In this study, xiquexique (Pilosocereus gounellei) native endemic cactus from the Brazilian semiarid was used for the first time as a natural carbon source (“green precursor”) for the synthesis of highly fluorescent carbon dots (CDs). These CDs were successfully used to develop a fast, low-cost, eco-friendly fluorescence method for the determination of tannins in teas. This method was automatized employing a flow-batch system coupled to an inexpensive ultraviolet light-emitting diode (UV-LED) used as an excitation source and a cheap handheld spectrometer used as a detector. CDs were characterized by transmission electron microscopy (TEM), X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), UV-Vis, and fluorescence spectroscopy. The proposed method presented a linear range from 2.0-30.0 mg L-1, relative standard deviation (RSD) < 3.0%, limit of detection (LOD) = 0.102 mg L-1, and recovery rates from 90.5-109.1%. A paired t-test at a 95% confidence level indicated no statistically significant difference between the proposed and reference methods, revealing that it is a useful alternative for the determination of tannins.

An automated microsystem for sulfite determination in beverages was developed. It presents higher sampling throughput, lower chemical consumption and less waste generation than previous flow methods, while using the same p-rosaniline-formaldehyde-sulfite reaction. The sampling rate, limit of detection (LOD), and relative standard deviation (RSD) were estimated at 130 h-1, 80.0 µg L–1, and < 1.3% (n = 5), respectively. Recoveries ranged from 96.8 to 102.6%.

This paper proposes an active search method aimed at finding objects with optimal or near-optimal y-property values, on the basis of x-variables obtained by indirect, less costly methods. The proposed method progresses in a sequential manner, starting from a small subset of objects with known y-values. At each iteration, the K-nearest neighbour regression technique is employed to obtain estimates ŷ for the objects with unknown y-values. The object with best ŷ value is then subjected to a direct analysis procedure for evaluation of the y-property. Examples are presented with simulated data, as well as actual quantitative structure-activity relationship (QSAR) and near-infrared (NIR) spectrometry datasets. The QSAR and NIR case studies involve the search for maximal antidepressant activity in a set of arylpiperazine compounds and maximal pulp yield in a set of eucalyptus wood samples, respectively. In all these cases, the active search yielded results closer to the maximal y-value compared to the classical Kennard-Stone algorithm for object selection.

This paper investigates the use of an automatic system for preparation of gas mixtures in a multivariate calibration problem involving near-infrared (NIR) spectrometric analysis of natural gas. The automatic system is used to prepare calibration mixtures according to a Brereton experimental design, in order to exploit a suitable range of gas concentrations and thus avoid extrapolations in the predictions. These mixtures were employed to build partial-least-squares models for NIR determination of methane, ethane and propane, which are the major components of natural gas. Prediction performance was evaluated by using a separate set of prepared mixtures and natural gas samples with composition analyzed by gas chromatography, as well as a group of certified mixtures. The resulting root-mean-square errors of prediction (RMSEP) values for methane, ethane and propane (3.0, 0.9 and 1.2% mol mol-1, respectively) were approximately 10 times smaller than the corresponding calibration ranges, with correlations of 0.91, 0.96 and 0.86 between the predicted and reference values.

<p>This work reports the development of a fast chromatographic methodology for quantitation of two phytoestrogens: daidzein (DAI), and genistein (GEN), in river water samples. The proposed method is based on high performance liquid chromatography-diode array detection (HPLC-DAD) data, and multivariate curve resolution-alternative least square (MCR-ALS) second-order calibration. Initially, the method was evaluated analyzing a synthetic validation set; prepared based on a Taguchi design. Subsequently, the method was applied to predict the concentration of the phytoestrogens in spiked river water samples, previously pre-processed by solid phase extraction (SPE). By implementation of the present chromatographic methodology, a 50% reduction in operation time was achieved (from 7.00 to 3.25 min) when compared with previous work in the literature. Precision was achieved even in the presence of non-modeled constituents and strong background. Thus, the proposed method is a rapid and robust alternative for the quantitation of studied phytoestrogens.</p>

Este artigo propõe um método de espectrometria no infravermelho próximo (NIR) de reflectância difusa para a discriminação não-destrutiva de sementes de mamona das cultivares mais comumente empregadas nas plantações brasileiras (BRS Nordestina e BRS Paraguaçu). Para esta finalidade, duas técnicas de classificação são comparadas, o SIMCA (modelagem independente flexível por analogias de classe) e PLS-DA (análise discriminante por mínimos quadrados parciais). Ao aplicar a classificação SIMCA a um conjunto de teste contendo 150 sementes, os modelos para as classes BRS Nordestina e BRS Paraguaçu apresentaram valores de sensibilidade/especificidade de 0,91/0,99 e 0,71/1,00, respectivamente. Melhores resultados foram obtidos usando PLS-DA, que classificou corretamente todas as amostras de teste, proporcionando assim valores de sensibilidade e seletividade de 1,00. Estes resultados sugerem que o método proposto é promissor na identificação de genótipos de sementes de mamona, em lotes de sementes ou para fins de reprodução, antes de serem plantadas.

