Statistical optimization of the phytoremediation of arsenic by <i>Ludwigia octovalvis-</i> in a pilot reed bed using response surface methodology (RSM) versus an artificial neural network (ANN)

Titah, Harmin Sulistiyaning; Halmi, Mohd Izuan Effendi; Abdullah, Siti Rozaimah Sheikh; Hasan, Hassimi Abu; Idris, Muhamad; Anuar, Nurina

doi:10.1080/15226514.2017.1413337

Cited by 43 publications

(15 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The interaction effect of temperature and substrate concentration on sugar yield was represented in Figure 6 calculated by ANN. In most of the cases in optimization of bioprocesses ANN performed better than the RSM (Zafar et al, 2012;Nelofer et al, 2012;Siddique et al, 2014;Titah et al, 2018). In the present studies ANN gave the prediction of optimal levels close to the actual with 85.5% saccharification of wheat straw.…”

Section: Figuresupporting

confidence: 61%

Conversion of Wheat Straw Into Fermentable Sugars Using Carboxymethyl Cellulase From Trichoderma Viride Through Box-Behnken Design and Artificial Neural Network

Nelofer¹,

Nadeem²,

Irfan³

et al. 2021

J microb biotech food sci

View full text Add to dashboard Cite

Cellulase production was carried out in submerged fermentation using medium composition of (g/L); pretreated wheat straw 50, (NH4)2SO4 10, CaCl2 0.5, MgSO4.7H2O 0.5 and KH2PO4 4.0 and pH was maintained at 5.0 with 1N In this study, carboxymethyl cellulase was produced in submerged fermentation characterized and saccharification was optimized through Box-Behnken design. The optima pH and temperature of enzyme produced by Trichoderma viride were 5 and 50 oC, respectively. The crude enzyme had Km and Vmax values of 1.5143 µM and 0.9253µM/min, respectively, using carboxymethyl cellulose as substrate respectively. Three variables including pH (X1), incubation temperature (X2) and substrate concentration (X3) with three levels were used to optimize saccharification of wheat straw having 83% cellulose content using Box Behnken design and Artificial Neural Network (ANN). Results reveal that the proposed model was significant and quadratic effect of these parameters significantly affects the sugar production. Maximum sugar production (28.87 mg/ml) was predicted at RSM predicted levels of pH (6.6), incubation temperature (50 ºC) and (6.2%) substrate concentration, while the levels predicted for pH, temperature and substrate concentration were 5, 50 o C and 3.5 %, respectively, by ANN. The predicted sugar concentration at these levels was 30.72 mg/mL. The observed values at the predicted levels of RSM and ANN were 25.52 and 29.95 mg/mL respectively. ARTICLE INFO

show abstract

Section: Figuresupporting

confidence: 61%

Conversion of Wheat Straw Into Fermentable Sugars Using Carboxymethyl Cellulase From Trichoderma Viride Through Box-Behnken Design and Artificial Neural Network

Nelofer¹,

Nadeem²,

Irfan³

et al. 2021

J microb biotech food sci

View full text Add to dashboard Cite

show abstract

“…While the greater the amplitude modulation value will lead to increase of current response generated because the amplitude modulation value is directly proportional to the current response (19). only 4.1%, which is close to an acceptable value for an RSM model (20).…”

Section: Optimization Of Factors For Dy Measurementsupporting

confidence: 50%

Optimization of Voltammetric Determination of Dysprosium (III) Using Plackett-Burman and RSM-CCD Experimental Designs

Wyantuti¹,

Setyorini

Ishmayana

et al. 2020

Baghdad Sci.J

View full text Add to dashboard Cite

This study was aimed to develop an optimized Dy determination method using differential pulse voltammetry (DPV). The Plackett-Burman (PB) experimental design was used to select significant factors that affect the electrical current response, which were further optimized using the response surface method-central composite design (RSM-CCD). The type of electrolyte solution and amplitude modulation were found as two most significant factors, among the nine factors tested, which enhance the current response based on PB design. Further optimization using RSM-CCD shows that the optimum values for the two factors were 0.1046 M and 0.1082 V respectively. When the optimum conditions were applied for Dy determination good recovery and precision were achieved with values of 91.58%, and 99.80%, respectively. The detection limit and quantification limit of the method were 1.4322 mg/L and of 4.7741 mg/L, respectively.

