Balanced scaling as a pretreatment step in Multivariate Curve Resolution analysis of noisy data
Analysis of data sets with heteroscedastic error has been a challenging problem in the chemometrics literature. Different methods have been proposed for analyzing this type of data, in particular, using the Multivariate Curve Resolution Alternating Least Squares (MCR-ALS) method. The present paper i...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión aceptada para publicación |
| Fecha de publicación: | 2021 |
| País: | España |
| Institución: | Consejo Superior de Investigaciones Científicas (CSIC) |
| Repositorio: | DIGITAL.CSIC. Repositorio Institucional del CSIC |
| OAI Identifier: | oai:digital.csic.es:10261/264701 |
| Acceso en línea: | http://hdl.handle.net/10261/264701 https://api.elsevier.com/content/abstract/scopus_id/85096838349 |
| Access Level: | acceso abierto |
| Palabra clave: | Multivariate Curve Resolution Alternating Least Squares Balanced-Scaling Maximum Likelihood Principal Component Analysis |
| Sumario: | Analysis of data sets with heteroscedastic error has been a challenging problem in the chemometrics literature. Different methods have been proposed for analyzing this type of data, in particular, using the Multivariate Curve Resolution Alternating Least Squares (MCR-ALS) method. The present paper introduces the Balanced Scaling (BS) approach as a pretreatment step combined with the Multivariate Curve Resolution Alternating Least Squares (BS-MCR-ALS) method as an adequate procedure to analyze data with heteroscedastic noise. In particular, for the analysis of environmental data, the Balanced Scaling (BS) method can be a useful approach to provide an optimal individual data scaling. The performance of the BS-MCR-ALS method is compared with the performance of the Maximum Likelihood Principal Component Analysis Multivariate Curve Resolution Alternating Least Squares (MLPCA-MCR-ALS) method, and also with the performance of the traditional Multivariate Curve Resolution Alternating Least Squares (MCR-ALS) method in the analysis of data sets with different type of error structures. The results obtained in this comparison revealed that the solutions obtained by BS-MCR-ALS and MLPCA-MCR-ALS were very similar. |
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