2005
Optimization of high performance liquid chromatography separation of neuroprotective peptides. Fractional experimental design combined with Artificial neural networks
HAVLIŠ, Jan, Klára NOVOTNÁ a Josef HAVELZákladní údaje
Originální název
Optimization of high performance liquid chromatography separation of neuroprotective peptides. Fractional experimental design combined with Artificial neural networks
Název česky
Optimalizace separace neuroprotektivních peptidů vysoko-účinnou kapalinovou chromatografií. Dílčí plány pokusů kombinované s Umělými neuronovými sítěmi
Autoři
HAVLIŠ, Jan (203 Česká republika, garant), Klára NOVOTNÁ (203 Česká republika) a Josef HAVEL (203 Česká republika)
Vydání
Journal of Chromatography A, Elsevier B.V. 2005, 0021-9673
Další údaje
Jazyk
angličtina
Typ výsledku
Článek v odborném periodiku
Obor
10406 Analytical chemistry
Stát vydavatele
Nizozemské království
Utajení
není předmětem státního či obchodního tajemství
Impakt faktor
Impact factor: 3.096
Kód RIV
RIV/00216224:14310/05:00019956
Organizační jednotka
Přírodovědecká fakulta
UT WoS
000233672100006
Klíčová slova anglicky
optimisation of separation; artificial neural networks; ANN; experimental design; fractional experimental design; neuroprotective peptides; HPLC; liquid chromatography
Štítky
Příznaky
Mezinárodní význam, Recenzováno
Změněno: 2. 7. 2009 18:59, doc. Mgr. Jan Havliš, Dr.
V originále
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.
Česky
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.
Návaznosti
| GA203/02/1103, projekt VaV |
| ||
| GA305/03/1100, projekt VaV |
| ||
| MSM 143100011, záměr |
|