast discovery of computer-driven screenings while addressing their limitations, such as the inability to capture matrix effects and reliance on existing data quality. Sensory analysis bridges the gap between computational prediction and real-world perception. The integration of AI, molecular docking/simulation, and sensory validation creates a closed-loop system for bitter compound identification. Further research in food bitterness should focus on collecting structural data on bitter taste receptors, developing tailored debittering approaches targeting key bitter molecules, and mapping the exact neurological pathways of aroma-bitterness interaction for a wide application of the multisensory-integration debittering strategy.
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来源期刊
Trends in Food Science & Technology
Trends in Food Science & Technology 工程技术-食品科技
CiteScore
32.50
自引率
2.60%
发文量
322
审稿时长
37 days
期刊介绍: Trends in Food Science & Technology is a prestigious international journal that specializes in peer-reviewed articles covering the latest advancements in technology, food science, and human nutrition. It serves as a bridge between specialized primary journals and general trade magazines, providing readable and scientifically rigorous reviews and commentaries on current research developments and their potential applications in the food industry. Unlike traditional journals, Trends in Food Science & Technology does not publish original research papers. Instead, it focuses on critical and comprehensive reviews to offer valuable insights for professionals in the field. By bringing together cutting-edge research and industry applications, this journal plays a vital role in disseminating knowledge and facilitating advancements in the food science and technology sector.
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