Microwave assisted extraction of essential oil from the leaves of Palmarosa: Multi-response optimization and predictive modelling

Abstract

Cymbopogon martinii (Palmarosa), an essential oil bearing industrial grass of India, is highly valued by cosmetics and perfumery industries for its rose like sweet odor from its inflorescences and leaves. Using microwave radiation, essential oil from the leaves of palmarosa was extracted for maximization of yield of oil, yield of geraniol and zone of inhibition (ZOI) as responses. For this purpose, various process parameters viz. solid loading, water volume, microwave power and extraction time were studied in detail and optimized using the Taguchi method and grey relational analysis. The optimized extraction conditions were obtained at, solid loading of 35g, water volume of 300mL, microwave power of 850W and extraction time of 20min. Under optimized conditions, 2.4400% (w/w) yield of essential oil, 2.1700% (w/w) yield of geraniol and 20mm ZOI were obtained. Artificial neural network (ANN) was used for the prediction of the results by studying different algorithms, transfer functions and numbers of neurons. A better prediction (overall R2=0.9997; mean squared error=0.0117) of the experimental data was observed using feed forward back propagation algorithm, log sigmoid transfer function as hidden layer and 4-7-3 topology.