Comparison of response surface methodology (RSM) and artificial neural networks (ANN) towards efficient extraction of artemisinin from Artemisia annuaTools Pilkington, J.L., Preston, Chris and Gomes, R.L. (2015) Comparison of response surface methodology (RSM) and artificial neural networks (ANN) towards efficient extraction of artemisinin from Artemisia annua. Industrial Crops and Products, 58 . pp. 15-24. ISSN 0926-6690
AbstractThe solid-liquid extraction of Artemisia annua remains an important source of artemisinin, the precursor molecule to the most potent anti-malarial drugs available. Industrial manufacturers of artemisinin face many challenges in regards to volatile markets and sub-optimal extraction approaches. There is a need to improve current processing conditions, and one method is to model the processing options and identify the most appropriate process conditions to suit the market forces. This study examined the impact of extraction temperature, duration and solvent (petroleum ether) to leaf proportions on the recovery of artemisinin from leaf steeped in solvent, in a central composite design (CCD), and the results were used to generate both a response surface methodology (RSM) model and an artificial neural network (ANN) model.
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