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Simultaneous spectrophotometric determination of manganese, zinc and cobalt by kernel partial least–squares method

Simultaneous spectrophotometric determination of Mn, Zn and Co was studied by two methods, classical partial least-squares (PLS) and kernel partial least-squares (KPLS), with 2-(5-bromo-2- pyridylazo)-5-diethylaminephenol (5-Br-PADAP) and cetyl pyridinium bromide (CPB). Two programs, SPGRPLS and SPG...

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Detalles Bibliográficos
Autores principales: Gao, Ling, Ren, Shouxin
Formato: Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 1998
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2548164/
https://www.ncbi.nlm.nih.gov/pubmed/18924832
http://dx.doi.org/10.1155/S1463924698000224
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author Gao, Ling
Ren, Shouxin
author_facet Gao, Ling
Ren, Shouxin
author_sort Gao, Ling
collection PubMed
description Simultaneous spectrophotometric determination of Mn, Zn and Co was studied by two methods, classical partial least-squares (PLS) and kernel partial least-squares (KPLS), with 2-(5-bromo-2- pyridylazo)-5-diethylaminephenol (5-Br-PADAP) and cetyl pyridinium bromide (CPB). Two programs, SPGRPLS and SPGRKPLS, were designed to perform the calculations. Eight error functions were calculated for deducing the number of factors. Data reductions were performed using principle component analysis. The KPLS method was applied for the rapid determination from a data matrix with many wavelengths and fewer numbers of samples. The relative standard errors of prediction (RSEP) for all components with KPLS and PLS methods were the same (0.0247). Experimental results showed both methods to be successful even where there was severe overlap of spectra.
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spelling pubmed-25481642008-10-16 Simultaneous spectrophotometric determination of manganese, zinc and cobalt by kernel partial least–squares method Gao, Ling Ren, Shouxin J Automat Chem Research Article Simultaneous spectrophotometric determination of Mn, Zn and Co was studied by two methods, classical partial least-squares (PLS) and kernel partial least-squares (KPLS), with 2-(5-bromo-2- pyridylazo)-5-diethylaminephenol (5-Br-PADAP) and cetyl pyridinium bromide (CPB). Two programs, SPGRPLS and SPGRKPLS, were designed to perform the calculations. Eight error functions were calculated for deducing the number of factors. Data reductions were performed using principle component analysis. The KPLS method was applied for the rapid determination from a data matrix with many wavelengths and fewer numbers of samples. The relative standard errors of prediction (RSEP) for all components with KPLS and PLS methods were the same (0.0247). Experimental results showed both methods to be successful even where there was severe overlap of spectra. Hindawi Publishing Corporation 1998 /pmc/articles/PMC2548164/ /pubmed/18924832 http://dx.doi.org/10.1155/S1463924698000224 Text en Copyright © 1998 Hindawi Publishing Corporation. http://creativecommons.org/licenses/by/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Gao, Ling
Ren, Shouxin
Simultaneous spectrophotometric determination of manganese, zinc and cobalt by kernel partial least–squares method
title Simultaneous spectrophotometric determination of manganese, zinc and cobalt by kernel partial least–squares method
title_full Simultaneous spectrophotometric determination of manganese, zinc and cobalt by kernel partial least–squares method
title_fullStr Simultaneous spectrophotometric determination of manganese, zinc and cobalt by kernel partial least–squares method
title_full_unstemmed Simultaneous spectrophotometric determination of manganese, zinc and cobalt by kernel partial least–squares method
title_short Simultaneous spectrophotometric determination of manganese, zinc and cobalt by kernel partial least–squares method
title_sort simultaneous spectrophotometric determination of manganese, zinc and cobalt by kernel partial least–squares method
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2548164/
https://www.ncbi.nlm.nih.gov/pubmed/18924832
http://dx.doi.org/10.1155/S1463924698000224
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