Informace o publikaci
OPTIMALIZATION OF ELSD PARAMETERS FOR HPLC CARBOHYDRATES ANALYSIS WITH AN ARTIFICIAL NEURAL NETWORK
| Autoři | |
|---|---|
| Rok publikování | 2023 |
| Druh | Další prezentace na konferencích |
| Fakulta / Pracoviště MU | |
| Citace | |
| Popis | 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. |