show abstract

“…The topology of the ANN was based on the number of hidden neurons chosen. The optimum number of hidden neurons was selected by trial‐and‐error procedure, the software autosearching for the best network by varying number of hidden neurons, and the nodes (neurons) within each layer (Titah et al, ). Different numbers of neurons were tested iteratively by adjusting the strength of connections between neurons with the aim to adapt the output of the entire network to be closer to the desired outputs or until minimum value of sum of squares error was identified.…”

Section: Methodsmentioning

confidence: 99%

“…The topology of the ANN was based on the number of hidden neurons chosen. The optimum number of hidden neurons was selected by trial-and-error procedure, the software autosearching for the best network by varying number of hidden neurons, and the nodes (neurons) within each layer (Titah et al, 2018). Different numbers of neu-…”

Section: Modeling For Artificial Neural Networkmentioning

confidence: 99%

Dough bread from refined wheat flour partially replaced by grape peels: Optimizing the rheological properties

Mironeasa

2019

J Food Process Engineering

View full text Add to dashboard Cite

The valorization of grape peels by‐product to supplement wheat bread has immense potential, particularly in refined wheat flour (RWF) to increase the dietary fibers. The main aims of this study were successful modeling of dough rheological properties using predictive models, investigating the effect of factors, grape peels flour (GPF) variety, level and particle size (PS) of GPF added in RWF on responses, and finally, optimizing the formulation with respect to dough rheological behavior. The rheological properties of grape peels–wheat composite flour formulated with GPF from two variety and increasing levels of GPF (0–9%) in the presence of different PS (large, L > 500 μm; medium, 200 μm > M < 500 μm, and small, S < 200 μm) of GPF was evaluated using farinograph, alveograph, and dynamic oscillation measurements. The artificial neural network models (R2 > .82) developed to predict dough rheological properties highlighted an improved estimation and predictive capabilities compared with response surface methodology models (R2 > .77). The multiobjective optimization approach allowed anticipation of the optimal value for each response in terms of dough rheological properties as function of GPF from white variety of small PS, which replaced RWF at level of 3.81%. Practical applications In the composite flour formulation to produce effectiveness and nutritive bread, optimizing dough rheology in relation to the formulation factors represents the actual need for the bread‐making industry. Dough behavior during mixing (water absorption, dough stability, dough development time, and degree of softening), alveograph parameters (dough tenacity, extensibility, and deformation energy), and dynamic rheological properties (elastic modulus, viscous modulus, loss tangent, and complex viscosity) are considered essential to optimization of grape peels–wheat composite flour formulation. The current study revealed the efficacy of modeling and optimization between response surface and artificial neural network techniques for the grape peels–wheat flour dough rheological properties. The information given from this study would help the industry to develop new bread‐making products with the desired particle size with optimum functional and nutritional properties.

show abstract

Statistical optimization of the phytoremediation of arsenic by Ludwigia octovalvis- in a pilot reed bed using response surface methodology (RSM) versus an artificial neural network (ANN)

Cited by 43 publications

References 37 publications

Conversion of Wheat Straw Into Fermentable Sugars Using Carboxymethyl Cellulase From Trichoderma Viride Through Box-Behnken Design and Artificial Neural Network

Conversion of Wheat Straw Into Fermentable Sugars Using Carboxymethyl Cellulase From Trichoderma Viride Through Box-Behnken Design and Artificial Neural Network

Optimization of Voltammetric Determination of Dysprosium (III) Using Plackett-Burman and RSM-CCD Experimental Designs

Dough bread from refined wheat flour partially replaced by grape peels: Optimizing the rheological properties

Contact Info

Product

Resources

About