Red pigment from Ixora coccinea waste flower biomass was extracted using water as a cost-effective solvent for a sustainable source of organic color. The significant effect of selected variables on the method of extraction was studied for the green-extraction techniques, i.e., microwave-assisted extraction (MAE) and ultrasound-assisted extraction (UAE), and conventional technique, i.e., heat-assisted extraction (HAE). Optimization of the pigment extraction was examined using response surface methodology (RSM) for each extraction technique. The parameters considered for MAE were power (W), time (s), pH, and solvent-to-sample ratio (mL/g) while temperature (°C), time (min), pH, and solvent-to-sample ratio (mL/g) were studied for both UAE and HAE. A training set for an artificial neural network (ANN) was created using the same design obtained from RSM. The modeling techniques (ANN and RSM) were then statistically differentiated by the coefficient of determination (R2), root mean square error (RMSE), and mean square error (MSE) which revealed the superiority of the RSM regression design over the ANN model. It was found that the energy consumption of pigment extraction was minimal for MAE. The obtained pigment was further characterized using Fourier transform infrared (FTIR) spectroscopy to reveal functional groups and a gas chromatography-mass spectrometry (GC-MS) to define its bioactive constituents.
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