{"title":"在无分析标准的非目标分析中定量化学物质 - 了解电喷雾离子化的机理并进行预测","authors":"Trevor A. Johnson, Dimitri P. Abrahamsson","doi":"10.1016/j.coesh.2023.100529","DOIUrl":null,"url":null,"abstract":"<div><p>The constant creation and release of new chemicals to the environment is forming an ever-widening gap between available analytical standards and known chemicals. Developing non-targeted analysis (NTA) methods that have the ability to detect a broad spectrum of compounds is critical for research and analysis of emerging contaminants. There is a need for methods that make it possible to identify compound structures based on their MS and MS/MS information and quantify them without analytical standards. Method refinements that utilize machine learning algorithms and chemical descriptors to estimate the instrument response of particular compounds have made progress in recent years. This narrative review seeks to summarize the current state of the field of NTA toward quantification of unknowns without the use of analytical standards. Despite the limited accumulation of validation studies on real samples, the ongoing enhancement in data processing and refinement of machine learning tools could lead to more comprehensive chemical coverage of NTA and validated quantitative NTA methods, thus boosting confidence in their usage and enhancing the utility of quantitative NTA.</p></div>","PeriodicalId":52296,"journal":{"name":"Current Opinion in Environmental Science and Health","volume":"37 ","pages":"Article 100529"},"PeriodicalIF":6.7000,"publicationDate":"2023-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2468584423000892/pdfft?md5=7af90c6fca406cdc1081c27a422a63a9&pid=1-s2.0-S2468584423000892-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Quantification of chemicals in non-targeted analysis without analytical standards – Understanding the mechanism of electrospray ionization and making predictions\",\"authors\":\"Trevor A. Johnson, Dimitri P. Abrahamsson\",\"doi\":\"10.1016/j.coesh.2023.100529\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>The constant creation and release of new chemicals to the environment is forming an ever-widening gap between available analytical standards and known chemicals. Developing non-targeted analysis (NTA) methods that have the ability to detect a broad spectrum of compounds is critical for research and analysis of emerging contaminants. There is a need for methods that make it possible to identify compound structures based on their MS and MS/MS information and quantify them without analytical standards. Method refinements that utilize machine learning algorithms and chemical descriptors to estimate the instrument response of particular compounds have made progress in recent years. This narrative review seeks to summarize the current state of the field of NTA toward quantification of unknowns without the use of analytical standards. Despite the limited accumulation of validation studies on real samples, the ongoing enhancement in data processing and refinement of machine learning tools could lead to more comprehensive chemical coverage of NTA and validated quantitative NTA methods, thus boosting confidence in their usage and enhancing the utility of quantitative NTA.</p></div>\",\"PeriodicalId\":52296,\"journal\":{\"name\":\"Current Opinion in Environmental Science and Health\",\"volume\":\"37 \",\"pages\":\"Article 100529\"},\"PeriodicalIF\":6.7000,\"publicationDate\":\"2023-12-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S2468584423000892/pdfft?md5=7af90c6fca406cdc1081c27a422a63a9&pid=1-s2.0-S2468584423000892-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Current Opinion in Environmental Science and Health\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2468584423000892\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENVIRONMENTAL SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Current Opinion in Environmental Science and Health","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2468584423000892","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
引用次数: 0
摘要
新化学物质的不断产生和向环境的不断释放,使得现有分析标准与已知化学物质之间的差距越来越大。开发能够检测多种化合物的非目标分析 (NTA) 方法对于研究和分析新出现的污染物至关重要。我们需要能根据 MS 和 MS/MS 信息识别化合物结构并在没有分析标准的情况下对其进行量化的方法。近年来,利用机器学习算法和化学描述符来估计特定化合物的仪器响应的方法改进取得了进展。本综述旨在总结 NTA 领域在不使用分析标准的情况下量化未知化合物的现状。尽管对真实样品的验证研究积累有限,但数据处理的不断改进和机器学习工具的不断完善可使 NTA 和经过验证的定量 NTA 方法的化学覆盖面更加全面,从而增强人们使用这些方法的信心,提高定量 NTA 的实用性。
Quantification of chemicals in non-targeted analysis without analytical standards – Understanding the mechanism of electrospray ionization and making predictions
The constant creation and release of new chemicals to the environment is forming an ever-widening gap between available analytical standards and known chemicals. Developing non-targeted analysis (NTA) methods that have the ability to detect a broad spectrum of compounds is critical for research and analysis of emerging contaminants. There is a need for methods that make it possible to identify compound structures based on their MS and MS/MS information and quantify them without analytical standards. Method refinements that utilize machine learning algorithms and chemical descriptors to estimate the instrument response of particular compounds have made progress in recent years. This narrative review seeks to summarize the current state of the field of NTA toward quantification of unknowns without the use of analytical standards. Despite the limited accumulation of validation studies on real samples, the ongoing enhancement in data processing and refinement of machine learning tools could lead to more comprehensive chemical coverage of NTA and validated quantitative NTA methods, thus boosting confidence in their usage and enhancing the utility of quantitative NTA.