HAVLIŠ, Jan, Klára NOVOTNÁ a 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, roč. 1096, 1-2, s. 50-57. ISSN 0021-9673. |
Další formáty:
BibTeX
LaTeX
RIS
@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 a 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, roč.~1096, 1-2, s.~50-57. ISSN~0021-9673.
|