This article proposes a near-infrared (NIR) diffuse reflectance spectrometric method for non-destructive discrimination of castor seeds from the two cultivars most commonly employed in Brazilian plantations (BRS Nordestina and BRS Paraguaçu). For this purpose, two classification techniques are compared, namely SIMCA (soft independent modelling of class analogies) and PLS-DA (partial least squares discriminant analysis). By applying the SIMCA classifier to a test set comprising 150 seeds, the BRS Nordestina and BRS Paraguaçu class models yielded sensitivity/specificity values of 0.91/0.99 and 0.71/1.00, respectively. Better results were obtained by using PLS-DA, which correctly classified all test samples, i.e., yielded sensitivity and specificity values of 1.00. These findings suggest that the proposed method is a promising approach to identify castor seed genotypes, either in seed lots or for breeding purposes, prior to being planted.

Uma simplificação no SPA-LDA é proposta para contornar a necessidade de conjuntos de treinamento e validação separados. O número de graus de liberdade é empregado na função de custo para evitar sobreajuste do modelo. Três exemplos são apresentados: classificação de cafés, diesel e óleos vegetais empregando espectrometria UV-Vis, NIR e voltametria, respectivamente

A simplification in SPA-LDA is proposed to circumvent the need for separate training and validation sets. The number of degrees of freedom is employed in the cost function to avoid model overfitting. Three examples are presented: classification of coffee, diesel and vegetable oils by using UV-Vis spectrometry, NIR spectrometry and voltammetry, respectively.

Um analisador flow-batch é proposto para detecção de adulterações em bebidas destiladas (uísque, conhaque, cachaça, rum e vodca) usando espectrofotometria UV-Vis. O analisador foi empregado para diluir as amostras não-adulteradas e para simular adulterações com adição de 5 e 10% (v/v) de água, etanol ou metanol. Modelos de classificação SIMCA (soft independent modelling of class analogies) foram construídos empregando espectros de amostras adulteradas e não-adulteradas na faixa de 235-355 nm. Aplicando-se os modelos SIMCA a um conjunto de teste, todas as amostras adulteradas e não-adulteradas foram corretamente discriminadas com um nível de confiança de 95% e uma frequência analítica superior a 120 amostras por hora.

An automatic flow-batch analyzer is proposed for detection of adulteration in distilled spirits (whiskey, brandy, cachaça, rum and vodka) using UV-Vis spectrophotometry. The analyzer was employed to dilute the non-adulterated samples and to simulate adulteration with addition of 5 and 10% (v/v) of water, ethanol or methanol. SIMCA (soft independent modelling of class analogies) classification models were built using spectra of non-adulterated and adulterated samples in the region of 235-355 nm. By applying the SIMCA models to a test set, all adulterated and non-adulterated samples were correctly discriminated at a confidence level of 95% with an analytical throughput larger than 120 samples per hour.

The application of multivariate calibration techniques to multicomponent analysis by UV-VIS molecular absorption spectrometry is a powerful tool for simultaneous determination of several chemical species. However, when this methodology is accomplished manually, it is slow and laborious, consumes high amounts of reagents and samples, is susceptible to contaminations and presents a high operational cost. To overcome these drawbacks, a flow-batch analyser is proposed in this work. This analyser was developed for automatic preparation of standard calibration and test (or validation) mixtures. It was applied to the simultaneous determination of Cu2+, Mn2+ and Zn2+ in polyvitaminic and polymineral pharmaceutical formulations, using 4-(2-piridilazo) resorcinol as reagent and a UV-VIS spectrophotometer with a photodiode array detector. The results obtained with the proposed system are in good agreement with those obtained by flame atomic absorption spectrometry, which was employed as reference method. With the proposed analyser, the preparation of calibration and test mixtures can be accomplished about four hours, while the manual procedure requires at least two days. Moreover, it consumes smaller amounts of reagents and samples than the manual procedure. After the preparation of calibration and test mixtures, 60 samples h-1 can be carried out with the proposed flow-batch analyser.

Este artigo apresenta uma aplicação do método para determinação espectrofotométrica simultânea dos íons divalentes de cobre, manganês e zinco à análise de medicamento polivitamínico/polimineral. O método usa 4-(2-piridilazo) resorcinol (PAR), calibração multivariada e técnicas de seleção de variáveis e foi otimizado o empregando-se o algoritmo das projeções sucessivas (APS) e o algoritmo genético (AG), para escolha dos comprimentos de onda mais informativos para a análise. Com essas técnicas, foi possível construir modelos de calibração por regressão linear múltipla (RLM-APS e RLM-AG). Os resultados obtidos foram comparados com modelos de regressão em componentes principais (PCR) e nos mínimos quadrados parciais (PLS). Demonstra-se a partir do erro médio quadrático de previsão (RMSEP) que os modelos apresentam desempenhos semelhantes ao prever as concentrações dos três analitos no medicamento. Todavia os modelos RLM são mais simples pois requerem um número muito menor de comprimentos de onda e são mais fáceis de interpretar que os baseados em variáveis latentes.

The application of the method for the simultaneous spectrophotometric determination of the divalent ions of copper, manganese and zinc for analysis of a pharmaceutical formulation of polivitaminic/polimineral is reported. This method uses 4-(2-pyridylazo) resorcinol (PAR) and multivariate calibration and was optimized using the successive projections algorithm (SPA) and a genetic algorithm (GA) for choosing the best series of wavelengths for analysis. Thus the construction of calibration models based on multiple linear regression (MLR-SPA and MLR-GA respectively) was made possible. The results obtained were compared with models based on latent variables, principal component regression (PCR) and partial least square regression (PLS) through the criterion of the root mean square error of prediction (RMSEP). All the methods presented even performance but the RLM methods are simpler since they require a smaller number of wavelengths and are easier to interpret than those based on latent variables.