Preparation of agar nanospheres: Comparison of response surface and artificial neural network modeling by a genetic algorithm approach

Zaki, Mohammad Reza; Varshosaz, Jaleh; Fathi, Milad

doi:10.1016/j.carbpol.2014.12.031

Cited by 34 publications

(17 citation statements)

References 24 publications

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“…A comparison of the performance of various previously reported adsorbents and their maximum adsorption capacities for SO are presented in Table . It is evident that the adsorption capacity for the present adsorbent is satisfactory compared to the other adsorbents.…”

Section: Resultsmentioning

confidence: 79%

“…A chromosome is a set of parameters usually represented as binary bit strings. Different encoding methods like binary encoding, integer or literal permutation encoding, or general data structure encoding are used in GA. Binary encoding is often used because it is simple to create and manipulate the crossover and mutation operators and can be applied over a wide range without much modification . The crossover operator was applied to create a pair of offspring chromosomes.…”

Section: Methodsmentioning

confidence: 99%

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Zinc oxide nanorod‐loaded activated carbon for ultrasound‐assisted adsorption of safranin O: Central composite design and genetic algorithm optimization

Sharifpour

Ghaedi

Azad

et al. 2017

Applied Organom Chemis

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This paper focuses on the development of an effective methodology to obtain the optimum ultrasonic‐assisted removal of a dye, safranin O (SO), under optimum conditions that maximize the removal percentage, using ZnO nanorod‐loaded activated carbon (ZnO‐NRs‐AC) in aqueous solution. Central composite design coupled with genetic algorithm was used for parameter optimization. The effects of variables such as pH, initial dye concentration, mass of ZnO‐NRs‐AC and sonication time were studied. The interactive and main effects of these variables were evaluated using analysis of variance. The structural and physicochemical properties of the ZnO‐NRs‐AC adsorbent were investigated using field emission scanning electron microscopy and X‐ray diffraction. Adsorption equilibrium data were fitted well with the Langmuir isotherm and the maximum monolayer capacity was found to be 32.06 mg g−1. Studies of the adsorption kinetics of the SO dye showed a rapid sorption dynamic with a pseudo‐second‐order kinetic model, suggesting a chemisorption mechanism.

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Section: Resultsmentioning

confidence: 79%

Section: Methodsmentioning

confidence: 99%

Zinc oxide nanorod‐loaded activated carbon for ultrasound‐assisted adsorption of safranin O: Central composite design and genetic algorithm optimization

Sharifpour

Ghaedi

Azad

et al. 2017

Applied Organom Chemis

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“…However, RSM cannot be applied to optimize non-linear system frequently encountered in material research where small variation in composition, processing parameter or experimental parameter can result in large variation in properties and consequently the output dependent on those properties. Since prediction and optimization capability of RSM is based on simple first or second order polynomial equation, it is unable to capture non-linear behavior and can give a poor estimation of drug release from the polymeric material [9]. Artificial Neural Network (ANN) can overcome the limitation of RSM in predicting non-linear system.…”

Section: Introductionmentioning

confidence: 99%

Preparation of Drug Eluting Natural Composite Scaffold Using Response Surface Methodology and Artificial Neural Network Approach

Shera

Sahu

Banik

2018

Tissue Eng Regen Med

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Silk fibroin/xanthan composite was investigated as a suitable biomedical material for controlled drug delivery, and blending ratios of silk fibroin and xanthan were optimized by response surface methodology (RSM) and artificial neural network (ANN) approach. A non-linear ANN model was developed to predict the effect of blending ratios, percentage swelling and porosity of composite material on cumulative percentage release. The efficiency of RSM was assessed against ANN and it was found that ANN is better in optimizing and modeling studies for the fabrication of the composite material. In-vitro release studies of the loaded drug chloramphenicol showed that the optimum composite scaffold was able to minimize burst release of drug and was followed by controlled release for 5 days. Mechanistic study of release revealed that the drug release process is diffusion controlled. Moreover, during tissue engineering application, investigation of release pattern of incorporated bioactive agent is beneficial to predict, control and monitor cellular response of growing tissues. This work also presented a novel insight into usage of various drug release model to predict material properties. Based on the goodness of fit of the model, Korsmeyer-Peppas was found to agree well with experimental drug release profile, which indicated that the fabricated material has swellable nature. The chloramphenicol (CHL) loaded scaffold showed better efficacy against gram positive and gram negative bacteria. CHL loaded SFX55 (50:50) scaffold shows promising biocomposite for drug delivery and tissue engineering applications.

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“…ANN approaches are commonly applied to model and to optimize processes. ANNs have been used in recent years for modeling many processes in food engineering (Chen et al, ; Kodogiannis, Kontogianni, & Lygouras, ; Zaki, Varshosaz, & Fathi, ).…”

Section: Introductionmentioning

confidence: 99%

“…ANNs have been used in recent years for modeling many processes in food engineering (Chen et al, 2015;Kodogiannis, Kontogianni, & Lygouras, 2014;Zaki, Varshosaz, & Fathi, 2015).…”

mentioning

confidence: 99%

An artificial neural network approach to estimating the enzymatic hydrolysis of Chinese yam (Dioscorea opposita Thunb.) starch

Ding

Yang

2017

Journal of Food Processing and Preservation

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Reasonable hydrolysis of starches could be used to partly replace fat in food. The Chinese yam starch hydrolysates were investigated based on DE value and inverted microscope. Photomicrographs clearly showed that the sizes of starch hydrolysate granules had been reduced effectively. A three‐layer back‐propagation neural network (BPNN) network was designed based on the results of orthogonal design experiment with a L18(35) array, and the BPNN network architecture contained 5, 4, 1 neuron in the input layer, the hidden layer, and the output layer, respectively. The hydrolysis conditions and DE values were considered as input and output variables, respectively, and they were utilized to train the BPNN network. The network configuration could be used to predict the DE values of starch hydrolysates with correlation coefficient of 0.9550. Moreover, variable importance analysis indicated that the descending sequence of the five variables was: enzyme concentration, reaction temperature, pH, starch concentration, and reaction time. Practical applications Excessive intake of fats would cause some health problems. It is very necessary to reduce the amount of fat in consumers’ diet and to produce the low‐fat food. Starch hydrolysates are considered to be a kind of potential fat replacer. It can provide fewer calories. However, starch hydrolysis is highly nonlinear because of the complexity and variability of the processing conditions. Traditional experiment data analysis methods, such as the “pick the winner” approach, are impotent to find optima. In the current study, Chinese yam starch hydrolysates were obtained by enzymatic hydrolysis. The relationship between hydrolysis variables and DE values of starch hydrolysates was described by artificial neural network model. The model could be used to guide the production of the starch‐based fat replacer. Furthermore, Chinese yam could be adequately developed as a good source of starch in East Asia, and its added value was also enhanced.

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Preparation of agar nanospheres: Comparison of response surface and artificial neural network modeling by a genetic algorithm approach

Cited by 34 publications

References 24 publications

Zinc oxide nanorod‐loaded activated carbon for ultrasound‐assisted adsorption of safranin O: Central composite design and genetic algorithm optimization

Zinc oxide nanorod‐loaded activated carbon for ultrasound‐assisted adsorption of safranin O: Central composite design and genetic algorithm optimization

Preparation of Drug Eluting Natural Composite Scaffold Using Response Surface Methodology and Artificial Neural Network Approach

An artificial neural network approach to estimating the enzymatic hydrolysis of Chinese yam (Dioscorea opposita Thunb.) starch

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