In this study, microwave-assisted extraction (MAE) of polysaccharides from Opuntia ficus indica C... more In this study, microwave-assisted extraction (MAE) of polysaccharides from Opuntia ficus indica Cladodes were investigated using response surface methodology (RSM). The effects of three extraction factors on the yield of mucilage were examined. The results indicated that the optimum extraction conditions were determined as follows: microwave power X1, 700 W; extraction time X2, 5.15 minand ratio water / raw material X3, 4.83 ml/g at fixed pH 11. Under these optimal extraction conditions, mucilage yield was found to be Y, 25.6%.A comparison between the model results and experimental data gave a high correlation coefficient (R2 = 0.88), adjusted coefficient (Radj = 0.83) and low root mean square error (RMSE = 2.45) and showed that the two models were able to predict a mucilage yield by green extraction microwave process.
Design of experiments (DOE) based on central composite design (CCD) and artificial neural network... more Design of experiments (DOE) based on central composite design (CCD) and artificial neural networks (ANNs) were efficaciously applied for the study of the operating parameters of ultrasound assisted extraction (UAE) in the recovery of phenolic compounds from P. lentiscus leaves. These models were used to evaluate the effects of process variables and their interaction toward the attainment of their optimum conditions. Under the optimal conditions (13.79 min extraction time, 33.82 % amplitude and 30.99 % ethanol proportion), DOE and ANN models predicted a maximum response of 140.55 and 138.3452 mgGAE/gdw, respectively. A mean value of 142.76 ± 19.98 mgGAE/gdw, obtained from real experiments, demonstrated the validation of the extraction models. A comparison between the model results and experimental data gave high correlation coefficients (R 2 ANN = 0.999, R 2 RSM = 0.981), adjusted coefficients (R adjANN = 0.999, R adjRSM = 0.967) and low root mean square errors (RMSE ANN = 0.37 and RMSE RSM = 4.65) and showed that the two models were able to predict a total phenolic compounds (TPC) by green extraction ultrasound process. The results of ANN were found to be more consistent than DOE since better statistical parameters were obtained.
In this study, microwave-assisted extraction (MAE) of polysaccharides from Opuntia ficus indica C... more In this study, microwave-assisted extraction (MAE) of polysaccharides from Opuntia ficus indica Cladodes were investigated using response surface methodology (RSM). The effects of three extraction factors on the yield of mucilage were examined. The results indicated that the optimum extraction conditions were determined as follows: microwave power X1, 700 W; extraction time X2, 5.15 minand ratio water / raw material X3, 4.83 ml/g at fixed pH 11. Under these optimal extraction conditions, mucilage yield was found to be Y, 25.6%.A comparison between the model results and experimental data gave a high correlation coefficient (R2 = 0.88), adjusted coefficient (Radj = 0.83) and low root mean square error (RMSE = 2.45) and showed that the two models were able to predict a mucilage yield by green extraction microwave process.
Design of experiments (DOE) based on central composite design (CCD) and artificial neural network... more Design of experiments (DOE) based on central composite design (CCD) and artificial neural networks (ANNs) were efficaciously applied for the study of the operating parameters of ultrasound assisted extraction (UAE) in the recovery of phenolic compounds from P. lentiscus leaves. These models were used to evaluate the effects of process variables and their interaction toward the attainment of their optimum conditions. Under the optimal conditions (13.79 min extraction time, 33.82 % amplitude and 30.99 % ethanol proportion), DOE and ANN models predicted a maximum response of 140.55 and 138.3452 mgGAE/gdw, respectively. A mean value of 142.76 ± 19.98 mgGAE/gdw, obtained from real experiments, demonstrated the validation of the extraction models. A comparison between the model results and experimental data gave high correlation coefficients (R 2 ANN = 0.999, R 2 RSM = 0.981), adjusted coefficients (R adjANN = 0.999, R adjRSM = 0.967) and low root mean square errors (RMSE ANN = 0.37 and RMSE RSM = 4.65) and showed that the two models were able to predict a total phenolic compounds (TPC) by green extraction ultrasound process. The results of ANN were found to be more consistent than DOE since better statistical parameters were obtained.
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