OPTIMALIZATION OF ELSD PARAMETERS FOR HPLC CARBOHYDRATES ANALYSIS WITH AN ARTIFICIAL NEURAL NETWORK

CRHA, Tomáš and Jiří PAZOUREK. OPTIMALIZATION OF ELSD PARAMETERS FOR HPLC CARBOHYDRATES ANALYSIS WITH AN ARTIFICIAL NEURAL NETWORK. In Student Scientific Conference MUNI Pharm, Doctoral Students. 2023. ISBN 978-80-280-0324-1.

Basic information

Original name

OPTIMALIZATION OF ELSD PARAMETERS FOR HPLC CARBOHYDRATES ANALYSIS WITH AN ARTIFICIAL NEURAL NETWORK

Authors

CRHA, Tomáš and Jiří PAZOUREK

Edition

Student Scientific Conference MUNI Pharm, Doctoral Students, 2023

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Other information

Language

English

Type of outcome

Prezentace na konferencích

Country of publisher

Czech Republic

Confidentiality degree

není předmětem státního či obchodního tajemství

Organization unit

Faculty of Pharmacy

ISBN

978-80-280-0324-1

Keywords in English

carbohydrates, HILIC, HPLC, CCM, ELSD optimalization

Tags

ÚChL

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Abstract

V originále

Evaporative light-scattering detector (ELSD) is a simple and inexpensive way to determinate analytes without a suitable chromophore. Three ‘analogue’ parameters for ELSD can be set: nebulization gas flow, temperature of an evaporator and temperature of a nebulizer. For better and faster optimalization of these parameters, a central composite (CCM) response surface design with an artificial neural network (ANN) can be used with advantage. Output of the ANN is a prediction, which gives us probably the best ELSD condition for sugars analysis. Of course, the prediction must be confirmed and verified with HPLC-ELSD measurements.