Sherlan G. Lemos

Electroanalytical determination of carbendazim by square wave adsorptive stripping voltammetry with a multiwalled carbon nanotubes modified electrode

A preconcentrating/voltammetric multiwalled carbon nanotube modified glassy carbon electrode (MWCNT–GCE) has been developed for stripping analysis of carbendazim (Methyl Benzimidazol-2-yl Carbamate—MBC), based on dispersing MWCNT in water. The effect of experimental variables, such as the dispersion and loading of MWCNT, was assessed. A quasi-reversible behavior for MBC in acetic acid/acetate buffer 0.1 mol L−1 (pH 4.7) was verified and its high effective pre-concentration was attributed to the high adsorption capability and enormous surface area of the MWCNT. No evidence of carry-over effect, combined with the easiness of electrode preparation, led to the development of a highly sensitive and reliable method with an experimental work range from 0.256 to 3.11 µmol L−1 with a detection limit of 10.5 ppb for a short (60 s) accumulation period. Measurement of MBC in a river water sample was demonstrated. The accuracy of the method for real sample analysis was assessed by estimating the apparent recovery (93 ± 2.9% and 86 ± 4.1% for 4.3 × 10−7 mol L−1) for a MBC spiked river water sample.

Indirect determination of sodium diclofenac, sodium dipyrone and calcium gluconate in injection drugs using digital image-based (webcam) flame emission spectrometric method

This paper proposes a digital image-based flame emission spectrometric (DIB-FES) method for indirect determination of sodium diclofenac, sodium dipyrone and calcium gluconate in injectable forms. The proposed DIB-FES method uses digital images obtained from a webcam, based on the RGB (Red-Green-Blue) system. It offers a simple and inexpensive way to quantify these organic substances using the radiation emitted by the alkaline and earth-alkaline metals present in their formulae. Analytical curves were constructed on the basis of the relationship between RGB values and calibration solution concentrations. The results showed no statistical difference between the proposed and reference methods when applying the paired t-test at a 95% confidence level. The proposed DIB-FES method also performed well in terms of the figures of merit LOD, LOQ, linear range, precision, and the accuracy as revealed by recovery tests.

Exploring the emission intensities of ICP OES aided by chemometrics in the geographical discrimination of mineral waters

A rapid method for classification of mineral waters is proposed. The discrimination power was evaluated by a novel combination of chemometric data analysis and qualitative multi-elemental fingerprints of mineral water samples acquired from different regions of the Brazilian territory. The classification of mineral waters was assessed using only the wavelength emission intensities obtained by inductively coupled plasma optical emission spectrometry (ICP OES), monitoring different lines of Al, B, Ba, Ca, Cl, Cu, Co, Cr, Fe, K, Mg, Mn, Na, Ni, P, Pb, S, Sb, Si, Sr, Ti, V, and Zn, and Be, Dy, Gd, In, La, Sc and Y as internal standards. Data acquisition was done under robust (RC) and non-robust (NRC) conditions. Also, the combination of signal intensities of two or more emission lines for each element were evaluated instead of the individual lines. The performance of two classification—k-nearest neighbor (kNN) and soft independent modeling of class analogy (SIMCA)—and preprocessing algorithms, autoscaling and Pareto scaling, were evaluated for the ability to differentiate between the various samples in each approach tested (combination of robust or non-robust conditions with use of individual lines or sum of the intensities of emission lines). It was shown that qualitative ICP OES fingerprinting in combination with multivariate analysis is a promising analytical tool that has potential to become a recognized procedure for rapid authenticity and adulteration testing of mineral water samples or other material whose physicochemical properties (or origin) are directly related to mineral content.

A simple voltammetric electronic tongue for the analysis of coffee adulterations

This work presents a simple and low-cost analytical approach to detect adulterations in ground roasted coffee by using voltammetry and chemometrics. The voltammogram of a coffee extract (prepared as simulating a home-made coffee cup) obtained with a single working electrode is submitted to pattern recognition analysis preceded by variable selection to detect the addition of coffee husks and sticks (adulterated/unadulterated), or evaluate the shelf-life condition (expired/unexpired). Two pattern recognition methods were tested: linear discriminant analysis (LDA) with variable selection by successive projections algorithm (SPA), or genetic algorithm (GA); and partial least squares discriminant analysis (PLS-DA). Both LDA models presented satisfactory results. The voltammograms were also evaluated for the quantitative determination of the percentage of impurities in ground roasted coffees. PLS and multivariate linear regression (MLR) preceded by variable selection with SPA or GA were evaluated. An excellent predictive power (RMSEP = 0.05%) was obtained with MLR aided by GA.

Voltammetric e-Tongue Based on a Single Sensor and Variable Selection for the Classification of Teas

This work presents a simple and low-cost strategy to obtain voltammetric tongues by using a single voltammetric sensor aided by variable selection techniques. A usual electronic tongue consists of an array of physical sensors followed by data compression before pattern recognition modeling. Alternatively, a single voltammetric sensor can also act as an array of pseudo-sensors—applied potentials in a voltammogram—that can be properly selected to perform a given discrimination. The applicability of this strategy was evaluated in the discrimination of teas. Teas prepared for immediate intake (simulating a home-made tea cup) were analyzed with staircase voltammetry at an epoxy-graphite electrode. Voltammograms were submitted to variable selection previously to linear discriminant analysis (LDA) to identify the applied potentials that improve discrimination of teas according to variety, country of origin, and manufacturer. Successive projections algorithm (SPA), genetic algorithm (GA), and stepwise (SW) formulation were the variable selection techniques evaluated. Best results were achieved with SPA/LDA models, with correct classification rates for the prediction set close to 100%.