Detailed Information on Publication Record
2005
Optimization of high performance liquid chromatography separation of neuroprotective peptides. Fractional experimental design combined with Artificial neural networks
HAVLIŠ, Jan, Klára NOVOTNÁ and Josef HAVELBasic information
Original name
Optimization of high performance liquid chromatography separation of neuroprotective peptides. Fractional experimental design combined with Artificial neural networks
Name in Czech
Optimalizace separace neuroprotektivních peptidů vysoko-účinnou kapalinovou chromatografií. Dílčí plány pokusů kombinované s Umělými neuronovými sítěmi
Authors
HAVLIŠ, Jan (203 Czech Republic, guarantor), Klára NOVOTNÁ (203 Czech Republic) and Josef HAVEL (203 Czech Republic)
Edition
Journal of Chromatography A, Elsevier B.V. 2005, 0021-9673
Other information
Language
English
Type of outcome
Článek v odborném periodiku
Field of Study
10406 Analytical chemistry
Country of publisher
Netherlands
Confidentiality degree
není předmětem státního či obchodního tajemství
Impact factor
Impact factor: 3.096
RIV identification code
RIV/00216224:14310/05:00019956
Organization unit
Faculty of Science
UT WoS
000233672100006
Keywords in English
optimisation of separation; artificial neural networks; ANN; experimental design; fractional experimental design; neuroprotective peptides; HPLC; liquid chromatography
Tags
Tags
International impact, Reviewed
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.
In Czech
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.
Links
| GA203/02/1103, research and development project |
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| GA305/03/1100, research and development project |
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| MSM 143100011, plan (intention) |
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