Publication details
OPTIMALIZATION OF ELSD PARAMETERS FOR HPLC CARBOHYDRATES ANALYSIS WITH AN ARTIFICIAL NEURAL NETWORK
| Authors | |
|---|---|
| Year of publication | 2023 |
| Type | Appeared in Conference without Proceedings |
| MU Faculty or unit | |
| Citation | |
| Description | 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. |