Quantitative structure/eluent–retention relationships in reversed-phase high-performance liquid chromatography based on the solvatochromic method View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2013-01

AUTHORS

Angelo Antonio D’Archivio, Maria Anna Maggi, Fabrizio Ruggieri

ABSTRACT

Some predictive approaches aimed at modelling the combined effect of solute molecular structure and mobile phase composition on retention in reversed-phase high-performance chromatography (RP-HPLC) have been developed in the literature. These models are established for a given binary eluent (normally acetonitrile-water or methanol-water) by non-linear (curvilinear or artificial neural network) regression assuming as the mobile phase descriptor the volume fraction φ of the organic modifier. In the present investigation, we propose a model applicable simultaneously to acetonitrile-water and methanol-water eluents. To this end, the Kamlet-Taft solvatochromic descriptors of the eluent and the solvatochromic descriptors of the analytes are considered as the input variables of a multi-layer artificial neural network (ANN) providing the solute retention as the response. This approach is applied to a set of 31 molecules analyzed with five different columns in the φ range 20-70 % at 10 % steps for both acetonitrile- and methanol-containing mobile phases. For each column, an ANN-based model is built using retention data of 25 molecules selected by the Kennard-Stones algorithm while retention data of the unselected six solutes are considered in the final evaluation of predictive performance of the trained network. To test cross-eluent prediction, the network optimized for a given column was successively trained with data collected in eight out of 12 eluents and applied to deduce retention in the four remaining mobile phases. The results reveal that RP-HPLC behavior of external solutes is quite accurately modelled in the whole explored composition range of acetonitrile- and methanol-water mobile phases. Moreover, the model exhibits a promising capability of deducing retention of external solutes even in unknown eluents. More... »

PAGES

755-766

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00216-012-6191-4

DOI

http://dx.doi.org/10.1007/s00216-012-6191-4

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1049119923

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/22760503


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