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A simple procedure is proposed for the determination of the antibiotic moxifloxacin in urine using nanostructured gold as surface-enhanced Raman scattering signal enhancer. The standard addition method in conjunction to multivariate curve resolution-alternating least squares was applied to eliminate the matrix effect and to isolate the spectral contribution of the analyte. Even in the presence of unexpected interferences in the urinary media, it was possible to extract and quantify the analyte response, reaching, in this way, the so-called second-order advantage from first-order data. Moreover, although a saturation phenomenon of the metallic surface was observed, the results of the proposed methodology presented important advantages such as high sensitivity and simpler experimental procedures. The moxifloxacin was determined at levels of 0.70 and 1.50 μg mL(-1) in urine diluted to 1.0% (corresponding to 70.0 and 150 μg mL(-1) in the original samples) with relative errors of 4.23 and 8.70%, respectively. The limit of detection (0.085 μg mL(-1)) and limit of quantification (0.26 μg mL(-1)) values indicated that the quantification can be accomplished in urine up to 24 h after the administration of a single 400-mg dose.

The formation of cooperative hydrogen bonds between the thymine-adenine pair was used to indirectly determine thymine in aqueous solution by surface-enhanced Raman spectroscopy (SERS), therefore improving the limit of detection (LOD) values up to two orders of magnitude. The concentration of adenine was held constant and SERS spectra over gold nanoparticles were acquired through variable concentrations of thymine. The overall methodology followed a multivariate approach leading us to find the most suitable adenine concentrations to determine thymine and evidence the formation of new species whose response maintains a linear correlation with thymine nominal concentrations. Partial Least Squares (PLS) regression has been applied for modelling the data and close values of the root mean square error of cross-validation (RMSECV) and root mean square error of prediction (RMSEP) were obtained as indicators of model quality for modelling and prediction. The LOD for the thymine determination has been lowered from ∼20 to 0.278 mmol L(-1) with a mean prediction error of 3.3%. It was verified that the improvement in LOD is only possible if the base pair is formed prior to the addition of the plasmonic nanoparticles. Finally, the assessment of the effect of an interference species introducing uracil to the system showed that it was possible to isolate the analyte response from the overall signals.

In this study, a combination of the high detectability and specificity of the SERS technique with powerful chemometric tools was used to obtain hyperspectral images in order to assess the chemical distribution of the components of polymeric microfilms loaded with paracetamol as active principle. Four microfilms with drug content ranging between 5.42 and 18.62% were fabricated directly over nanostructured gold substrates and spectra were acquired over an area of 4 × 4 mm. Chemical images were first built following a qualitative approach by applying principal component analysis (PCA) and calculating correlation coefficients. With the algorithm multivariate curve resolution-alternating least squares (MCR-ALS), the mean concentrations of all components were calculated with relative error values between 3.5 and 4.9% for paracetamol. With the proposed methodology, both qualitative and quantitative assessments of the distribution of constituents of polymeric microfilms were successfully performed. In addition, physical-chemistry interactions among them could be also analyzed.

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