Optimization of Portulaca oleracea L. extract using response surface methodology and artificial neural network and characterization of bioactive compound by high-resolution mass spectroscopy

The well-known medicinal plant Portulaca oleracea L. (PO) is used as a traditional medicine and culinary herb to treat various diseases. Fatty acids, essential oils, and flavonoids were extracted from PO seeds and leaves using ultrasonic, microwave, and supercritical fluid extraction with RSM techniques. However, investigations on the secondary metabolites and antioxidant capabilities of the aerial part of PO (APO) are scarce. In order to extract polyphenols and antioxidants from APO as effectively as possible, this study used heat reflux extraction (HRE), response surface methodology (RSM), and artificial neural network (ANN) modeling. It also used high-resolution mass spectrometry to identify the APO secondary metabolite. A central-composite design (CCD) was used to establish the ideal ethanol content, extraction time, and extraction temperature to extract the highest polyphenolic compounds and antioxidant activity from APO. According to RSM, the highest amount of TPC (8.23 ± 1.06 mgGAE/g), TFC (43.12 ± 1.15 mgCAE/g), DPPH-scavenging activity (43.01 ± 1.25 % of inhibition) and FRAP (35.98 ± 0.19 µM ascorbic acid equivalent) were obtained at 60.0 % ethanol, 90.2 % time, and 50 °C. Statistical metrics such as the coefficient of determination (R²), root-mean-square error (RMSE), absolute average deviation (AAD), and standard error of prediction (SEP) revealed the ANN's superiority. Ninety-one (91) secondary metabolites, including phenolic, flavonoids, alkaloids, fatty acids, and terpenoids, were discovered using high-resolution mass spectrometry. In addition, 21 new phytoconstituents were identified for the first time in this plant. The results revealed a significant concentration of phytoconstituents, making it an excellent contender for the pharmaceutical and food industries.     

Neuro-computing intelligent networks for entropy optimized MHD fully developed nanofluid flow with activation energy and slip effects

In this paper, the main focus of this research is to represent an intelligent computing model through an artificial backpropagated Levenberg-Marquardt neural network (ABP-LMNN) for entropy optimized magnetohydrodynamic fully developed nanofluid flow with slip and activation energy effects. In mathematical modeling, dimensionless non-linear ODEs represent the magnetohydrodynamic nanofluid flow model (MHD-NFM). A reference dataset of ABP-LMNN is constructed for diverse situations of MHD-NFM by discrepancy of parameters. The attained reference dataset (RD) is randomly utilized for validation, testing and training processes for ABP-LMNN are employed to examine the approximate solution of MHD-NFM is demonstrated by comparison of outcomes. The authentic performance of the ABP-LMNN is validated through accuracy in the phrase of error histogram, mean square error and regression learning. The thermal and solutal parameters upsurge both the thermal and the concentration gradients. Moreover, the velocity profiles are declined owing to an increase in the second-order slip parameter in the tangential direction of the flow.     

盐湖水化学类型的人工神经网络判别方法

研究了作为典型径向基函数网络之一的概率神经网络在盐湖水化学类型分类预测中的应用，验证了该方法的可靠性，得到了满意的分类预测结果。实验结果和网络结构分析表明，概率神经网络方法比熟知的反向传播算法(BP)网络要好。概率神经网络的研究应用为化学模式识别提供了一个新工具。     

Optimization of Maceration Conditions for Improving the Extraction of Phenolic Compounds and Antioxidant Effects of Momordica Charantia L. Leaves Through Response Surface Methodology (RSM) and Artificial Neural Networks (ANNs)

The main goals of this research were the chemical and biological characterization of the bitter melon (Momordica charantia) isolate obtained by traditional (maceration) extraction, as well as optimization of this process using response surface methodology (RSM) and artificial neural networks (ANNs). Experiments were performed using Box–Behnken experimental design on three levels and three variables: extraction temperature (20?°C, 40?°C, and 60?°C), solvent concentration (30%, 50%, and 70%) and extraction time (30, 60, and 90?min). The measurements consisted of 15 randomized runs with 3 replicates in a central point. The antioxidant activity of obtained extracts was determined by the 1,1-diphenyl-2-picrylhydrazyl (DPPH), cupric ion reducing antioxidant capacity (CUPRAC) and ferric reducing antioxidant power (FRAP) assays while chemical characterization was done in terms of the total phenolic content (TPC). The methodology shows positive influence of solvent concentration on all four observed outputs, while temperature showed a negative impact. RSM showed that the optimal extraction conditions were 20?°C, 70% methanol, and an extraction time of 52.2?min. Under these conditions, the TPCs were 20.66 milligrams of gallic acid equivalents (mg GAE/g extract), DPPH 30.22 milligrams of trolox equivalents (mg TE/g extract), CUPRAC 67.78 milligrams of trolox equivalents (mg TE/g extract), and FRAP 45.48 milligrams of trolox equivalents (mg TE/g extract). The neural network coupled with genetic algorithms (ANN-GA) was also used to optimize the conditions for each of the outputs separately. It is anticipated that results reported herein will establish baseline data and also demonstrate that that the present model can be applied in the food and pharmaceutical industries.     

