Optimization of microwave-assisted extraction of plumbagin from Plumbago zeylanica by response surface methodology and adaptive neuro-fuzzy inference system modelling

This investigation aimed to optimize the extraction of plumbagin by the microwave-assisted extraction (MAE) method and optimize the extraction parameters using response surface methodology (RSM) and adaptive neurofuzzzy inference system (ANFIS). A single-factor experiment was conducted to examine the range of the extraction parameters. Box and Behnken's design (BBD) which is a three-level factorial experiment was selected to obtain the best combination of three extraction parameters, namely extraction time, solvent volume, and particle size. The experimental data obtained was analyzed and fitted in the second-order polynomial equation, the r2 value of 0.993 for plumbagin yield was obtained. The model was found significant and all three parameters had a significant effect on the plumbagin yield. The optimal parameters were extraction time of 4 min, the solvent volume of 20 mL, and particle size 0.6 mm. At these optimal conditions, the plumbagin yield was 0.992% which was found to be close to the predicted values of RSM and ANFIS. The results obtained showed that MAE can be a highly efficient method for plumbagin extraction by reducing time and solvent consumption by many folds. There is a rising demand for plumbagin in the pharmaceutical industry; this study will allow a timeefficient and cost-effective extraction process.

Automated summary

This study aimed to optimize the extraction of plumbagin using MAE method and response surface methodology (RSM) and adaptive neurofuzzy inference system (ANFIS). The results showed that MAE can be a highly efficient method for plumbagin extraction.