A Simple UV Spectrophotometric Method Based on Continuous Wavelet Transform and Multivariate Calibration Model for the Concurrent Analysis of Three Water-Soluble Vitamins in Fertility Supplements.

BACKGROUND: Owing to the presence of overlapping spectra in pharmaceutical components, classical spectrophotometry is hard for concurrent determination. The advance of chemometrics along with UV-Vis spectrophotometry has contributed to solving this problem. OBJECTIVE: In this study, a fast, easy, precise, accurate, low-cost, and eco-friendly spectrophotometric technique was introduced and validated for the simultaneous analysis of vitamin B6, vitamin B12, and vitamin C in fertility supplements for men and women using continuous wavelet transform (CWT) and partial least squares (PLS) techniques without using time-consuming extraction process and organic solvents. METHOD: In the CWT method, the zero-crossing technique was applied to obtain the optimum points for plotting calibration curves for each component. The validation of both methods was evaluated by analyzing several mixtures with different concentrations. The efficiency of the proposed methods was also surveyed on commercial capsules. RESULTS: Wavelet families, including Symlet (sym2) at 230, Biorthogonal (bior1.3) at 378 nm, and Daubechies (db2) at 261, were considered for vitamins B6, B12, and C, respectively. The linear range was found to be 8-20, 8-20, and 10-25 µg/mL with the coefficient of determination (R2) equal to 0.9982, 0.9978, and 0.9701 for B6, B12, and C, respectively. Low limit of detection (LOD) (<0.09 µg/mL) and limit of quantification (LOQ) <0.9 µg/mL were achieved. The mean recovery values in synthetic mixtures were from 98.38 to 98.89% and from 99.83 to 99.99%, where root-mean-square error (RMSE) of not more than 0.4 and 0.05 using the CWT and PLS methods, respectively. CONCLUSIONS: The obtained results from the commercial capsules, applying the suggested techniques, were compared to those yielded by the high-performance liquid chromatography (HPLC) method using the analysis of variance (ANOVA) test. According to the results, there are no significant differences, and they were in good agreement. According to all the mentioned cases, the proposed approaches can replace the time-consuming and costly HPLC method in quality control laboratories. HIGHLIGHTS: Green spectrophotometry coupling chemometrics methods were proposed. Simultaneous determination of three water-soluble vitamins in fertility supplements was done using these approaches. Rapidity, simplicity, low cost, and accuracy are the benefits of the proposed methods. A HPLC technique was used as a reference method to compare with the chemometrics methods.

Modeling of malachite green adsorption onto novel polyurethane/SrFe12O19/clinoptilolite nanocomposite using response surface methodology and biogeography-based optimization-assisted multilayer neural network.

This research studied the modeling of malachite green (MG) adsorption onto novel polyurethane/SrFe12O19/clinoptilolite (PU/SrM/CLP) nanocomposite from aqueous solutions by the application of biogeography-based optimization (BBO) algorithm-assisted multilayer neural networks (MNN-BBO) as a new evolutionary algorithm in environmental science. The PU/SrM/CLP nanocomposite was successfully fabricated and characterized by some spectroscopic analyses. Four variables influencing the removal efficiency were modeled by MNN-BBO and response surface methodology (RSM). The MNN-BBO model gave higher percentage removal (99.6%) about 7.6% compared to the RSM technique. Under optimal conditions obtained by MNN-BBO, the four independent variables including pH, shaking rate, initial concentration, and adsorbent dosage were 6.5, 255 rpm, 50 mg.L-1, and 0.08 g, respectively. Under these conditions, the results were fitted well to the Langmuir isotherm with a monolayer maximum amount of sorbate uptake (qmax) of 68.49 mg.g-1 and the pseudo-first-order kinetic pattern with the rate constant (K1) of 0.01 min-1 with the R2 values of 0.9248 and 0.9980, respectively. The results of thermodynamics demonstrated that the MG uptake was not spontaneous due to the positive value of the adsorption ΔG. In addition, the positive values of ΔS (0.079 kJ/mol K) and ΔH (30.816 kJ/mol) indicated the feasible operation and endothermic approach, respectively. Besides, the wastewater investigations showed that the nanocomposite could be used as a new promising sorbent for efficient removal of MG (R% > 72) and magnetically separable from the real samples.

