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传统的鲜味肽分离鉴定方法依靠分离纯化肽组分，具有成本高、效率低的缺点.随着计算机技术的快速发展，机器学习模型凭借高准确性和优秀预测性，已逐渐应用于鲜味肽分析和预测，有效克服了传统方法在鲜味肽筛选制备上的局限性.综述了不同食品来源的鲜味肽筛选制备技术、呈味机制、加工特性及生物活性研究的最新进展，探讨了虚拟筛选和计算机模拟技术在鲜味肽高通量筛选、味觉形成机制及其功能活性研究等方面的应用前景，旨在为后续鲜味肽制备技术开发、呈味机理以及生物活性研究提供参考.
Abstract：Umami peptides represent a novel class of umami substances that have been identified in recent years.These peptides not only exhibit favorable flavor characteristics but also demonstrate advantageous processing traits and biological activities, thus emerging as a focal point of research within the food science and food processing industries. Traditional methods for the separation and identification of umami peptides typically involve the extraction and purification of peptide components, which are often characterized by high costs and low efficiency.However, with the rapid advancements in computer technology, machine learning models that possess high predictive capabilities and accuracy have been implemented in the analysis and prediction of umami peptides. This approach effectively addresses the limitations associated with traditional methods used in the screening and preparation of these peptides. In this paper, we review the latest advancements in the preparation technologies,tasting mechanisms, processing characteristics, and biological activities of umami peptides derived from various food sources. Furthermore, we discuss the prospective applications of machine learning and computer simulation technologies in the high-throughput screening of umami peptides, the mechanisms underlying taste formation, and research into functional activities. Our objective is to provide a valuable reference for the development of preparation technologies, the mechanisms taste formation, and the biological activities of umami peptides.
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基本信息:
中图分类号:TS201.2
引用信息:
[1]郑文涛,王玉珠,杨雪,等.食源性鲜味肽筛选鉴定及生物活性研究进展[J].云南民族大学学报(自然科学版),2026,35(01):40-51.
基金信息:
云南省徐宝才专家工作站（202305AF150132）; 云南省科技厅科技计划（A3032023245）; 云南省教育厅科学研究基金（2024Y293）