| HAVLIŠ, Jan, Klára NOVOTNÁ and Josef HAVEL. Optimization of high performance liquid chromatography separation of neuroprotective peptides. Fractional experimental design combined with Artificial neural networks. Journal of Chromatography A. Elsevier B.V., 2005, vol. 1096, 1-2, p. 50-57. ISSN 0021-9673. |
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@article{617326,
author = {Havliš, Jan and Novotná, Klára and Havel, Josef},
article_number = {1-2},
keywords = {optimisation of separation; artificial neural networks; ANN; experimental design; fractional experimental design; neuroprotective peptides; HPLC; liquid chromatography},
language = {eng},
issn = {0021-9673},
journal = {Journal of Chromatography A},
title = {Optimization of high performance liquid chromatography separation of neuroprotective peptides. Fractional experimental design combined with Artificial neural networks},
volume = {1096},
year = {2005}
}
TY - JOUR
ID - 617326
AU - Havliš, Jan - Novotná, Klára - Havel, Josef
PY - 2005
TI - Optimization of high performance liquid chromatography separation of neuroprotective peptides. Fractional experimental design combined with Artificial neural networks
JF - Journal of Chromatography A
VL - 1096
IS - 1-2
SP - 50-57
EP - 50-57
PB - Elsevier B.V.
SN - 00219673
KW - optimisation of separation
KW - artificial neural networks
KW - ANN
KW - experimental design
KW - fractional experimental design
KW - neuroprotective peptides
KW - HPLC
KW - liquid chromatography
N2 - The study of experimental design conjunction with artificial neural networks for optimisation of isocratic ion-pair reverse phase HPLC separation of neuroprotective peptides is reported. Different types of experimental designs (full-factorial, fractional) were studied as suitable input and output data sources for ANN training and examined on mixtures of humanin derivatives. The independent input variables were: composition of mobile phase, including its pH, and column temperature. In case of a simple mixture of two peptides, the retention time of the most retentive component and resolution were used as the dependent variables (outputs). In case of a complex mixture with unknown number of components, number of peaks, sum of resolutions and retention time of ultimate peak were considered as output variables. Fractional factorial experimental design has been proved to produce sufficient input data for ANN approximation and thus further allowed decreasing the number of experiments necessary for optimisation. After the optimal separation conditions were found, fractions with peptides were collected and their analysis using off-line matrix assisted laser desorption/ionisation time of flight mass spectrometry (MALDI-TOF-MS) was performed.
ER -
HAVLIŠ, Jan, Klára NOVOTNÁ and Josef HAVEL. Optimization of high performance liquid chromatography separation of neuroprotective peptides. Fractional experimental design combined with Artificial neural networks. \textit{Journal of Chromatography A}. Elsevier B.V., 2005, vol.~1096, 1-2, p.~50-57. ISSN~0021-9673.
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