Optimizing the Extraction of Dietary Fibers from Sorghum Bran Using Response Surface Methodology

Response surface methodology was used to optimize the processing parameters of the fiber extraction from sorghum bran. The studied independent factors were ethanol/bran ratio, time, temperature, and number of treatment cycles. A three-level four-variable Box-Behnken design (BBD) was used to establish the optimum conditions of extraction. The results showed that the experimental data could be fitted to a second-order polynomial equation using multiple regression analysis and the model was highly significant (P<0.0001). The optimum extraction conditions were 11.8 mL·g−1 of ethanol/bran ratio, 60 min, 65°C, and 7 extraction cycles. The experimental yield was 35.52% which is close to the value predicted by the BBD model (34.36%). By applying the two combinations of factors generated by the path of steepest descent, the first combination (12 mL/g, 60 min, 65°C, and 8 cycles) allowed a yield of 35.50%, while the second (11 mL/g, 70 min, 55°C, and 8 cycles) exhibited a yield of 39.90% which is higher than that from the BBD model (P<0.05). Compared to the first combination generated by the path of steepest descent, the BBD model conditions were economical with small number of cycles and low ethanol/bran ratio.

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Optimization of extraction technology of polysaccharide from foxtail millet using response surface methodology

Chemical Industry and Chemical Engineering Quarterly ◽

10.2298/ciceq130926040z ◽

2014 ◽

Vol 20 (4) ◽

pp. 579-585 ◽

Cited By ~ 3

Author(s):

Ai-Shi Zhu ◽

Jin-Na Ye ◽

Fei-Na Yan

Keyword(s):

Response Surface Methodology ◽

Response Surface ◽

Foxtail Millet ◽

Polynomial Equation ◽

Extraction Time ◽

Extraction Temperature ◽

Extraction Technology ◽

Solid Ratio ◽

Experimental Yield ◽

Optimum Extraction

The experiment of extraction of polysaccharide from foxtail millet was investigated. Response Surface Methodology (RSM),based on a threelevel, three variablesBox-Behnken design (BBD), was employed to obtain the best possible combination of liquid-solid ratio(X1: 15.0-25.0 mL?g-1),extraction time (X2: 1.5-2.5h), and extraction temperature (X3: 65.0-75.0?C)for maximum polysaccharide yields. The experimental data obtained were fitted to a second-order polynomial equation using multiple regression analysis and also analyzed by appropriate statistical methods (ANOVA). The optimum extraction conditions were as follows: liquid-solid ratio 20.7mL?g-1, extraction time 2.0h, extraction temperature 72.3?C. Under these conditions, the experimental yield was 8.08mg?g-1, which is well in close agreement with8.02mg?g-1predicted value by the model.

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Response Surface Methodology for Optimization of the Ultrasonic Extraction of Polysaccharides from Melaleuca ahemifolia

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.655-657.1987 ◽

2013 ◽

Vol 655-657 ◽

pp. 1987-1995

Author(s):

Yue Xiao ◽

He Ping Yu ◽

Li Jing Lin

Keyword(s):

Response Surface Methodology ◽

Response Surface ◽

Polynomial Equation ◽

Extraction Yield ◽

Raw Material ◽

Extraction Time ◽

Ultrasonic Power ◽

Ultrasonic Assisted ◽

Experimental Yield ◽

Optimum Extraction

An ultrasonic-assisted procedure for the extraction of polysaccharides from Melaleuca ahemifolia was investigated using response surface methodology (RSM). A Box–Behnken design (BBD) was employed to investigate the effects of extraction time, ratio of water to raw material and ultrasonic power on the extraction yield of polysaccharides.The statistical analysis indicated that three variables and the quadratic terms had significant effects on the yield (p＜0.05). The experimental data obtained were fitted to a second-order polynomial equation using multiple regression analysis and further analyzed by appropriate statistical methods (ANOVA). The optimum extraction conditions were as follows: extraction time of 21 min, ratio of water to raw material of 40, ultrasonic power of 170 W. Under the optimization conditions, the experimental yield of polysaccharides was 6.194%, which was in good agreement with the predicted value (6.288%).

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EXPERIMENTAL INVESTIGATION OF EDM PROCESSING PARAMETERS ON MACHINING Al6063-12% SiC-5% Gr USING RESPONSE SURFACE METHODOLOGY

High Temperature Material Processes (An International Quarterly of High-Technology Plasma Processes) ◽

10.1615/hightempmatproc.2015014659 ◽

2014 ◽

Vol 18 (1-2) ◽

pp. 27-43

Author(s):

G. Pradeep Kumar ◽

Rajasekaran Thanigaivelan ◽

RM. Arunachalam ◽

P. Paramasivam

Keyword(s):

Experimental Investigation ◽

Response Surface Methodology ◽

Response Surface ◽

Processing Parameters

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Optimisation of the Oil Extraction from Nigella sativa Seeds Using Response Surface Methodology

Revista de Chimie ◽

10.37358/rc.17.2.5447 ◽

2017 ◽

Vol 68 (2) ◽

pp. 331-336

Author(s):

Gabriela Isopencu ◽

Mirela Marfa ◽

Iuliana Jipa ◽

Marta Stroescu ◽

Anicuta Stoica Guzun ◽

...

