scholarly journals Enhancement of GABA content in Hongqu wine by optimisation of fermentation conditions using response surface methodology

2021 ◽  
Author(s):  
Cuina Song ◽  
Liping Zhu ◽  
Yanchun Shao ◽  
Fusheng Chen
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Aminobutyric Acid ◽  
Optimal Conditions ◽  
Sodium Glutamate ◽  
Inhibitory Neurotransmitter ◽  
Gaba Content ◽  
Fermentation Parameters ◽  
Rsm Model ◽  
Boiled Water

γ-aminobutyric acid (GABA) is an important inhibitory neurotransmitter in the human body, but its content decreases with age. So it is suitable to supplement the body's GABA from diet. Hongqu wine is popular because of the addition of Monascus strains in the saccharification process, which makes the wine rich in functional ingredients such as GABA, and monacolin K. In this study, the fermentation parameters of Hongqu wine were optimised to maximise the GABA content through response surface methodology (RSM). The optimal conditions were as follows: 500 g of steamed rice was mixed with 115.4% of boiled water containing 10 g of sodium glutamate and adjusted to pH 3.8 with lactic acid, and then 32% of Hongqu seed inoculum was added. After 5 days of fermentation at 28 °C, 1.5 g of activated yeast was inoculated for ethanol fermentation at 30 °C for 5 days. Finally, the average content of GABA in Hongqu wine amounted to 710.24 mg L<sup>–1</sup>, which is close to the value predicted by RSM model (692.44 mg L<sup>–1</sup>), indicating the statistical fit is good. This provided technical support and theoretical guidance for the production of Hongqu wine rich in GABA by two-stage fermentation.

scholarly journals Modeling and Sensitivity Analysis of the Forward Osmosis Process to Predict Membrane Flux Using a Novel Combination of Neural Network and Response Surface Methodology Techniques

Membranes ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 70
Author(s):  
Jasir Jawad ◽  
Alaa H. Hawari ◽  
Syed Javaid Zaidi
Keyword(s):  
Response Surface Methodology ◽  
Experimental Design ◽  
Response Surface ◽  
Forward Osmosis ◽  
Feed Solution ◽  
Operating Conditions ◽  
Ann Model ◽  
Membrane Flux ◽  
Input Variables ◽  
Rsm Model

The forward osmosis (FO) process is an emerging technology that has been considered as an alternative to desalination due to its low energy consumption and less severe reversible fouling. Artificial neural networks (ANNs) and response surface methodology (RSM) have become popular for the modeling and optimization of membrane processes. RSM requires the data on a specific experimental design whereas ANN does not. In this work, a combined ANN-RSM approach is presented to predict and optimize the membrane flux for the FO process. The ANN model, developed based on an experimental study, is used to predict the membrane flux for the experimental design in order to create the RSM model for optimization. A Box–Behnken design (BBD) is used to develop a response surface design where the ANN model evaluates the responses. The input variables were osmotic pressure difference, feed solution (FS) velocity, draw solution (DS) velocity, FS temperature, and DS temperature. The R2 obtained for the developed ANN and RSM model are 0.98036 and 0.9408, respectively. The weights of the ANN model and the response surface plots were used to optimize and study the influence of the operating conditions on the membrane flux.

scholarly journals Efficient Removal of Levofloxacin by Activated Persulfate with Magnetic CuFe2O4/MMT-k10 Nanocomposite: Characterization, Response Surface Methodology, and Degradation Mechanism

Water ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 3583
Author(s):  
Junying Yang ◽  
Minye Huang ◽  
Shengsen Wang ◽  
Xiaoyun Mao ◽  
Yueming Hu ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Photoelectron Spectroscopy ◽  
Degradation Mechanism ◽  
Sol Gel ◽  
Combustion Method ◽  
Copper Ferrite ◽  
X Ray ◽  
Rsm Model

In this study, a magnetic copper ferrite/montmorillonite-k10 nanocomposite (CuFe2O4/MMT-k10) was successfully fabricated by a simple sol-gel combustion method and was characterised by X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), the Brunner–Emmett–Teller (BET) method, vibrating sample magnetometer (VSM), and X-ray photoelectron spectroscopy (XPS). For levofloxacin (LVF) degradation, CuFe2O4/MMT-k10 was utilized to activate persulfate (PS). Due to the relative high adsorption capacity of CuFe2O4/MMT-k10, the adsorption feature was considered an enhancement of LVF degradation. In addition, the response surface methodology (RSM) model was established with the parameters of pH, temperature, PS dosage, and CuFe2O4/MMT-k10 dosage as the independent variables to obtain the optimal response for LVF degradation. In cycle experiments, we identified the good stability and reusability of CuFe2O4/MMT-k10. We proposed a potential mechanism of CuFe2O4/MMT-k10 activating PS through free radical quenching tests and XPS analysis. These results reveal that CuFe2O4/MMT-k10 nanocomposite could activate the persulfate, which is an efficient technique for LVF degradation in water.

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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