engleski

Apllication of artificial neural networks (ANN) for development of HPLC gradient separation methods of polyphenolic compounds in medical plants

behaviour of gallic, rosmarinic and chlorogenic acids as well as quercetin and rutin as the outputs from five different medicinal plant extracts. The optimal neural network chosen based on the predicted response factor values, mean of squares error (MSE), average error percentage (Er%), and coefficients of correlation (r) was able to highly accurately predict the experimental responses. The results of the present study confirm the usefulness of ANNs in the development of HPLC gradient separation methods.

artificial neural networks; HPLC; medicinal plants; polyphenols

Ovaj rad doprinos je projektu "Primjena mikroreaktora u praćenju antioksidacijske aktivnosti samoniklog bilja (MICRO-AA)" projektni broj HR.3.2.01-0069, koji je financiran iz Europskog socijalnog fonda (ESF) putem operativnog programa Razvoj ljudskih potencijala.