首页|Response surface methodology and artificial neural network modeling for optimization of ultrasound-assisted extraction and rapid HPTLC analysis of asiaticoside from Centella asiatica
Response surface methodology and artificial neural network modeling for optimization of ultrasound-assisted extraction and rapid HPTLC analysis of asiaticoside from Centella asiatica
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NSTL
Elsevier
The present study optimizes various extraction conditions for better yield of asiaticoside in Centellaasiatica. Response surface methodology (RSM) and artificial neural network (ANN) were used for the first time here in order to model and optimize the ultrasonic extraction parameters of asiaticoside from C.asiaticaleaves for comparing and establishment of effective prediction models.The quantitative determination of asiaticoside was carried out on silica gel 60 F254HPTLC plates by using the mobile phase consisting of butanol: ethyl acetate: water (4:1:5). The optimum sonication parameters solid:solvent ratio (1:15), sonication time (18 min), solvent composition (35% aqueous-ethanol), the experimental maximum yield obtained for asiaticoside were 0.198% and the maximum predicted yield were found to be 0.201% i.e closely related to the experimental yield. The results showed that RBF gives better performance as compared to MLP and RSM. The study suggests that RSM and ANN model system can be manipulated for the optimization and production of valuable bioactive compounds.
NSTL
Elsevier
