Statistical optimization and characterization of Pectin extracted from Orange peel using response surface methodology and artificial neural network

Abstract

The objective of study was extraction of pectin from orange peel using ultrasound assisted extraction and response surface method and artificial neural network technique. The following findings are absorbed from the effects of extraction parameters. The pH solution was highly significant compared to ultrasound power. As well as interactions between ultrasound and pH found to be strongly influenced the extraction yield of pectin. The optimal parameters for extraction were irradiation time of 22.5min, pH of 1.5, and ultrasound power of 155W and liquid-solid ratio 22.5:1 mL/ g. Under these conditions, yield of pectin was 26.87% experimentally, while 26.74 and 26.93% of yield have predicted by response surface and artificial neural network model respectively. The extracted pectin has categorized as high methoxyl pectin, since it has 63.13% degree of esterification, which is above 50% affirmed by Fourier transform infrared spectroscopy detection. Both response surface methodology and artificial neural network model prediction was in good agreement with experimental data; however, the prediction of artificial neural network prediction was better than artificial neural network. Therefore, artificial neural network model is much more accurate in estimating the values of pectin yield and mean square error when compared with the response surface methodology.

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