Keyword(s):

Response Surface Methodology ◽

Response Surface ◽

Nigella Sativa ◽

Polynomial Equation ◽

Herbaceous Plant ◽

Box Behnken Design ◽

Pareto Analysis ◽

Black Cumin ◽

Mediterranean Countries ◽

Experimental Values

Nigella sativa, also known as black cumin, an annual herbaceous plant growing especially in Mediterranean countries, has recently gained considerable interest not only for its use as spice and condiment but also for its healthy properties of the fixed and essential oil and its potential as a biofuel. Nigella sativa seeds fixed oil, due to its high content in linoleic acid followed by oleic and palmitic acid, could be beneficial to human health. The objective of this study is to determine the optimum conditions for the solvent extraction of Nigella sativa seeds fixed oil using a three-level, three-factor Box-Behnken design (BBD) under response surface methodology (RSM). The obtained experimental data, fitted by a second-order polynomial equation were analysed by Pareto analysis of variance (ANOVA). From a total of 10 coefficients of the statistical model only 5 are important. The obtained experimental values agreed with the predicted ones.

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Synergistic effects of processing parameters on the biochemical and physical properties of tofu made from yellow field pea ( Pisum sativum ), as determined by response surface methodology

Food Science & Nutrition ◽

10.1002/fsn3.2091 ◽

2021 ◽

Author(s):

Kevin DePalma ◽

Brennan Smith ◽

Armando G. McDonald

Keyword(s):

Response Surface Methodology ◽

Pisum Sativum ◽

Physical Properties ◽

Response Surface ◽

Synergistic Effects ◽

Processing Parameters ◽

Field Pea

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Optimization of processing parameters for microwave drying of selenium-rich slag using incremental improved back-propagation neural network and response surface methodology

Journal of Central South University ◽

10.1007/s11771-011-0859-3 ◽

2011 ◽

Vol 18 (5) ◽

pp. 1441-1447 ◽

Cited By ~ 6

Author(s):

Ying-wei Li ◽

Jin-hui Peng ◽

Gui-an Liang ◽

Wei Li ◽

Shi-min Zhang

Keyword(s):

Neural Network ◽

Response Surface Methodology ◽

Response Surface ◽

Back Propagation ◽

Processing Parameters ◽

Back Propagation Neural Network ◽

Microwave Drying

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Optimization of Manufacturing Process of Traditional Algerian Jben Cheese Using Response Surface Methodology (RSM)

South Asian Journal of Experimental Biology ◽

10.38150/sajeb.8(1).p31-42 ◽

2018 ◽

Vol 8 (1) ◽

pp. 31-42

Author(s):

M. Amimour ◽

T. Idoui ◽

A. Cheriguene

Keyword(s):

Response Surface Methodology ◽

Response Surface ◽

Manufacturing Process ◽

Milk Powder ◽

Statistical Design ◽

Polynomial Equation ◽

Enzymatic Extract ◽

Traditional Cheese ◽

Order Polynomial ◽

Predicted Values

The Aim of this study was to develop an optimized method for manufacturing process of traditional Algerian Jben cheese, using response surface methodology (RSM). In order to develop the objective method of making this traditional cheese, several factors have been studied and a Plackett-Burman statistical design was applied. The effects of the four screened factors (enrichment with milk powder, 10 - 20 g/l; pH of milk, 5.75 - 6.75, enzymatic extract dose, 0.5 - 1.5 ml and coagulation temperature 40 - 60 °C) on the response were investigated, using a Box-Behnken statistical design. Multiple regression analysis was used so that experimental data fits to a second-order polynomial equation. This multiple analysis showed that the model explains about 90.73% of the variation. Based on statistical results, it can be noticed that enrichment with milk powder and pH of milk (Ë‚0.0001***) were highly significant factor influincing cheese yield. The optimal production parame-ters that maximized cheese product (20 g/l enrichment with milk powder, 5.75 pH of milk, 1.29 ml enzymatic extract dose and 60°C coagulation temperature) and the maximal predicted cheese yield (52.68 % ) were found out through response surface methodology. Under these conditions, a verification experiment was carried out and cheese yield was found to be 49.46 %. The overall percentage of agreement for the experimental results (more than 93 % validity) with the predicted values indicates the validation of the statistical model and the success of the optimization process.

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