About: Spectrophotometric analysis of nucleic acid bases in binary and ternary mixtures by partial least squares and artificial neural networks     Goto   Sponge   NotDistinct   Permalink

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  • The contribution shows a comparative study of the use of partial least squares (PLS) and artificial neural networks (ANNs) to analyze nucleic acid bases (adenine - A, cytosine - C, thymine -T) in mixtures by UV-Vis spectrophotometry. Multivariate calibration based on a suitable experimental design (ED) and soft modeling for the quantitative analysis of fully overlapped ultraviolet spectra of A+C binary and/or A+C+T ternary systems were employed. The optimal ANN architecture, enabling to model the system, was established by means of TRAJAN program. Using the multivariate statistical method SIMCA the linearity of the PLS model for dilute solutions containing two or three bases in acetate buffer (pH 4.7) was proved. A combination of the chemometric methods and derivative spectrophotometry was applied. The ability of ANN and PLS to model binary and ternary systems was evaluated on the basis of the root means square error for prediction (RMSEP) and the agreement factor (AF).
  • The contribution shows a comparative study of the use of partial least squares (PLS) and artificial neural networks (ANNs) to analyze nucleic acid bases (adenine - A, cytosine - C, thymine -T) in mixtures by UV-Vis spectrophotometry. Multivariate calibration based on a suitable experimental design (ED) and soft modeling for the quantitative analysis of fully overlapped ultraviolet spectra of A+C binary and/or A+C+T ternary systems were employed. The optimal ANN architecture, enabling to model the system, was established by means of TRAJAN program. Using the multivariate statistical method SIMCA the linearity of the PLS model for dilute solutions containing two or three bases in acetate buffer (pH 4.7) was proved. A combination of the chemometric methods and derivative spectrophotometry was applied. The ability of ANN and PLS to model binary and ternary systems was evaluated on the basis of the root means square error for prediction (RMSEP) and the agreement factor (AF). (en)
Title
  • Spectrophotometric analysis of nucleic acid bases in binary and ternary mixtures by partial least squares and artificial neural networks
  • Spectrophotometric analysis of nucleic acid bases in binary and ternary mixtures by partial least squares and artificial neural networks (en)
skos:prefLabel
  • Spectrophotometric analysis of nucleic acid bases in binary and ternary mixtures by partial least squares and artificial neural networks
  • Spectrophotometric analysis of nucleic acid bases in binary and ternary mixtures by partial least squares and artificial neural networks (en)
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  • RIV/00216224:14310/06:00019330!RIV10-MSM-14310___
http://linked.open...avai/riv/aktivita
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  • P(IAA100040602), P(LC06035), Z(MSM0021622412)
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
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  • 500785
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  • RIV/00216224:14310/06:00019330
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  • artificial neural networks; partial least squares; adenine; cytosine; thymine; UV-Vis spectrophotometry (en)
http://linked.open.../riv/klicoveSlovo
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  • [5A8EC101D02C]
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  • Lake Balaton, Hungary
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  • Veszprém
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  • 3rd International Symposium on Computer Applications and Chemometrics in Analytical Chemistry SCAC 2006
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  • Havel, Josef
  • Trnková, Libuše
  • Topinková, Jana
  • Pena-Méndez, Eladia Maria
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number of pages
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  • University of Veszprém
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  • 963-9696-01-3
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  • 14310
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