Abstract

The essential oil of Cymbopogon martinii (Palmarosa) is one of the industrially important essential oil having rose like sweet aroma. Using hydrodistillation technique, essential oil from the leaves of palmarosa was extracted to maximize the yield of oil, yield of geraniol along with zone of inhibition (ZOI) as responses. These responses were influenced by solid loading, volume of water, size of leaves and extraction time. The optimization of process parameters was performed using the Taguchi method and grey relational analysis. The optimized conditions were obtained at, solid loading of 50 g, water volume of 1000 mL, 20 mm size of the leaves and extraction time of 35 min. Under optimized conditions, 1.6832% w/w yield of essential oil, 1.4230% w/w yield of geraniol and 16 mm ZOI were obtained. Artificial neural network was applied for predictive modeling, providing better accuracy (mean square error = 0.0301) in case of 4-12-2 topology and log sigmoid transfer function as hidden layer. Proximate and ultimate analyses were carried out to find the quantitative energy content of the used palmarosa leaves by finding its higher heating value (HHV). HHV of used palmarosa leaves was found to be 17.8741 MJ/kg suggesting the potential of leaves as a renewable source of energy.

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