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Statements

Subject Item
n2:RIV%2F00216224%3A14310%2F02%3A00006479%21RIV%2F2003%2FAV0%2F143103%2FN
rdf:type
n6:Vysledek skos:Concept
dcterms:description
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 reliable and prec 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 reliable and prec
dcterms:title
Partial least squares and artificial neural networks for multicomponent analysis from derivative UV-Vis spectra Partial least squares and artificial neural networks for multicomponent analysis from derivative UV-Vis spectra
skos:prefLabel
Partial least squares and artificial neural networks for multicomponent analysis from derivative UV-Vis spectra Partial least squares and artificial neural networks for multicomponent analysis from derivative UV-Vis spectra
skos:notation
RIV/00216224:14310/02:00006479!RIV/2003/AV0/143103/N
n3:strany
P21
n3:aktivita
n11:P n11:Z
n3:aktivity
P(IAA1163201), Z(MSM 143100011)
n3:dodaniDat
n19:2003
n3:domaciTvurceVysledku
n10:3790185 n10:6955231 n10:7437056
n3:druhVysledku
n14:D
n3:duvernostUdaju
n22:S
n3:entitaPredkladatele
n4:predkladatel
n3:idSjednocenehoVysledku
657830
n3:idVysledku
RIV/00216224:14310/02:00006479
n3:jazykVysledku
n16:eng
n3:klicovaSlova
Partial least squares (PLS);artificial neural networks(ANN);multicomponent analysis;derivative UV-Vis spectra;adenine;cytosine
n3:klicoveSlovo
n5:adenine n5:multicomponent%20analysis n5:Partial%20least%20squares%20%28PLS%29 n5:derivative%20UV-Vis%20spectra n5:cytosine n5:artificial%20neural%20networks%28ANN%29
n3:kontrolniKodProRIV
[409D60E39F3F]
n3:mistoKonaniAkce
September, 1. -5.2002, Brno Czech Republic
n3:mistoVydani
Czech Republic, Brno
n3:nazevZdroje
International Chemometric Conference - CHEMOMETRICS VI
n3:obor
n17:CB
n3:pocetDomacichTvurcuVysledku
3
n3:pocetTvurcuVysledku
4
n3:pocetUcastnikuAkce
0
n3:pocetZahranicnichUcastnikuAkce
0
n3:projekt
n18:IAA1163201
n3:rokUplatneniVysledku
n19:2002
n3:tvurceVysledku
Havel, Josef Peňa-Méndez, Eladia M. Topinková, Jana Trnková, Libuše
n3:typAkce
n8:WRD
n3:zahajeniAkce
2002-01-01+01:00
n3:zamer
n21:MSM%20143100011
s:numberOfPages
1
n13:hasPublisher
Masaryk University, Brno, Czech Republic
n20:isbn
80-210-2918-8
n15:organizacniJednotka
14310