Development and validation of colorimetric-assisted chemometrics methods based on the localized gold nanoparticles surface plasmon resonance for fast simultaneous estimation of anti-hepatitis C virus drugs in their combined dosage form: A comparative study with HPLC method.

The present work describes a developed analytical method based on a colorimetric assay using gold nanoparticles (AuNPs) along with chemometric techniques for the simultaneous estimation of sofosbuvir (SOF) and ledipasvir (LED) in their synthetic mixtures and tablet dosage form. The applied chemometric approaches were continuous wavelet transform (CWT) and least squares support vector machine (LS-SVM). Characterization of AuNPs and AuNPs in combination with the drug was performed by UV-vis spectrophotometer, transmission electron microscopy (TEM), dynamic light scattering (DLS), and Fourier transform infrared (FTIR) spectroscopy. In the CWT method, the zero amplitudes were determined at 427 nm with Daubechies wavelet family for SOF (zero crossing point of LED) and 440 nm with Symlet wavelet family for LED (zero crossing point of SOF) over the concentration range of 7.5-90.0 µg/L and 40.0-100.0 µg/L with coefficients of determination (R2) of 0.9974 and 0.9907 for SOF and LED, respectively. The limit of detection (LOD) and limit of quantification (LOQ) of this method were found to be 7.92, 9.96 µg/L and 12.02, 30.2 µg/L for SOF and LED, respectively. In the LS-SVM model, the mean percentage recovery of SOF and LED in synthetic mixtures was 98.29 % and 99.25 % with root mean square error of 2.392 and 1.034, which were obtained by the optimization of regularization parameter (γ) and width of the function (σ) based on the cross-validation method. The proposed methods were also applied for the determination concentration of SOF and LED in the combined dosage form, recoveries were higher than 95 %, and relative standard deviation (RSD) values were lower than 0.4 %. The achieved results were statistically compared with those obtained from the high-performance liquid chromatography (HPLC) technique for the concurrent estimation of components through one-way analysis of variance (ANOVA), and no significant difference was found between the suggested approaches and the reference one. According to these results, simplicity, high speed, lack of time-consuming process, and cost savings are considerable benefits of colorimetry along with chemometrics methods compared to other ways.

Rapid and naked-eye colorimetric detection of ultra trace sumatriptan in drinking water, saliva, and human urine samples based on the aggregation of gold nanoparticles.

In this study, the determination of sumatriptan (SUM) was performed using a simple, rapid, and precise colorimetric method based on the surface plasmon resonance (SPR) feature of gold nanoparticles (AuNPs). By adding SUM, the aggregation was observed in AuNPs with red-to-blue color shifts. The size distribution of NPs was estimated before and after adding SUM via dynamic light scattering (DLS), which was found to be 15.34 and 97.45 nm, respectively. Characterization of AuNPs, SUM, and AuNPs in combination with SUM was investigated using transmission electron microscopy (TEM) and Fourier transform infrared spectroscopy (FTIR). Examining the effect of pH, the volume of buffer, the concentration of AuNPs, interaction time, and ionic strength revealed that their optimal values were 6, 100 µL, 5 µM, 14 min, and 12 µg L-1, respectively. The suggested method was able to determine the amount of SUM in a linear range of 10 to 250 µg L-1 with a limit of detection (LOD) and limit of quantification (LOQ) of 0.392 and 1.03 µg L-1, respectively. This approach was successfully applied to determine SUM in drinking water, saliva, and human urine samples with relative standard deviations (RSD) lower than 0.03%, 0.3%, and 1.0%, respectively.

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.

Simultaneous ultra-trace quantitative colorimetric determination of antidiabetic drugs based on gold nanoparticles aggregation using multivariate calibration and neural network methods.

In this study, a simple and rapid method was investigated for the simultaneous ultra-trace colorimetric determination of Metformin (MET) and Sitagliptin (STG) based on the aggregation of gold nanoparticles (AuNPs). The Morphology and size distribution of synthesized AuNPs before and after adding drug (Zipmet) were monitored using transmission electron microscopy (TEM) and dynamic light scattering (DLS), respectively. By adding a drug, the absorption peak was shifted from 520 to 650 nm. The colorimetric method along with partial least squares (PLS) as a multivariate calibration method, as well as neural network time series were applied to estimate MET and STG simultaneously. The percentage of the mean recovery and root mean square error (RMSE) of the test set of mixtures related to the MET and STG were obtained 99.96, 1.1301 and 99.77, 1.0106, respectively. On the other hand, the regression coefficient (R2) of the training, validation, and test sets corresponding to the artificial neural network (ANN) were close to one for both components. Eventually, the proposed method was compared with a reference technique named high-performance liquid chromatography (HPLC) by analysis of variance (ANOVA) test and there was no significant difference between them.