Optimization of extraction of linarin from Flos chrysanthemi indici by response surface methodology and artificial neural network

The extraction of linarin from Flos chrysanthemi indici by ethanol was investigated. Two modeling techniques, response surface methodology and artificial neural network, were adopted to optimize the process parameters, such as, ethanol concentration, extraction period, extraction frequency, and solvent to material ratio. We showed that both methods provided good predictions, but artificial neural network provided a better and more accurate result. The optimum process parameters include, ethanol concentration of 74%, extraction period of 2 h, extraction three times, solvent to material ratio of 12 mL/g. The experiment yield of linarin was 90.5% that deviated less than 1.6% from that obtained by predicted result.     

Polarography and artificial neural network for the simultaneous determination of nalidixic acid and its main metabolite (7-hydroxymethylnalidixic acid)

Nalidixic acid (NA) and its main metabolite, 7-hydroxymethylnalidixic acid (OH-NA), are simultaneously determined by applying artificial neural networks (ANNs), to their square wave voltammetric signals. The scores of a PCR model, built with the voltammetric data of a set of standard samples, recorded between −0.70 and −1.0 V, are used as training set for the net for each compound. The trained nets (ANNs) are used for the simultaneous determination of NA and OH-NA in urine. The recovery values are comprised between 91 and 109% for NA and between 82 and 112% for OH-NA, being these results better than the results obtained by application of partial least squares (PLS) multivariate calibration.     

2D bitmapping approach for identification and quantitation of common base flavor adulterants using surface acoustic wave arrays and artificial neural network data analysis

Arrays of polymer-coated surface acoustic wave microsensors are used in conjunction with a variety of signal-processing algorithms known as artificial neural networks (ANN). This format of data analysis has the capability to characterize complex mixtures of volatile and semi-volatile organic compounds commonly found in base flavors. The approach described, which minimizes the number of training sets while retaining the robustness of an ANN, utilizes a 2D bitmap matrix. The matrix is obtained by converting the time domain kinetics of sensor response into a bitmap. The high data throughput of this approach enables quantitation on the order of ppm of common base flavor adulterants.     

Application of an artificial neural network (ANN) and piezoelectric chemical sensor array for identification of volatile organic compounds

A piezoelectric chemical sensor array was developed using four quartz crystals. Gas chromatographic stationary phases were used as sensing materials and the array was connected to an artificial neural network (ANN). The application of the ANN method proved to be particularly advantageous if the measured property (mass, concentration, etc.) should not be connected exactly to the signal of the transducers of the piezoelectric sensor. The optimum structure of neural network was determined by a trial and error method. Different structures were tried with several neurons in the hidden layer and the total error was calculated. The optimum values of primary weight factors, learning rate (η=0.15), momentum term (μ=0.9), and the sigmoid parameter (β=1) were determined. Finally, three hidden neurons and 900 training cycles were applied. After the teaching process the network was used for identification of taught analytes (acetone, benzene, chloroform, pentane). Mixtures of organic compounds were also analysed and the ANN method proved to be a reliable way of differentiating the sensing materials and identifying the volatile compounds.     

Large Volume Sample Stacking (LVSS) in Capillary Electrophoresis (CE) with Response Surface Methodology (RSM) for the Determination of Phenolics in Food Samples

Abstract

A capillary electrophoresis method with large volume sample stacking (CE-LVSS) has been developed and validated for the simultaneous determination of seven phenolic compounds: naringin, rutin, carnosic acid, apigenin, quercetin, morin, and chichoric acid. Optimization was carried out by response surface methodology and a set of 20 experiments helped to optimize the parameters such as the concentration of buffer, buffer pH, and applied voltage. Analytes were separated using a 50?µm diameter capillary with 56?cm effective length and an extended light path using 20?mM borate buffer at pH 9.2. The LVSS method was optimized and a three- to fivefold improvement in detectability was achieved with injection at 100 mbar for 20?s followed by polarity switching at –20?kV for 6?s. The linearity values of all seven analytes were observed in the concentration ranges from 0.5 to 50?µg/mL for CE and 0.1 to 25?µg/mL for LVSS. The limits of detection were from 0.012 to 0.241 and 0.003 to 0.086?µg/mL for CE and LVSS. The obtained limits of quantitation were within 0.041 to 0.802 for CE and 0.012 to 0.286?µg/mL for LVSS. The recoveries were between 91.1 and 109.8% and 96.3 and 108.4% for CE and LVSS, respectively. The developed method has been successfully applied for the quantitative determination of analyzed components from food samples that are important sources of these compounds.     

