In this paper, the selectivity and resolution of enantiomeric separation by capillary liquid chromatography (cLC) of racemates of phenoxy acid herbicides are modelled. The compounds studied were 2-(±)-(2,4,5-trichlorophenoxy)propanoic acid (2,4,5-TP), 2-(±)-(2,4- dichlorophenoxy)propanoic acid (2,4-DP),2-(±)-(4-chloro-2-methyl)phenoxypropanoic acid (MCPP) and 2-(±)-[4-(2,4-dichlorophenoxy)phenoxy]propanoic acid] (diclofop acid), using a capillary column packed with silica particles modified with teicoplanin as chiral selector. Several mixtures of methanol (MeOH), water and triethylamine acetate (TEAA) buffer at different pHs have been tested as mobile phases, and experimental parameters such as resolution (Rs), retention factor (k) and enantioselectivity factor (a) have been measured in all tested conditions. The chemometric methods used to explore and to model the data were principal component analysis (PCA), stepwise multiple linear regression (stepwise-MLR) and response surface analysis (RSA). The results show that it is possible to quantitatively predict the quality of enantiomeric separations of related compounds in a given chromatographic system.
Multivariate optimization approach for chiral resolution of chlorophenoxy acid herbicides using teicoplanin as chiral selector in capillary LC
Giovanni D'Orazio;Salvatore Fanali
2008
Abstract
In this paper, the selectivity and resolution of enantiomeric separation by capillary liquid chromatography (cLC) of racemates of phenoxy acid herbicides are modelled. The compounds studied were 2-(±)-(2,4,5-trichlorophenoxy)propanoic acid (2,4,5-TP), 2-(±)-(2,4- dichlorophenoxy)propanoic acid (2,4-DP),2-(±)-(4-chloro-2-methyl)phenoxypropanoic acid (MCPP) and 2-(±)-[4-(2,4-dichlorophenoxy)phenoxy]propanoic acid] (diclofop acid), using a capillary column packed with silica particles modified with teicoplanin as chiral selector. Several mixtures of methanol (MeOH), water and triethylamine acetate (TEAA) buffer at different pHs have been tested as mobile phases, and experimental parameters such as resolution (Rs), retention factor (k) and enantioselectivity factor (a) have been measured in all tested conditions. The chemometric methods used to explore and to model the data were principal component analysis (PCA), stepwise multiple linear regression (stepwise-MLR) and response surface analysis (RSA). The results show that it is possible to quantitatively predict the quality of enantiomeric separations of related compounds in a given chromatographic system.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.