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.
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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.
Other information
Original language English
Type of outcome Presentations at conferences
Country of publisher Czech Republic
Confidentiality degree is not subject to a state or trade secret
Organization unit Faculty of Pharmacy
ISBN 978-80-280-0324-1
Keywords in English carbohydrates, HILIC, HPLC, CCM, ELSD optimalization
Tags ÚChL
Changed by Changed by: Mgr. Daniela Černá, učo 489184. Changed: 29/2/2024 09:47.
Abstract
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.
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