The efficient removal of bisphenol A from aqueous solution using an assembled nanocomposite of zero-valent iron nanoparticles/graphene oxide/copper: Adsorption isotherms, kinetic, and thermodynamic studies.

In this study, nanoparticles of zero-valent iron (nZVI) along with graphene oxide (GO) and copper (Cu) was synthesized to apply as a promising adsorbent for the rapid removal of bisphenol A (BPA) from aqueous solution. The characteristics of nZVI-GO-Cu were analyzed by field emission scanning electron microscope (FESEM), energy-dispersive X-ray spectroscopy (EDS), Fourier-transform infrared spectroscopy (FTIR), X-Ray Diffraction (XRD), and vibrating sample magnetometer (VSM). The average particle size of nZVI-GO-Cu was found to be 20.89 nm. The effective experimental variables such as pH, adsorbent dosage, contact time, initial BPA concentration, and temperature were surveyed to assess optimum conditions. Results revealed that the maximum removal percentage was obtained at pH of 7, adsorbent dosage of 0.2 g, contact time of 10 min, the BPA concentration of 10 mg/L, and a temperature of 35 °C as optimum conditions. Experimental data were fitted to the Langmuir and pseudo second-order models with a coefficient of determination (R2) equal to 1 and 0.995, respectively. The obtained maximum adsorption capacity (qmax) of the Langmuir isotherm was 21.59 mg g-1. Thermodynamic parameters under the various temperatures confirmed that the adsorption process was endothermic (ΔH = 17,459.4 J/mol and ΔS = 61.23 J/mol/K) and spontaneous (ΔG < 0). As a conclusion, nZVI-GO-Cu can be selected as an efficient adsorbent for the treatment of aqueous media from BPA and the other pollutants, due to its low-cost, high removal efficiency (97%), and rapid adsorption with the minimum time of 10 min compared with the other adsorbents.

Rapid extraction of copper ions in water, tea, milk and apple juice by solvent-terminated dispersive liquid-liquid microextraction using p-sulfonatocalix (4) arene: optimization by artificial neural networks coupled bat inspired algorithm and response surface methodology.

A bat inspired algorithm with the aid of artificial neural networks (ANN-BA) has been used for the first time in chemistry and food sciences to optimize solvent-terminated dispersive liquid-liquid microextraction (ST-DLLME) as a green, fast and low cost technique for determination of Cu2+ ions in water and food samples using p-sulfonatocalix (4) arene as a complexing reagent. For this purpose, the influence of four important factors four factors which was influenced on the extraction efficiency such as salt addition, solution pH and disperser and extraction solvent volumes were investigated. Central composite design (CCD) as a comparative technique was employed for optimization of ST-DLLME efficiency. The ANN-BA optimization technique was regarded as a superior model due to its higher value of extraction efficiency (about 7.21%) compared to CCD method. Under ANN-BA optimal conditions, the limit of quantitation (S/N = 10), limit of detection (S/N = 3) and linear range were 0.35, 0.12 and 0.35-1000 µg L-1, respectively. In these circumstances, the percentage recoveries for drinking tea, apple juice, milk, bottled drinking water, river and well water spiked with 0.05, 0.1 and 0.2 mg L-1 of Cu2+ ions were in the acceptable range (91.4-107.1%). In comparison to other methods, the developed ST-DLLME method showed the lowest solvent and sample consumption, shortest value of extraction time, most suitable determination and detection limits and linear range with simple and low cost apparatus. Additionally, the use of bat inspired algorithm as a powerful metaheuristic algorithm with the aid of artificial networks is another advantage of the present work.

Simultaneous extraction of Cu2+ and Cd2+ ions in water, wastewater, and food samples using solvent-terminated dispersive liquid-liquid microextraction: optimization by multiobjective evolutionary algorithm based on decomposition.

