Optimization of high performance liquid chromatography separation of neuroprotective peptides. Fractional experimental design combined with Artificial neural networks

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.

Základní 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

• Originální 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: ANN, artificial neural networks, experimental design, fractional experimental design, HPLC, liquid chromatography, neuroprotective peptides, optimisation of separation
• Příznaky: Mezinárodní význam, Recenzováno

Anotace

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.

Anotace 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: Název: Umělé neuronové sítě a plánování pokusů v analytické chemii, zejména v separačních metodách

Investor: Grantová agentura ČR, Umělé neuronové sítě a plánování pokusů v analytické chemii, zejména v separačních metodách

• GA305/03/1100, projekt VaV: Název: Syntéza a studium neuroprotektivních peptidů odvozených od humaninu
• MSM 143100011, záměr: Název: Struktura a vazebné poměry, vlastnosti a analýza syntetických a přírodních molekulových ansamblů

Investor: Ministerstvo školství, mládeže a tělovýchovy ČR, Struktura a vazebné poměry, vlastnosti a analýza syntetických a přírodních molekulových ansamblů