Using the response surface methodology to establish the optimal conditions for preserving bananas (Musa acuminata) in a pulsed electric field and to decrease browning induced by storage at a low temperature

2022 ◽  
Vol 31 ◽  
pp. 100804
Author(s):  
Bo-Kuen Chen ◽  
Chao-Kai Chang ◽  
Kuan-Chen Cheng ◽  
Chih-Yao Hou ◽  
Jer-An Lin ◽  
...  
Keyword(s):  
Response Surface Methodology ◽  
Low Temperature ◽  
Electric Field ◽  
Response Surface ◽  
Pulsed Electric Field ◽  
Musa Acuminata ◽  
Optimal Conditions

scholarly journals Effect of pH and pulsed electric field process parameters on the aflatoxin reduction in model system using response surface methodology

2018 ◽  
Vol 55 (3) ◽  
pp. 868-878 ◽  
Author(s):  
Subramanian Vijayalakshmi ◽  
Shanmugam Nadanasabhapathi ◽  
Ranganathan Kumar ◽  
S. Sunny Kumar
Keyword(s):  
Response Surface Methodology ◽  
Electric Field ◽  
Response Surface ◽  
Process Parameters ◽  
Pulsed Electric Field ◽  
Model System ◽  
Effect Of Ph

Optimization of Washing Conditions with Lipase by Response Surface Methodology

2012 ◽  
Vol 441 ◽  
pp. 625-630
Author(s):  
Chen Ying Xu ◽  
Jiang Ren ◽  
Jian Zhong Shao
Keyword(s):  
Response Surface Methodology ◽  
Low Temperature ◽  
Response Surface ◽  
Orthogonal Design ◽  
Cotton Fabrics ◽  
Optimal Conditions ◽  
Critical Factors ◽  
Detergent Concentration ◽  
Washing Process ◽  
Wetting Time

Response surface methodology (RSM) was employed to optimize the washing conditions. A four-factorial three-level Box-Behnken partial orthogonal design was chosen to explain the washing process based on four critical factors, detergent concentration, lipase concentration, washing temperature and buffer pH. According to the responses from the experimental model, the effects of each variable were assessed and the interactions between variables were also determined. The experiment suggested that the optimal conditions for removal of olive oil from the cotton fabrics were 0.60g/L detergent, 4.37g/L lipase, washing temperature of 25.06 and buffer pH of 7.29. By actual washing confirmation, the wetting time of the washed fabric under the optimal condition was 0.51s. Home laundry could be conducted at low temperature with addition of lipase.

Better lime purification of sugar beet juice obtained by low temperature aqueous extraction assisted by pulsed electric field

LWT ◽  
2012 ◽  
Vol 46 (1) ◽  
pp. 371-374 ◽  
Author(s):  
K. Loginova ◽  
M. Loginov ◽  
E. Vorobiev ◽  
N.I. Lebovka
Keyword(s):  
Low Temperature ◽  
Sugar Beet ◽  
Electric Field ◽  
Pulsed Electric Field ◽  
Aqueous Extraction

Optimization of the Ultrasonic Extraction of Gypenoside III from Gynostemma pentaphyllum by Response Surface Methodology

2012 ◽  
Vol 550-553 ◽  
pp. 1866-1870
Author(s):  
Xiao Dan Tang ◽  
Hai Yang Hang ◽  
Shao Yan Wang ◽  
Jing Xiang Cong
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Extraction Yield ◽  
Raw Material ◽  
Extraction Temperature ◽  
Optimal Conditions ◽  
Gynostemma Pentaphyllum ◽  
Bioactive Component ◽  
Yield Value ◽  
Ultrasonic Time

Gypenosides III is a major bioactive component which is rich in Gynostemma pentaphyllum. For better utilization of the native resource, response surface methodology was used to optimize the extraction conditions of gypenosides III from G. pentaphyllum. The effects of three independent variables on the extraction yield of gypenosides III were investigated and the optimal conditions were evaluated by means of Box-Behnken design. The optimal conditions are as follows: ratio of ethanol to raw material 25, extraction temperature 58°C and ultrasonic time 25min. Under these conditions, the yield of gypenoside III is 1.216±0.05%, which is agreed closely with the predicted yield value.

scholarly journals Response surface methodology-artificial neural network based optimization and strain improvement of cellulase production by Streptomyces sp.

2020 ◽  
Vol 36 (4) ◽  
Author(s):  
Ega Soujanya Lakshmi ◽  
Manda Rama Narasinga Rao ◽  
Muddada Sudhamani
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Wild Strain ◽  
Cellulase Activity ◽  
Cellulase Production ◽  
Optimal Conditions ◽  
Zone Formation ◽  
Artificial Neural

ABSTRACT Thirty seven different colonies were isolated from decomposing logs of textile industries. From among these, a thermotolerant, grampositive, filamentous soil bacteria Streptomyces durhamensis vs15 was selected and screened for cellulase production. The strain showed clear zone formation on CMC agar plate after Gram’s iodine staining.  Streptomyces durhamensis vs15 was further confirmed for cellulase production by estimating the reducing sugars through dinitrosalicylic acid (DNS) method. The activity was enhanced by sequential mutagenesis using three mutagens of ultraviolet irradiation (UV), N methyl-N’-nitro-N-nitrosoguanidine (NTG) and Ethyl methane sulphonate (EMS). After mutagenesis, the cellulase activity of GC23 (mutant) was improved to 1.86 fold compared to the wild strain (vs15). Optimal conditions for the production of cellulase by the GC 23 strain were evaluated using Response Surface Methodology (RSM) and Artificial Neural Network (ANN). Effect of pH, temperature, duration of incubation, , and substrate concentration on cellulase production were evaluated. Optimal conditions for the production of cellulase enzyme using Carboxy Methyl Cellulase as a substrate are 55 oC of temperature, pH of 5.0 and incubation for 40 h. The cellulase activity of the mutant Streptomyces durhamensis GC23 was further optimised to 2 fold of the activity of the wild type by RSM and ANN.  

scholarly journals Applying Response Surface Methodology to optimize the decarbonization process of Timahdit oil shale

2019 ◽  
Vol 8 (5) ◽  
pp. 372-379 ◽  
Author(s):  
Laila Makouki ◽  
Meriem Tarbaoui ◽  
Samia Glissi ◽  
Said Mansouri ◽  
Hassan Hannache ◽  
...  
Keyword(s):  
Response Surface Methodology ◽  
Acetic Acid ◽  
Response Surface ◽  
Oil Shale ◽  
Processing Time ◽  
Texture Evolution ◽  
Optimal Conditions ◽  
X Ray ◽  
Decarbonization Process ◽  
Scanning Electron

The present article aims to optimize the decarbonization of Timahdit oil shale layer Y by removing carbonates from the raw rock using acetic acid. The response surface methodology “central composite design (CCD)” has been used as a method of optimization to study the 3 factors of the process. The factors studied are the concentration of the acid, the processing time, and the ratio (liquid/solid). The optimal conditions with 68.17% of residue rate are obtained with 2 mol/l as concentration, 120 min as a time of treatment and 10.02 for the ratio.The raw (Y) and optimized materials (YO) were characterized by Scanning Electron Microscopy (SEM), X-ray fluorescence (XRF) and X-ray diffraction (XRD). The results showed that the acetic acid used to remove carbonates affects the chemical composition and the texture evolution of the residues.

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