Studies on the applicability of artificial neural network (ANN) in emulsion liquid membranes

An artificial neural network (ANN) model of emulsion liquid membrane (ELM) process is proposed in the present study which is able to predict solute concentration in feed during extraction operation and ultimate % extraction at different initial solute concentration in feed phase, internal reagent concentration, treat ratio, volume fraction of internal aqueous phase in emulsion and time. Because of the complexity in generalization of the phenomenon of ELM process by any mathematical model, the neural network proves to be a very promising method for the purpose of process simulation. The network uses the back-propagation algorithm (BPA) for evaluating the connection strengths representing the correlations between inputs (initial solute concentration in feed phase, internal reagent concentration, treat ratio, volume fraction of internal aqueous phase in emulsion and time) and outputs (solute concentration in feed during extraction operation and % extraction). The network employed in the present study uses five input nodes corresponding to the operating variables and two output nodes corresponding to the measurement of the performance of the network (solute concentration in feed during extraction and % extraction). Batch experiments are performed for separation of nickel(II) from aqueous sulphate solution of initial concentration in the 200–100 mg/l ranges. The network employed in the present study uses two hidden layers of optimum number of nodes being thirty and twenty. A leaning rate of 0.3 and momentum factor of 0.4 is used. The model predicted results in good agreement with the experimental data and the average deviations for all the cases are found to be well within ±10%.     

Optimization of Microwave-Assisted Extraction (MAE) for the Isolation of Antioxidants from Basil (Ocimum basilicum L.) by Response Surface Methodology (RSM)

Abstract

The recovery of antioxidants from basil (Ocimum basilicum L.) was modeled with the aid of response surface methodology (RSM) using microwave-assisted extraction (MAE). Face-centered central design (FCCD) was employed to optimize the MAE operational parameters including the extraction time (1 to 7?min), extraction temperature (30 to 120?°C), solid-to-solvent ratio (0.1 to 0.4), and solvent concentration (20 to 80% ethanol, v/v), and to obtain the best possible combinations of these parameters for a high antioxidant yield from basil. The total antioxidant capacity (TAC) was expressed in trolox (TR) equivalents per gram of dried sample (DS). Three of the operational parameters (temperature, extraction time and solvent concentration) were shown to have significant effect on the extraction efficiency of antioxidants in basil extracts (p?<?0.05). The solvent concentration was shown to be the most significant factor on antioxidant yield obtained by MAE. There was a close relationship between experimental and predicted values using the proposed method. This optimized MAE method shows an application potential for the efficient extraction of antioxidants from basil in the food and pharmaceutical industries.     

Single-step calibration, prediction and real samples data acquisition for artificial neural network using a CCD camera

An artificial neural network (ANN) model is developed for simultaneous determination of Al(III) and Fe(III) in alloys by using chrome azurol S (CAS) as the chromogenic reagent and CCD camera as the detection system. All calibration, prediction and real samples data were obtained by taking a single image. Experimental conditions were established to reduce interferences and increase sensitivity and selectivity in the analysis of Al(III) and Fe(III). In this way, an artificial neural network consisting of three layers of nodes was trained by applying a back-propagation learning rule. Sigmoid transfer functions were used in the hidden and output layers to facilitate nonlinear calibration. Both Al(III) and Fe(III) can be determined in the concentration range of 0.25-4 μg ml⁻¹ with satisfactory accuracy and precision. The proposed method was also applied satisfactorily to the determination of considered metal ions in two synthetic alloys.     

