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Bibliographic record number: 917837


Authors: Belščak-Cvitanović, Ana; Jurinjak Tušek, Ana; Valinger, Davor; Benković, Maja; Jurina, Tamara; Komes, Draženka
Title: Application of artificial neural networks (ANNs) for development of HPLC gradient separation methods of polyphenolic compounds in medicinal plants
Source: Proceedings of the 16th Ružička days, "Today science-tomorrow industry" / Jukić, A ; Šubarić, D. (ed). - Zagreb : Hrvatsko društvo kemijskih inženjera i tehnologa (HDKI) , 2017. 198-208.
ISSN: 2459-9387
Meeting: 16th Ružička days, "Today science-tomorrow industry"
Location and date: Vukovar, 21-23.09.2016.
Keywords: artificial neural networks (ANNs) ; HPLC ; medicinal plants ; polyphenols
Extensive research activities have been undertaken recently to systematize the identification, standardization and use of medicinal plants. For that purpose, high performance liquid chromatography (HPLC) methods are imperative for the regular quality control and identification of pharmacologically active compounds. Enormous research efforts have been conducted so far resulting in a vast number of HPLC methods developed for identifying polyphenolic constituents of medicinal plants. In the present study, the approach of using artificial neural networks (ANNs) for prediction of optimal HPLC method for the separation of different groups of polyphenolics was applied. For that purpose, the composition of mobile phase, pH, analysis duration and flow rate as the input parameters were investigated on separation 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 values of the root mean square error (RMSE) and the linear correlation coefficient (R2) was able to accurately predict the experimental responses. The results of the present study confirm the usefulness of ANNs in the development of HPLC gradient separation methods.
Type of meeting: Poster
Type of presentation in a journal: Full-text (1500 words and more)
Type of peer-review: International peer-review
Project / theme: HR.3.2.01-0069
Original language: ENG
Category: Znanstveni
Research fields:
Contrib. to CROSBI by: Maja Benkovic (, 11. Sij. 2018. u 09:50 sati

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