An electrochemical DNA-sensor developed with the use of methylene blue as a redox indicator for the detection of DNA damage induced by endocrine-disrupting compounds

An electrochemical biosensor capable of indirect detection of DNA damage induced by any one of the three endocrine-disrupting compounds (EDCs) – bisphenol A (BPA), 4-nonylphenol (NP) and 4-t-octylphenol (OP), has been researched and developed. The methylene blue (MB) dye was used as the redox indicator. The glassy carbon electrode (GCE) was modified by the assembled dsDNA/graphene oxide-chitosan/gold nano-particles to produce a dsDNA/GO-CS/AuNPs/GCE sensor. It was characterized with the use of electrochemical impedance spectroscopy (EIS), cyclic voltammetry (CV) and scanning electron microscopy (SEM) techniques. The loading/release of the MB dye by the dsDNA/GO-CS/AuNPs film was investigated, and the results showed that the process was reversible. Based on this, the sensor was used to measure the difference between the loading capabilities of intact and damaged dsDNA in the films. The sensor was then successfully applied to detect DNA damage electrochemically. The differential pulse voltammetry (DPV) peak current ratio for MB, observed before and after DNA damage, increased linearly in the presence the BPA, NP or OP compounds; the treatment range was 10–60 min, and the respective damage rates were 0.0069, 0.0044 and 0.0031 min⁻¹, respectively. These results were confirmed by the binding constants: 2.09 × 10⁶ M⁻¹ (BPA-DNA), 1.28 × 10⁶ M⁻¹ (NP-DNA) and 9.33 × 10⁵ M⁻¹ (OP-DNA), all of which were obtained with the use of differential pulse stripping voltammetry (DPSV).     

Simultaneous analysis of three catecholamines by a kinetic procedure: comparison of prediction performance of several different multivariate calibrations

A rapid kinetic method for the simultaneous determination of levodopa, dopamine, and dobutamine was examined and developed. It was based on a consecutive reaction of a reduction of Cu(II) to Cu(I) by catecholamines, followed by the complexation of Cu(I) with neocuproine to form a yellow product in an acetic acid-acetate buffer. Spectrophotometric data were recorded at 453 nm (wavelength at the yellow complex absorption maximum) for 300 s. Linear calibrations were obtained in the concentration ranges of (0.08–1.44) × 10⁻⁵ mol L⁻¹, (0.08–1.44) × 10⁻⁵ mol L⁻¹, and (0.16–1.44) × 10⁻⁵ mol L⁻¹ for levodopa, dopamine, and dobutamine, respectively. A variety of multivariate calibration models was developed for simultaneous analysis of the three analytes; while most models produced satisfactory prediction results for synthetic samples, the hybrid linear analysis method was arguably the best-performing (relative prediction error, RPET = 6.6 %). The proposed method was applied to an analysis of spiked rabbit serum samples and the results showed good agreement with the high performance liquid chromatography measurements.     

Binding Interaction of Dopamine with Bovine Serum Albumin: A Biochemical Study

The binding interaction of bovine serum albumin (BSA) with dopamine was studied by different spectroscopic techniques, and a fluorescence quenching mechanism was associated with this process. Estimated thermodynamic parameters indicated the presence of hydrogen bonding and van der Walls forces between dopamine and BSA. The microenvironment of the protein-binding site was studied by the synchronous fluorescence, FT-TR, and three-way fluorescence techniques in the presence of dopamine, and changes in conformation were indicated within the binding cavity. This study provides useful information on the transportation and distribution of dopamine in proteins.     

Wanderung und Verteilung von Vernetzungsmitteln in Cellulose-Substraten. II. Eine Deutung der Querschnittsphotographien vernetzter und mit Rhodamin B gefärbter Baumwoll-Gewebe

Ohne Zusammenfassung     

Does chemometrics enhance the performance of electroanalysis?

This review explores the question whether chemometrics methods enhance the performance of electroanalytical methods. Electroanalysis has long benefited from the well-established techniques such as potentiometric titrations, polarography and voltammetry, and the more novel ones such as electronic tongues and noses, which have enlarged the scope of applications. The electroanalytical methods have been improved with the application of chemometrics for simultaneous quantitative prediction of analytes or qualitative resolution of complex overlapping responses. Typical methods include partial least squares (PLS), artificial neural networks (ANNs), and multiple curve resolution methods (MCR-ALS, N-PLS and PARAFAC). This review aims to provide the practising analyst with a broad guide to electroanalytical applications supported by chemometrics. In this context, after a general consideration of the use of a number of electroanalytical techniques with the aid of chemometrics methods, several overviews follow with each one focusing on an important field of application such as food, pharmaceuticals, pesticides and the environment. The growth of chemometrics in conjunction with electronic tongue and nose sensors is highlighted, and this is followed by an overview of the use of chemometrics for the resolution of complicated profiles for qualitative identification of analytes, especially with the use of the MCR-ALS methodology. Finally, the performance of electroanalytical methods is compared with that of some spectrophotometric procedures on the basis of figures-of-merit. This showed that electroanalytical methods can perform as well as the spectrophotometric ones. PLS-1 appears to be the method of practical choice if the %relative prediction error of ∼±10% is acceptable.     

