Multi-response optimization and neural network modeling for parameter precision in heat reflux extraction of spice oleoresins from two pepper cultivars (Piper nigrum)

Abstract

Black and white peppers are important oil bearing commodity crop in tropical areas. They are highly beneficial in food industries and herbal medicine, due to their amazing aroma and therapeutic activities. In this study, heat reflux technique was employed to extract medicinal oleoresin from the two peppercorns. For this purpose, various extraction parameters were considered viz: extraction time, particle size and feed-solvent ratio. These extraction parameters were employed to optimize the extraction yield and absorbed energy via Taguchi methodology. The established optimal condition values of the yield and absorbed energy from the parametric study were 13.22mg/g and 285.60J/min, respectively in black pepper heat refluxation. Moreover, in white pepper refluxation, the extraction yield and absorbed energy were 14.04mg/g and 264.82J/min, respectively. Artificial neural network (ANN) was used for prediction purposes. This was achieved by comparing two algorithms, transfer functions and neurons. A good training and better prediction of the experimental data were observed using the Levenberg Marquardt (LM) feed forward backpropagation algorithm with log sigmoid transfer function as hidden layer and 3-4-5-1 as model topology. Furthermore, a total of 19 and 25 bioactive compounds were identified in black and white pepper extracts, respectively. The results from the Scanning Electron Spectrometry (SEM) showed a remarkable morphological changes during the heat refluxation process.