3 years ago

Comparison of response surface methodology (RSM) and artificial neural networks (ANN) towards efficient extraction of artemisinin from Artemisia annua

The 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. Appraisal of the models through the coefficient of determination (R 2) and the absolute average deviation (AAD) showed that the ANN was superior (R 2 =0.991, AAD=1.37%) to the RSM model (R 2 =0.903, AAD=4.57%) in predicting artemisinin recovery. The ANN model was subsequently used to determine the optimal extraction conditions for the recovery of artemisinin, which were found to be an extraction duration of 8h at a temperature of 45°C and a leaf loading of 0.12g/ml petroleum ether, from the conditions tested. An illustration is provided in how the results obtained from an ANN model may be used to determine optimal extraction conditions in response to market conditions. In addition, a co-solvency effect has been observed between extracted impurities and petroleum ether that substantially increases the solubility of artemisinin over that in petroleum ether alone, and which will require further investigation in the future. The impact of this co-solvency effect on the efficiency of artemisinin recovery in secondary extraction cycles was found to be significant.

Publisher URL: www.sciencedirect.com/science

DOI: S0926669014001502

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