Synchronous fluorescence and UV-vis spectroscopic studies of interactions between the tetracycline antibiotic, aluminium ions and DNA with the aid of the Methylene Blue dye probe

Synchronous fluorescence spectroscopy (SFS) was applied for the investigation of interactions of the antibiotic, tetracycline (TC), with DNA in the presence of aluminium ions (Al³⁺). The study was facilitated by the use of the Methylene Blue (MB) dye probe, and the interpretation of the spectral data with the aid of the chemometrics method, parallel factor analysis (PARAFAC). Three-way synchronous fluorescence analysis extracted the important optimum constant wavelength differences, Δλ, and showed that for the TC-Al³⁺-DNA, TC-Al³⁺ and MB dye systems, the associated Δλ values were different (Δλ = 80, 75 and 30 nm, respectively). Subsequent PARAFAC analysis demonstrated the extraction of the equilibrium concentration profiles for the TC-Al³⁺, TC-Al³⁺-DNA and MB probe systems. This information is unobtainable by conventional means of data interpretation. The results indicated that the MB dye interacted with the TC-Al³⁺-DNA surface complex, presumably via a reaction intermediate, TC-Al³⁺-DNA-MB, leading to the displacement of the TC-Al³⁺ by the incoming MB dye probe.     

Simultaneous determination of iron and aluminium by differential kinetic spectrophotometric method and chemometrics

A differential kinetic spectrophotometric method was researched and developed for the simultaneous determination of iron and aluminium in food samples. It was based on the direct reaction kinetics and spectrophotometry of these two metal ions with Chrome Azurol S (CAS) in ethylenediamine-hydrochloric acid buffer (pH 6.3). The results were interpreted with the use of chemometrics. The kinetic runs and the visible spectra of the complex formation reaction were studied between 540 and 750 nm every 30 s over a total period of 285 s. A set of synthetic metal mixture samples was used to build calibrations models. These were based on the spectral and kinetic two-way data matrices, which were processed separately by the radial basis function-artificial neural network (global RBF-ANN) method. The prediction performance of these models was poorer than that from the combined kinetic-spectral three-way array, which was similarly processed by the same method (% relative prediction error (RPE_T) = 5.6). These results demonstrate that improved predictions can be obtained from the data array, which has more information, and that appropriate chemometrics methods can enhance analytical performance of simple techniques such as spectrophotometry.Other chemometrics models were then applied: N-way partial least squares (NPLS), parallel factor analysis (PARAFAC), back propagation-artificial neural network (BP-ANN), single radial basis function-artificial neural network (RBF-ANN), and principal component neural network (PC-RBF-ANN). There was no substantial difference between the methods with the overall %RPE_T range being 5.0-5.8. These two values corresponded to the NPLS and BP-ANN models, respectively. The proposed method was applied for the determination of iron and aluminium in some commercial food samples with satisfactory results.     

The Use of Chronoamperometry and Chemometrics for Optimization of Conducting Polymer Sensor Arrays

A general approach using principal component analysis was applied to amperometric data obtained from flow injection analysis (FIA) to select conducting electroactive polymers (CEPs) and applied potential waveforms for discrimination of potassium and methylamine. The results were compared with the selection from the application of the PCA approach for cyclic voltammetry data in our previous research. Good agreement between the two methods of selection was found, which indicates that within the general qualitative limits of the approach, PCA can be applied for selection of chemical and electrochemical parameters for FIA detection/discrimination using CEP sensor arrays detector for a range of analytes.     

Simultaneous Determination of Major Constituents of Honeybee Venom by LC-DAD

The aim of the study was to develop an LC method for honeybee venom analysis, using cytochrome c as an internal standard. The SynChropack C8 6.5 μm, 4.6 × 100 mm column was applied. The bee venom was separated by linear gradient 5–80% B at 30 min (eluent A—0.1% TFA in water, eluent B—0.1% TFA in acetonitrile:water (80:20)). The flow rate of mobile phase was maintained at 1 mL min^?1, injection volume: 40 μL, separation temperature: 25 °C. The analysis was monitored at 220 nm. Several honeybee venom constituents were separated and the content of four of them (apamine, mast cell degranulating peptide, phospholipase A₂ and melittin) were determined. By applying this methodology differences in chemical composition of honeybee venom were evaluated. In order to confirm the data obtained, the following steps and parameters were taken into account for the validation of the method: selectivity, precision (injection repeatability, analysis repeatability), accuracy (recovery), linearity and operating range, limit of detection and limit of quantitation. All steps of validation proved that the developed analytical procedure was suitable for its intended purpose (standardization). Due to its simplicity, the developed method can be easily automated and incorporated into routine operations both in the bee venom identification, quality control and assay tests.     

Application of differential pulse stripping voltammetry and chemometrics for the determination of three antibiotic drugs in food samples

A reliable method for simultaneous determination of three antibiotic drugs(levofloxacin,gatifloxacin and lomefloxacin) by differential pulse stripping voltammetry(DPSV) in Britton-Robinson buffer(pH 7.96) was presented.The method is based on adsorptive accumulation of the antibacterial drugs on a hanging mercury dropping electrode(HMDE),followed by the reduction of the adsorptive species by the technique of DPSV.Optimal conditions,the deposition time of 80 s,the deposition potential of—1250 mV,and the scan rate of 25 mV/s,were obtained.The linear concentration ranges of 0.010-0.080μg/mL were obtained for all these three antibiotic drugs,while the detection limits were 2.38,3.20 and 1.60ng/mL for levofloxacin,gatifloxacin and lomefloxacin,respectively.In this work,chemometrics methods,such as classical least squares(CLS),partial least squares(PLS), principle component regression(PCR) and radial basis function-artificial neural networks(RBF-ANN),were used to quantitatively resolve the overlapping signals.It was found that PCR gave the best results with total relative prediction error(RPE_T) of 7.71%.The proposed method was applied to determine these three drugs in several commercial food samples with spiked method and yielded satisfactory recoveries.