Solvent-terminated dispersive liquid-liquid microextraction (ST-DLLME) as a simple, fast, and low-cost technique was developed for simultaneous extraction of Cd2+ and Cu2+ ions in aqueous solutions. Multiobjective evolutionary algorithm based on decomposition with the aid of artificial neural networks (ANN-MOEA/D) was used for the first time in chemistry, environment, and food sciences to optimize several independent variables affecting the extraction efficiency, including disperser volume and extraction solvent volume, pH, and salt addition. To perform the ST-DLLME operations, xylene, methanol, and dithizone were utilized as an extraction solvent, disperser solvent, and chelating agent, respectively. Non-dominated sorting genetic algorithm versions II and III (NSGA II and NSGA III) as multiobjective metaheuristic algorithms and in addition central composite design (CCD) were studied as comparable optimization methods. A comparison of results from these techniques revealed that ANN-MOEA/D model was the best optimization technique owing to its highest efficiency (97.6% for Cd2+ and 98.3% for Cu2+). Under optimal conditions obtained by ANN-MOEAD, the detection limit (S/N = 3), the quantitation limit(S/N = 10), and the linear range for Cu2+ were 0.05, 0.15, and 0.15-1000 µg L-1, respectively, and for Cd2+ were 0.07, 0.21, and 0.21-750 µg L-1, respectively. The real sample recoveries at a spiking level of 0.05, 0.1, and 0.3 mg L-1 of Cu2+ and Cd2+ ions under the optimal conditions obtained by ANN-MOEA/D ranged from 94.8 to 105%.

Using artificial neural network and multivariate calibration methods for simultaneous spectrophotometric analysis of Emtricitabine and Tenofovir alafenamide fumarate in pharmaceutical formulation of HIV drug.

Spectrophotometric analysis method based on artificial neural network (ANN), partial least squares regression (PLS) and principal component regression (PCR) models was proposed for the simultaneous determination of Emtricitabine (ETB) and Tenofovir alafenamide fumarate (TAF) in human immunodeficiency virus (HIV) drug. An artificial neural network consisting of two, five, and seven layers with 2,3,5,7, and 9 neurons was trained by applying a feed forward back-propagation learning. In this method, Levenberg-Marquardt (LM) and gradient descent with momentum and adaptive learning rate back propagation (GDX) algorithms were used. Statistical parameters indicated that the ability of LM was better than GDX algorithm. Also, root mean square error (RMSE) and recovery (%) of the PLS and PCR methods showed that PLS has worked better than PCR. The proposed models were compared to the high- performance liquid chromatography (HPLC) as a reference method. Furthermore, the obtained results of the one-way analysis of variance (ANOVA) test at the 95% confidence level represented that there was no significant difference between the proposed and reference methods.

Determination of trace amounts of aromatic amines after magnetic solid-phase extraction using silver-modified Fe3 O4 /graphene nanocomposite.

In this work, a fast and simple magnetic dispersive solid phase extraction methodology was developed utilizing Ag@magnetite nanoparticles@graphene nanocomposite as an efficient magnetic nanosorbent for preconcentration and determine of five aromatic amines in water samples. The sorbent was characterized by diverse characterization techniques. After the extraction, high-performance liquid chromatography with UV detection was utilized to analysis the aromatic amines. The effects of different factors on the extraction process were studied thoroughly via design of experiment and desirability function. Detection limits and linear dynamic ranges were obtained in the range of 0.10-0.20 and 0.3-300 µg/L, respectively. The relative standard deviations (n = 5) were in the range of 4.3-6.5%. Eventually, the method was employed for determination of target aromatic amines in various water samples.

Use of continuous wavelet transform approach for simultaneous quantitative determination of multicomponent mixture by UV-Vis spectrophotometry.

