Qi Lulu, Gao Xinchang, Pan Daodong, Sun Yangying, Cai Zhendong, Xiong Yongzhao, Dang Yali
State Key Laboratory for Managing Biotic and Chemical Threats to the Quality and Safety of AgroProducts, College of Food and Pharmaceutical Sciences, Ningbo University, Ningbo, Zhejiang, China.
Department of Chemistry, Tsinghua University, Beijing, China.
Compr Rev Food Sci Food Saf. 2022 Mar;21(2):1462-1490. doi: 10.1111/1541-4337.12916. Epub 2022 Feb 24.
Umami is an important element affecting food taste, and the development of umami peptides is a topic of interest in food-flavoring research. The existing technology used for traditional screening of umami peptides is time-consuming and labor-intensive, making it difficult to meet the requirements of high-throughput screening, which limits the rapid development of umami peptides. The difficulty in performing a standard measurement of umami intensity is another problem that restricts the development of umami peptides. The existing methods are not sensitive and specific, making it difficult to achieve a standard evaluation of umami taste. This review summarizes the umami receptors and umami peptides, focusing on the problems restricting the development of umami peptides, high-throughput screening, and establishment of evaluation standards. The rapid screening of umami peptides was realized based on molecular docking technology and a machine learning method, and the standard evaluation of umami could be realized with a bionic taste sensor. The progress of rapid screening and evaluation methods significantly promotes the study of umami peptides and increases its application in the seasoning industry.
鲜味是影响食物味道的重要元素，鲜味肽的开发是食品调味研究中的一个热门话题。用于传统鲜味肽筛选的现有技术耗时且费力，难以满足高通量筛选的要求，这限制了鲜味肽的快速发展。难以对鲜味强度进行标准测量是另一个制约鲜味肽发展的问题。现有方法不灵敏且缺乏特异性，难以实现对鲜味的标准评估。本综述总结了鲜味受体和鲜味肽，重点关注限制鲜味肽发展、高通量筛选以及评估标准建立的问题。基于分子对接技术和机器学习方法实现了鲜味肽的快速筛选，并且可以用仿生味觉传感器实现对鲜味的标准评估。快速筛选和评估方法的进展显著推动了鲜味肽的研究，并增加了其在调味品行业的应用。
