RESUMO
In this communication, we report one factor that could limit the quantitative analysis by SERS, which has not yet been discussed in the literature. Our results show that SERS experiments performed with the substrate immersed in liquid solutions are subjected to a temporal drift in the Raman signal intensity. Measurements were performed using gold nanoparticle suspensions and gold-covered nanostructured ITO surfaces as SERS substrates, immersed in analyte solutions of crystal violet and 4-mercaptobenzoic acid. Depending on the substrate and the conditions used for measurements, the Raman signal can take between 30 min and several hours to stabilize. This effect, if not taken into account, could have a negative impact on the results of the quantitative chemical analysis by SERS performed in situ in liquid solutions.
RESUMO
Chlorpheniramine maleate (CLOR) enantiomers were quantified by ultraviolet spectroscopy and partial least squares regression. The CLOR enantiomers were prepared as inclusion complexes with beta-cyclodextrin and 1-butanol with mole fractions in the range from 50 to 100%. For the multivariate calibration the outliers were detected and excluded and variable selection was performed by interval partial least squares and a genetic algorithm. Figures of merit showed results for accuracy of 3.63 and 2.83% (S)-CLOR for root mean square errors of calibration and prediction, respectively. The ellipse confidence region included the point for the intercept and the slope of 1 and 0, respectively. Precision and analytical sensitivity were 0.57 and 0.50% (S)-CLOR, respectively. The sensitivity, selectivity, adjustment, and signal-to-noise ratio were also determined. The model was validated by a paired t test with the results obtained by high-performance liquid chromatography proposed by the European pharmacopoeia and circular dichroism spectroscopy. The results showed there was no significant difference between the methods at the 95% confidence level, indicating that the proposed method can be used as an alternative to standard procedures for chiral analysis.
Assuntos
1-Butanol/metabolismo , Clorfeniramina/análise , Clorfeniramina/química , Espectrofotometria Ultravioleta , beta-Ciclodextrinas/metabolismo , Calibragem , Clorfeniramina/metabolismo , Cromatografia Líquida de Alta Pressão , Dicroísmo Circular , EstereoisomerismoRESUMO
The simultaneous determination of lanthanide family elements is one of the greatest problems in analytical chemistry, due to the close similarity of their chemical properties. Spectrophotometric methods are generally of limited use, due to the various mutual spectral interferences involved. By using multivariate calibration methods (partial least-squares regression, PLSR), it was possible to obtain a model that adjusts itself perfectly to the values of the mixture concentrations used in the calibration. The model used absorption spectra in the 290-800 nm range for a set of 20 different mixtures of Ce, Pr, Nd and Sm, and made possible the determination of Ce, Pr and Nd concentrations of a commercial rare-earth product, with significantly greater precision than the conventional univariate calibration method. Determination of the Sm concentrations was not possible, since its concentration was below the concentrations used in the model definition.
RESUMO
The simultaneous determination of organic dye mixtures by using spectrophotometric methods is a difficult problem in analytical chemistry, due to spectral interferences. By using multivariate calibration methods such as partial least-squares regression (PLSR), it is possible to obtain a model adjusted to the concentration values of the mixtures used in the calibration stage. In this study, the calibration model is based on absorption spectra in the 350-650-nm range for a set of 16 different mixtures of reactive red 195, reactive yellow 145 and reactive orange 122 dyes, and made the determination of the dye concentrations possible in a validation set with significantly greater accuracy than the conventional univariate calibration method. By using the developed model it was possible to monitor the decolorization kinetic of one dye (reactive orange 122), when the mixture of the three dyes was previously submitted to an ozonation process.
RESUMO
This paper describes how artificial neural networks can be used to classify multivariate data. Two types of neural networks were applied: a counter propagation neural network (CP-ANN) and a radial basis function network (RBFN). These strategies were used to classify soil samples from different geographical regions in Brazil by means of their near-infrared (diffuse reflectance) spectra. The results were better with CP-ANN (classification error 8.6%) than with RBFN (classification error 11.0%).
RESUMO
In this work a stainless steel electrode was prepared, characterised and used as indicator electrode in a potentiometric flow cell. Due to its versatility, it was possible to study different electrode geometries and flow cell arrangements. After optimising the system, mixtures of succinic and oxalic acids were determined by titration. Partial least squares (PLS) regression as multivariate calibration tool was applied for data treatment. The predicted results obtained in a test set showed a relative error of 4.3% for succinic acid and 5.5% for oxalic acid.
RESUMO
An approach aiming at extracting the relevant component for multivariate calibration is introduced, and its performance is compared with the "uninformative variable elimination" approach and with the standard PLS method for the modeling of near-infrared data. The extraction of the relevant component is carried out in the wavelet domain. The PLS results on these relevant features are better, and therefore, it seems that this approach can successfully be used to remove noise and irrelevant information from spectra for multivariate calibration.