{"title":"pyMCR: A Python Library for MultivariateCurve Resolution Analysis with Alternating Regression (MCR-AR).","authors":"C. Camp","doi":"10.1002/HTTPS://DOI.ORG/10.6028/JRES.124.018","DOIUrl":null,"url":null,"abstract":"pyMCR is a new open-source software library for performing multivariate curve\n resolution (MCR) analysis with an alternating regression scheme (MCR-AR). MCR is a\n chemometric method for elucidating measurement signatures of analytes and their relative\n abundance from a series of mixture measurements, without any knowledge of these values a\n priori. This software library, written in Python, enables users to perform MCR analysis\n with their choice of error functions for minimization, constraints, and regressors.\n Further, users can apply different constraints and regressors for signature and\n abundance calculations. Finally, this library enables users to develop their own\n constraints, regressors, and error functions or import them from existing\n libraries.","PeriodicalId":54766,"journal":{"name":"Journal of Research of the National Institute of Standards and Technology","volume":"124 1","pages":"1-10"},"PeriodicalIF":1.3000,"publicationDate":"2019-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"34","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Research of the National Institute of Standards and Technology","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1002/HTTPS://DOI.ORG/10.6028/JRES.124.018","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"INSTRUMENTS & INSTRUMENTATION","Score":null,"Total":0}
引用次数: 34
Abstract
pyMCR is a new open-source software library for performing multivariate curve
resolution (MCR) analysis with an alternating regression scheme (MCR-AR). MCR is a
chemometric method for elucidating measurement signatures of analytes and their relative
abundance from a series of mixture measurements, without any knowledge of these values a
priori. This software library, written in Python, enables users to perform MCR analysis
with their choice of error functions for minimization, constraints, and regressors.
Further, users can apply different constraints and regressors for signature and
abundance calculations. Finally, this library enables users to develop their own
constraints, regressors, and error functions or import them from existing
libraries.
期刊介绍：
The Journal of Research of the National Institute of Standards and Technology is the flagship publication of the National Institute of Standards and Technology. It has been published under various titles and forms since 1904, with its roots as Scientific Papers issued as the Bulletin of the Bureau of Standards.
In 1928, the Scientific Papers were combined with Technologic Papers, which reported results of investigations of material and methods of testing. This new publication was titled the Bureau of Standards Journal of Research.
The Journal of Research of NIST reports NIST research and development in metrology and related fields of physical science, engineering, applied mathematics, statistics, biotechnology, information technology.
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