In the present paper, a multicomponent analysis approach based on spectrophotometry method was developed for simultaneous determination of Guaifenesin (GU), Chlorpheniramine (CHL) and Pseudoephedrine (PSE) without any separation steps. The method under study is signal processing method based on Continuous Wavelet Transform (CWT) coupled with zero cross point technique. In this paper, CWT method was tested by synthetic ternary mixtures and was applied to the commercial cough syrup as a real sample and assessed by applying the standard addition technique. For demonstrate the accuracy of the results, other applications of signal processing, such as Derivative Transform (DT), Partial Least Squares (PLS) regression and Principal Components Regression (PCR) were used as comparative methods. Afterwards, the obtained results from analyzing the cough syrup by all methods were compared to the High-Performance Liquid Chromatography (HPLC) as a reference method. One-way analysis of variance test at 95% confidence level showed no significant differences between CWT and other applications.

Optimization of Solvent Terminated Dispersive Liquid-Liquid Microextraction of Copper Ions in Water and Food Samples Using Artificial Neural Networks Coupled Bees Algorithm.

A multivariate method based on solvent terminated dispersive liquid-liquid microextraction was developed for the determination of Cu2+ ions in aqueous samples. In the proposed approach, di-2-ethylhexylphosphoric acid, xylene and acetone were used as chelating agent, dispersive and extraction solvents, respectively. The effects of various factors on the extraction efficiency such as extraction and dispersive solvent volumes, salt addition and pH were studied using central composite design (CCD) and artificial neural networks coupled bees algorithm (ANN-BA). Upon comparison of these techniques, ANN-BA model was considered to be better optimization method due to its higher percentage relative recovery (about 5%) as compared to the CCD approach. The linear range and the limits of detection (S/N = 3) and quantitation (S/N = 10) were 0.22-140, 0.08 and 0.22 µg L-1, respectively. Under the optimal conditions, the recoveries for real samples spiked with 0.1 and 0.3 mg L-1 were in the range of 85-98%.

Synthesis, Characterization and Antibacterial Activity of Novel 1,3-Diethyl-1,3-bis(4-nitrophenyl)urea and Its Metal(II) Complexes.

A bioactive ligand and its dinuclear metal(II) complexes were synthesized and characterized by Fourier-transform infrared spectroscopy (FT-IR), ultraviolet-visible (UV-Visible), nuclear magnetic resonance (¹H-NMR), mass spectroscopy and molar conductance measurements. The ligand has been crystalized in the monoclinic system with a P21/c space group. The biological activities of metal complexes were evaluated using disc diffusion and broth dilution methods. In vitro antibacterial activities of the ligand and their metal complexes were examined against two Gram-positive bacteria (Bacillus subtilis and Staphylococcus aureus) and two Gram-negative bacteria (Escherichia coli and Serratia marcescens) and compared to the standard drugs. It was found that metal complexes displayed much higher antibacterial activities and better inhibitory effects than that of the ligand and standard drugs. Among these complexes, the compound having Zn-metal showed greater antibacterial activity against all four tested bacteria and was more effective against Serratia marcescens with the zone inhibition diameter of 26 mm and MIC value of 31.25 µg/mL.

Optimizing Cu(II) removal from aqueous solution by magnetic nanoparticles immobilized on activated carbon using Taguchi method.

This study synthesized magnetic nanoparticles (Fe(3)O(4)) immobilized on activated carbon (AC) and used them as an effective adsorbent for Cu(II) removal from aqueous solution. The effect of three parameters, including the concentration of Cu(II), dosage of Fe(3)O(4)/AC magnetic nanocomposite and pH on the removal of Cu(II) using Fe(3)O(4)/AC nanocomposite were studied. In order to examine and describe the optimum condition for each of the mentioned parameters, Taguchi's optimization method was used in a batch system and L9 orthogonal array was used for the experimental design. The removal percentage (R%) of Cu(II) and uptake capacity (q) were transformed into an accurate signal-to-noise ratio (S/N) for a 'larger-the-better' response. Taguchi results, which were analyzed based on choosing the best run by examining the S/N, were statistically tested using analysis of variance; the tests showed that all the parameters' main effects were significant within a 95% confidence level. The best conditions for removal of Cu(II) were determined at pH of 7, nanocomposite dosage of 0.1 gL(-1) and initial Cu(II) concentration of 20 mg L(-1) at constant temperature of 25 °C. Generally, the results showed that the simple Taguchi's method is suitable to optimize the Cu(II) removal experiments.