Chemometrical Tools in the Study of the Retention Behavior of Azole Antifungals
Апстракт
Certain chemometrical tools allow an efficient way to provide valuable data to evaluate the retention behavior of analytes in liquid chromatography. In this study of the retention behavior of azole antifungals, the experimental design was applied in combination with artificial neural networks (ANNs). Three potentially significant factors (methanol content, pH of the mobile phase and column temperature) were incorporated in the plan of experiments, defined by central composite design. As the system outputs, the retention factors of all six investigated substances (fluconazole, ketoconazole, bifonazole, clotrimazole, econazole and miconazole) were determined. The pattern for the analyzed behavior of the system was created by employing ANNs. The final, optimized topology of the highly predictive network was 3-8-6. Twelve experiments were used in a training set, whereas a back-propagation algorithm was optimal for network training. The ability of the defined network to predict the retentio...n of the investigated azoles was confirmed by correlations higher than 0.9912 for all analytes. The presented approach allowed the adequate prediction of the retention behavior of azoles, in addition to the extraction of important information for a better understanding of the analyzed system.
Извор:
Journal of Chromatographic Science, 2014, 52, 2, 95-102Издавач:
- Oxford Univ Press Inc, Cary
Финансирање / пројекти:
- Моделовање различитих хроматографских система са хемометријским приступом у фармацеутској анализи (RS-172052)
DOI: 10.1093/chromsci/bms211
ISSN: 0021-9665
PubMed: 23295779
WoS: 000342733800001
Scopus: 2-s2.0-84892694675
Институција/група
PharmacyTY - JOUR AU - Vemić, Ana AU - Malenović, Anđelija AU - Rakić, Tijana AU - Kostić, Nada AU - Jančić-Stojanović, Biljana PY - 2014 UR - https://farfar.pharmacy.bg.ac.rs/handle/123456789/2079 AB - Certain chemometrical tools allow an efficient way to provide valuable data to evaluate the retention behavior of analytes in liquid chromatography. In this study of the retention behavior of azole antifungals, the experimental design was applied in combination with artificial neural networks (ANNs). Three potentially significant factors (methanol content, pH of the mobile phase and column temperature) were incorporated in the plan of experiments, defined by central composite design. As the system outputs, the retention factors of all six investigated substances (fluconazole, ketoconazole, bifonazole, clotrimazole, econazole and miconazole) were determined. The pattern for the analyzed behavior of the system was created by employing ANNs. The final, optimized topology of the highly predictive network was 3-8-6. Twelve experiments were used in a training set, whereas a back-propagation algorithm was optimal for network training. The ability of the defined network to predict the retention of the investigated azoles was confirmed by correlations higher than 0.9912 for all analytes. The presented approach allowed the adequate prediction of the retention behavior of azoles, in addition to the extraction of important information for a better understanding of the analyzed system. PB - Oxford Univ Press Inc, Cary T2 - Journal of Chromatographic Science T1 - Chemometrical Tools in the Study of the Retention Behavior of Azole Antifungals VL - 52 IS - 2 SP - 95 EP - 102 DO - 10.1093/chromsci/bms211 ER -
@article{ author = "Vemić, Ana and Malenović, Anđelija and Rakić, Tijana and Kostić, Nada and Jančić-Stojanović, Biljana", year = "2014", abstract = "Certain chemometrical tools allow an efficient way to provide valuable data to evaluate the retention behavior of analytes in liquid chromatography. In this study of the retention behavior of azole antifungals, the experimental design was applied in combination with artificial neural networks (ANNs). Three potentially significant factors (methanol content, pH of the mobile phase and column temperature) were incorporated in the plan of experiments, defined by central composite design. As the system outputs, the retention factors of all six investigated substances (fluconazole, ketoconazole, bifonazole, clotrimazole, econazole and miconazole) were determined. The pattern for the analyzed behavior of the system was created by employing ANNs. The final, optimized topology of the highly predictive network was 3-8-6. Twelve experiments were used in a training set, whereas a back-propagation algorithm was optimal for network training. The ability of the defined network to predict the retention of the investigated azoles was confirmed by correlations higher than 0.9912 for all analytes. The presented approach allowed the adequate prediction of the retention behavior of azoles, in addition to the extraction of important information for a better understanding of the analyzed system.", publisher = "Oxford Univ Press Inc, Cary", journal = "Journal of Chromatographic Science", title = "Chemometrical Tools in the Study of the Retention Behavior of Azole Antifungals", volume = "52", number = "2", pages = "95-102", doi = "10.1093/chromsci/bms211" }
Vemić, A., Malenović, A., Rakić, T., Kostić, N.,& Jančić-Stojanović, B.. (2014). Chemometrical Tools in the Study of the Retention Behavior of Azole Antifungals. in Journal of Chromatographic Science Oxford Univ Press Inc, Cary., 52(2), 95-102. https://doi.org/10.1093/chromsci/bms211
Vemić A, Malenović A, Rakić T, Kostić N, Jančić-Stojanović B. Chemometrical Tools in the Study of the Retention Behavior of Azole Antifungals. in Journal of Chromatographic Science. 2014;52(2):95-102. doi:10.1093/chromsci/bms211 .
Vemić, Ana, Malenović, Anđelija, Rakić, Tijana, Kostić, Nada, Jančić-Stojanović, Biljana, "Chemometrical Tools in the Study of the Retention Behavior of Azole Antifungals" in Journal of Chromatographic Science, 52, no. 2 (2014):95-102, https://doi.org/10.1093/chromsci/bms211 . .