Partial least squares and artificial neural networks for multicomponent analysis from derivative UV-Vis spectra

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14310%2F02%3A00006479" target="_blank" >RIV/00216224:14310/02:00006479 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Partial least squares and artificial neural networks for multicomponent analysis from derivative UV-Vis spectra

Popis výsledku v původním jazyce

This contribution presents a comparative study of the use of PLS and ANNs to analyze A and C mixtures using UV-Vis derivative spectra. The optimum ANN architecture enabling to model the system was established by means of TRAJAN 6.0 program. Several algorithms (Back propagation, Conjugate gradients, Quick propagation, and Delta-Bar Delta algorithm) were used for the training of the ANN to obtain a reliable model. With help of a suitable experimental design in combination with soft ANN modelling, the concentration of both A and C in mixtures can be quantified with an excellent accuracy (about 1 %). The quality of the testing set was evaluated on the basis of the average root mean square error for prediction (RMSEP) calculated from true and found values of A and C concentrations (RMSEP = 0.07 for A and 0.09 for C). It was found that ANN gives better results for the first and second derivative spectra than for original spectra. Furthermore, in comparison with PLS the ANN provides a more re

Název v anglickém jazyce

Partial least squares and artificial neural networks for multicomponent analysis from derivative UV-Vis spectra

Popis výsledku anglicky

This contribution presents a comparative study of the use of PLS and ANNs to analyze A and C mixtures using UV-Vis derivative spectra. The optimum ANN architecture enabling to model the system was established by means of TRAJAN 6.0 program. Several algorithms (Back propagation, Conjugate gradients, Quick propagation, and Delta-Bar Delta algorithm) were used for the training of the ANN to obtain a reliable model. With help of a suitable experimental design in combination with soft ANN modelling, the concentration of both A and C in mixtures can be quantified with an excellent accuracy (about 1 %). The quality of the testing set was evaluated on the basis of the average root mean square error for prediction (RMSEP) calculated from true and found values of A and C concentrations (RMSEP = 0.07 for A and 0.09 for C). It was found that ANN gives better results for the first and second derivative spectra than for original spectra. Furthermore, in comparison with PLS the ANN provides a more re

Druh

D - Stať ve sborníku

CEP obor

CB - Analytická chemie, separace

OECD FORD obor

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Projekt

<a href="/cs/project/IAA1163201" target="_blank" >IAA1163201: Využití adsorptivní přenosové a eliminační techniky pro elektrochemickou analýzu oligonukleotidů a nukleových kyselin</a><br>

Návaznosti

Z - Vyzkumny zamer (s odkazem do CEZ)

Rok uplatnění

2002

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

International Chemometric Conference - CHEMOMETRICS VI

ISBN

80-210-2918-8

ISSN

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e-ISSN

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Počet stran výsledku

1

Strana od-do

"P21"

Název nakladatele

Masaryk University, Brno, Czech Republic

Místo vydání

Czech Republic, Brno

Místo konání akce

September, 1. -5.2002, Brno Czech Republic

Datum konání akce

1. 1. 2002

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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