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@proceedings{2348549, author = {Crha, Tomáš and Pazourek, Jiří}, booktitle = {Student Scientific Conference MUNI Pharm, Doctoral Students}, keywords = {carbohydrates, HILIC, HPLC, CCM, ELSD optimalization}, language = {eng}, isbn = {978-80-280-0324-1}, title = {OPTIMALIZATION OF ELSD PARAMETERS FOR HPLC CARBOHYDRATES ANALYSIS WITH AN ARTIFICIAL NEURAL NETWORK}, year = {2023} }
TY - CONF ID - 2348549 AU - Crha, Tomáš - Pazourek, Jiří PY - 2023 TI - OPTIMALIZATION OF ELSD PARAMETERS FOR HPLC CARBOHYDRATES ANALYSIS WITH AN ARTIFICIAL NEURAL NETWORK SN - 9788028003241 KW - carbohydrates, HILIC, HPLC, CCM, ELSD optimalization N2 - 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. ER -
CRHA, Tomáš a Jiří PAZOUREK. OPTIMALIZATION OF ELSD PARAMETERS FOR HPLC CARBOHYDRATES ANALYSIS WITH AN ARTIFICIAL NEURAL NETWORK. In \textit{Student Scientific Conference MUNI Pharm, Doctoral Students}. 2023. ISBN~978-80-280-0324-1.
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