Lijun Su , Huizhuo Ji , Jianlei Kong , Wenjing Yan , Qingchuan Zhang , Jian Li , Min Zuo
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Deep learning and electroencephalogram can enable high-throughput screening of taste peptides and analysis of taste mechanism, which has attracted extensive attention.</p></div><div><h3>Scope and approach</h3><p>This review summarizes the structural characteristics, taste cells and their subtypes, as well as cellular mechanism of taste transduction of the taste peptides. Significant attention has been focused on the high-throughput screening of taste peptides using deep learning model. Furthermore, the methods for evaluating taste intensity and clarifying the taste mechanism of taste peptides have also been reviewed.</p></div><div><h3>Key findings and conclusions</h3><p>The application of deep learning in the high-throughput screening of taste peptides maintained a strong prediction performance. Notably, the combination of multiple deep learning algorithms could enhance the accuracy of predicting taste peptides compared to a single algorithm. In addition, the application of electroencephalogram, bioelectronic tongue, and taste organoids-on-a-chip is an effective way to evaluate taste intensity, while fluorescence spectrum, surface plasmon resonance, and molecular docking are suitable for elucidating the taste-presenting mechanism of taste peptides. The review can provide a valuable reference for high-throughput screening of taste peptides and increases its application in the condiment industry.</p></div>","PeriodicalId":441,"journal":{"name":"Trends in Food Science & Technology","volume":null,"pages":null},"PeriodicalIF":15.1000,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Recent advances and applications of deep learning, electroencephalography, and modern analysis techniques in screening, evaluation, and mechanistic analysis of taste peptides\",\"authors\":\"Lijun Su , Huizhuo Ji , Jianlei Kong , Wenjing Yan , Qingchuan Zhang , Jian Li , Min Zuo\",\"doi\":\"10.1016/j.tifs.2024.104607\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Background</h3><p>Taste peptides are oligopeptides that improve the flavor and palatability of food. Due to their unique taste characteristics and nutritional values, the development of taste peptides has become a hot spot for food flavoring research and commercial applications. Screening and evaluating of taste peptides based on traditional experimental methods is inefficient and labor-intensive. Deep learning and electroencephalogram can enable high-throughput screening of taste peptides and analysis of taste mechanism, which has attracted extensive attention.</p></div><div><h3>Scope and approach</h3><p>This review summarizes the structural characteristics, taste cells and their subtypes, as well as cellular mechanism of taste transduction of the taste peptides. Significant attention has been focused on the high-throughput screening of taste peptides using deep learning model. Furthermore, the methods for evaluating taste intensity and clarifying the taste mechanism of taste peptides have also been reviewed.</p></div><div><h3>Key findings and conclusions</h3><p>The application of deep learning in the high-throughput screening of taste peptides maintained a strong prediction performance. Notably, the combination of multiple deep learning algorithms could enhance the accuracy of predicting taste peptides compared to a single algorithm. In addition, the application of electroencephalogram, bioelectronic tongue, and taste organoids-on-a-chip is an effective way to evaluate taste intensity, while fluorescence spectrum, surface plasmon resonance, and molecular docking are suitable for elucidating the taste-presenting mechanism of taste peptides. The review can provide a valuable reference for high-throughput screening of taste peptides and increases its application in the condiment industry.</p></div>\",\"PeriodicalId\":441,\"journal\":{\"name\":\"Trends in Food Science & Technology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":15.1000,\"publicationDate\":\"2024-06-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Trends in Food Science & Technology\",\"FirstCategoryId\":\"97\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0924224424002838\",\"RegionNum\":1,\"RegionCategory\":\"农林科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"FOOD SCIENCE & TECHNOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Trends in Food Science & Technology","FirstCategoryId":"97","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0924224424002838","RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"FOOD SCIENCE & TECHNOLOGY","Score":null,"Total":0}
Recent advances and applications of deep learning, electroencephalography, and modern analysis techniques in screening, evaluation, and mechanistic analysis of taste peptides
Background
Taste peptides are oligopeptides that improve the flavor and palatability of food. Due to their unique taste characteristics and nutritional values, the development of taste peptides has become a hot spot for food flavoring research and commercial applications. Screening and evaluating of taste peptides based on traditional experimental methods is inefficient and labor-intensive. Deep learning and electroencephalogram can enable high-throughput screening of taste peptides and analysis of taste mechanism, which has attracted extensive attention.
Scope and approach
This review summarizes the structural characteristics, taste cells and their subtypes, as well as cellular mechanism of taste transduction of the taste peptides. Significant attention has been focused on the high-throughput screening of taste peptides using deep learning model. Furthermore, the methods for evaluating taste intensity and clarifying the taste mechanism of taste peptides have also been reviewed.
Key findings and conclusions
The application of deep learning in the high-throughput screening of taste peptides maintained a strong prediction performance. Notably, the combination of multiple deep learning algorithms could enhance the accuracy of predicting taste peptides compared to a single algorithm. In addition, the application of electroencephalogram, bioelectronic tongue, and taste organoids-on-a-chip is an effective way to evaluate taste intensity, while fluorescence spectrum, surface plasmon resonance, and molecular docking are suitable for elucidating the taste-presenting mechanism of taste peptides. The review can provide a valuable reference for high-throughput screening of taste peptides and increases its application in the condiment industry.
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.