This work developed an effective and eco-friendly analytical method to support quality control for Brazilian Bill proposal No. 5332/2009. The bill advocates the creation of the “Brazilian bread” by mandating the incorporation of cassava byproducts into wheat flour, with the aim of reducing Brazil’s external dependency on wheat imports and promoting domestic cassava family farming. For this, a handheld near-infrared (NIR) spectrophotometer was utilized for on-site determination of cassava starch in commercial wheat flour. The quantitative analysis, which utilized NIR spectra pre-processed with offset correction (OFF) combined with linear baseline correction (LBC), alongside the successive projections algorithm for interval selection in partial least squares (iSPA-PLS), exhibited superior predictive capability, achieving a relative error of prediction below 8%. Furthermore, the classification process, which involved NIR spectra pre-processed with LBC and iSPA-PLS for discriminant analysis (iSPA-PLS-DA), yielded an impressive 94% overall accuracy, with only 3 misclassifications in the test set. ecofriendly eco friendly No 53322009 5332 2009 5332/2009 bread flour Brazils Brazil s farming this nearinfrared near infrared (NIR onsite site preprocessed pre processed OFF (OFF LBC, , (LBC) iSPAPLS, iSPAPLS iSPA PLS (iSPA-PLS) capability 8 8% Furthermore process iSPAPLSDA, iSPAPLSDA DA (iSPA-PLS-DA) 94 accuracy set 5332200 533 200 5332/200 (LBC (iSPA-PLS (iSPA-PLS-DA 9 533220 53 20 5332/20 53322 5 2 5332/2 5332/

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. fast dipyrone (DSC visiblenear visible near VNIRS (VNIRS oneclass one algorithm 1 3 PCA (PCA datadriven data driven DDSIMCA DD SIMCA (DD-SIMCA results 100 sensitivity specificity 001 0 01 0.01 00 0.0 0.

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%. (guaco diseases purposes borondoped boron doped electrode 1 0.1 01 0 (0. L1 L L- BrittonRobinson Britton Robinson solution, solution , solution) 177 77 +1.7 diffusion 154 54 1.5 153 15 3 15. mL1 mL mL- 020 20 0.2 062 62 0.6 mL1, 1, respectively lackoffit lack fit 95 95% analyzed highperformance high performance ttest t test 0. (0 17 7 +1. 5 1. 02 2 06 6 9 ( +1 +

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.