Using a Matlab implemented algorithm for UV-vis spectral resolution for pKa determination and multicomponent analysis.

Yotam Gonen, Giora Rytwo
{"title":"Using a Matlab implemented algorithm for UV-vis spectral resolution for pKa determination and multicomponent analysis.","authors":"Yotam Gonen,&nbsp;Giora Rytwo","doi":"10.4137/aci.s3499","DOIUrl":null,"url":null,"abstract":"<p><p>A Matlab implemented computer code for spectral resolution is presented. The code enables the user to resolve the UV-visible absorption spectrum of a mixture of up to 3 previously known components, to the individual components, thus, evaluating their quantities. The resolving procedure is based on searching the combination of the components which yields the spectrum which is the most similar (minimal RMSE) to the measured spectrum of the mixture. Examples of using the software for pK(a) value estimation and multicomponent analysis are presented and other implementations are suggested.</p>","PeriodicalId":7781,"journal":{"name":"Analytical Chemistry Insights","volume":"4 ","pages":"21-7"},"PeriodicalIF":0.0000,"publicationDate":"2009-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.4137/aci.s3499","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Analytical Chemistry Insights","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4137/aci.s3499","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3

Abstract

A Matlab implemented computer code for spectral resolution is presented. The code enables the user to resolve the UV-visible absorption spectrum of a mixture of up to 3 previously known components, to the individual components, thus, evaluating their quantities. The resolving procedure is based on searching the combination of the components which yields the spectrum which is the most similar (minimal RMSE) to the measured spectrum of the mixture. Examples of using the software for pK(a) value estimation and multicomponent analysis are presented and other implementations are suggested.

Abstract Image

Abstract Image

Abstract Image

利用Matlab实现了紫外-可见光谱分辨率算法,用于pKa的测定和多组分分析。
给出了一个用Matlab实现的光谱分辨率计算机代码。该代码使用户能够将多达3种已知成分的混合物的紫外-可见吸收光谱分解为单个成分,从而评估其数量。解析过程是基于搜索产生与混合物的测量光谱最相似(最小RMSE)的光谱的组分的组合。给出了使用该软件进行pK(a)值估计和多成分分析的实例,并提出了其他实现方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信