Statistical analysis and optimization of photodegradation efficiency of methyl orange from aqueous solution using cellulose/zinc oxide hybrid aerogel by response surface methodology (RSM)

The photodegradation efficiency of cellulose-X/zinc oxide-Y (CA-X/ZnO-Y) aerogels was studied to degrade methyl orange (MO) as an organic dye pollutant from an aqueous solution under UV light irradiation. In this study, the initial pH of the solution (3, 7, and 11), the photocatalyst dosage (3, 6, and 9 g L^-1), the initial concentration of solution MO (10, 20, and 30 ppm), and the concentration of cellulose in CA-X/ZnO-Y hybrid aerogel (3, 6, and 9 wt%) were selected as four variable parameters, whereas the photoderadation performance was selected as the response. Moreover, the response surface methodology (RSM) analysis was carried out to investigate the influence of four various experimental factors at different times on the degradation of MO. The adequacy result of the proposed models displays that total of the proposed models can predict the photodegradation efficiency of MO by CA-x/ZnO-y aerogel. The optimization results of the process showed that pH = 3 and concentration of MO = 10 ppm, photocatalyst dosage (9 g L^-1), and MCC concentration (9 g) are the optimal level of the studied parameters. Also, the results showed that desirability of 0.96 confirms the acceptance and applicability of the model where the RSM model is a helpful technique for the optimum conditions design.     

Hall effects and entropy generation applications for peristaltic flow of modified hybrid nanofluid with electroosmosis phenomenon

The electroosmotic peristaltic flow of modified hybrid nanofluid in presence of entropy generation has been presented in this thermal model. The Hall impact and thermal radiation with help of nonlinear relations has also been used to modify the analysis. The assumed flow is considered due to a non-uniform trapped channel. The properties of modified hybrid nanofluid model are focused with interaction of three distinct types of nanoparticles namely copper ($Cu)$, silver ($Ag$) and aluminum oxide ($A{l}_2{O}_3).$ The mathematical modeling and significances of entropy generation and Bejan number are identified. With certain flow assumptions, the governing equations are attained for optimized peristaltic electroosmotic problem. Widely used assumptions of long wave length and low Reynolds number reduced the governing equations in ordinary differential equations. The ND solver is flowed for the solution process. The physical significant of results is observed by assigning the numerical values to parameters.     

Separation of organic acid compounds from biological samples by zinc oxide nanoparticles–chitosan using genetic algorithm based on response surface methodology and artificial neural network

In this study, zinc oxide nanoparticles–chitosan based on solid phase extraction and high performance liquid chromatography was developed for the separation of organic compounds including citric, tartaric and oxalic acids from biological samples. For simulation and optimization of this method, the hybrids of genetic algorithm with response surface methodology (RSM) and artificial neural network (ANN) have been used. The predictive capability and generalization of both predictive models (RSM and ANN) were compared by unseen data. The results have shown the superiority of ANN compared with RSM. At the optimum conditions, the limits of detections of 2.2–2.9 µg L⁻¹ were obtained for the analytes. The developed procedure was then applied to the extraction and determination of organic acid compounds from biological samples. Copyright © 2014 John Wiley & Sons, Ltd.     

Response surface methodology (RSM) of chicken skin gelatin based composite films with rice starch and curcumin incorporation

This study aims to optimize the formulation of composite films based on chicken skin gelatin with incorporation of rice starch (10–20%, w/w) and curcumin (0.03–0.10%, w/v). The effect of their interaction on film's tensile strength (TS), elongation at break (EAB), water vapor permeability (WVP) and antioxidant properties (DPPH%) were investigated using a response surface methodology-central composite design (RSM-CCD). The optimized film formulation was further validated to indicate the validity of the prediction model. The optimum conditions of the film were selected with incorporation of rice starch at 20% (w/w) and curcumin at 0.03% (w/v). The optimized film formulation has revealed better mechanical properties with low WVP value and good antioxidant activity. The results showed that optimized composite films formulation based on chicken skin gelatin with the incorporation of rice starch and curcumin has proving good validation of model prediction and can be effectively utilized in food packaging industry.     

Statistical optimizing of electrocoagulation process for the removal of Cr(VI) using response surface methodology and kinetic study

Optimization of electrocoagulation (EC) using copper electrode in terms of Cr(VI) removal from simulated waste water was executed by applying surface methodology and kinetic study. In this research, electrocoagulation process was applied to evaluate the outcome of operational parameters such as initial Cr(VI) concentration, pH, electrode distance, current density and supporting electrolyte (NaCl) concentration for the removal of Cr(VI). The experimental results showed that current density of 41.32 A/m², electrode distance of 1.4 cm, initial pH of 5.65, time of electrocoagulation of 40 min and initial conductivity 0.21 ms are the optimal operating parameters to attain 93.33% removal efficiency of Cr(VI) ions from simulated waste water. The high value of R² = 98.15 and R²_adj = 96.49 show that fitted model confirms a good agreement with the real and predicted Cr(VI) removal percentage. It was concluded that Cr(VI) ion removal follows the first-order kinetic model by kinetic study of EC process.