The aim of this study was to develop a fast, simple and accurate analytical method for the classification of reference tablet analgesic drugs containing dipyrone, orphenadrine and caffeine using differential scanning calorimetry (DSC) and visible-near infrared spectroscopy (VNIRS) associated with one-class chemometric classification algorithm. The training set is based on reference medicine with 15 samples as target class. Three different brands with 10 samples each and five reference medicine samples, obtaining 35 samples, were used as the test set. Chemometric models based on principal component analysis (PCA) and data-driven soft independent modelling of class analogy (DD-SIMCA) were used to obtain the results. Two DD-SIMCA models obtained 100% sensitivity, specificity, and accuracy using DSC and VNIRS, both with a significance level of 0.01. This method using one-class classification as a chemometric tool proved to be a good alternative for quality control of pharmaceutical samples.

Infusions of Mikania glomerata Sprengel (guaco) leaves are used in Brazil for the treatment of respiratory diseases. Coumarin is the chemical marker of guaco for quality control purposes. This work proposes a voltammetric methodology to determine coumarin by using a boron-doped diamond electrode. At pH 10 (0.1 mol L-1 Britton-Robinson buffer solution), coumarin is irreversibly oxidized around +1.77 V in a process predominantly controlled by diffusion. The methodology described here presented a linear range from 1.54 to 15.3 µg mL-1 with limits of detection and quantification of 0.20 and 0.62 µg mL-1, respectively. Analysis of variance confirmed the significance of the regression and the absence of lack-of-fit at the confidence level of 95%. Infusion of commercial guaco leaves was directly analyzed, and results were in agreement with high-performance liquid chromatography after applying the paired t-test at a confidence level of 95%.

O objetivo deste estudo foi classificar amostras de comprimidos contendo dipirona, cafeína e orfenadrina usando espectroscopia no infravermelho próximo e técnicas quimiométricas. O conjunto de dados foi de 300 espectros de amostras de três comprimidos por lote e quatro diferentes produtores. O pré-processamento foi realizado pelo algoritmo Savitzky-Golay com primeira derivada, janela de 17 pontos e polinômio de segunda ordem. A classificação dos comprimidos foi conduzida usando modelos quimiométricas baseados na análise de componentes principais (PCA), modelagem independente flexível por analogias de classes (SIMCA), algoritmo genético- (GA-LDA) e algoritmo das projeções sucessivas-análise discriminante linear (SPA-LDA). Pela análise PCA, observou-se agrupamentos para cada conjunto de comprimidos. Para o modelo SIMCA, utilizou-se 15 e 30 medidas espectrais para o conjunto de treinamento dos medicamentos similares e de referência, respectivamente. Para o modelo GA-LDA, utilizou-se 12 variáveis, enquanto que o modelo SPA-LDA selecionou somente dois comprimentos de onda, 1572 e 1933 nm. Os modelos classificaram corretamente todas as amostras. A metodologia permitiu uma classificação rápida e não destrutiva das amostras e sem necessidade de determinações analíticas convencionais.

The objective of this study was to classify samples of tablets containing dipyrone, caffeine and orphenadrine using near infrared (NIR) spectroscopy and chemometric techniques. The data set had 300 spectra of samples from three tablets per batch and four different manufacturers. The pre-processing was accomplished by Savitzky-Golay algorithm with the first derivative, window with 17 points and second-order polynomial. The tablet classification was performed using chemometric models based on principal component analysis (PCA), soft independent modeling of class analogies (SIMCA), genetic algorithm- (GA-LDA) and successive projection algorithm-linear discriminant analysis (SPA-LDA). For PCA analysis, clusters were observed for each group of tablets. The SIMCA model was built using 15 and 30 spectral measures for the training set of similar drugs and reference drugs, respectively. The GA-LDA model used 12 variables, whereas SPA-LDA selected only two wavelengths, 1572 and 1933 nm. The methodology allowed a quick and non-destructive classification of the tablets and without the need for conventional analytical determinations.