Brazilian law forbids the addition of cheese whey in milk. However, adulteration with cheese whey is one of the most applied fraud due to its low cost. The detection of this fraud is the quantification of Caseinomacropeptide (CMP). The CMP is a constituent of the whey that can be used as adulteration marker. Thus, an analytical method capable of identifying CMP by Fourier Transform Infrared Spectra (FTIR) was developed using chemometrics methods. Firstly, we attempted to develop an exploratory analysis model by Hierarchical Grouping Analysis (HCA) and Principal Component Analysis (PCA) that indicated similarity between samples of raw milk and semi-skimmed milk. Moreover, in the PCA scores, it was possible to observe a tendency of separation between samples with different concentrations of CMP. Afterwards, multivariate regression models were used for Partial Least Squares (PLS), Partial Least Square with Interval Synergism (siPLS) and Supporting Machines with Least Squares (LS-SVM) to quantify the adulteration in different types of milk by Cheese serum through the CMP. All the models were then compared to each other and the results of the official method with Liquid Chromatography Tandem mass spectrometry (LCMS/MS) analysis used by the Ministry of Livestock and Supply (MAPA). The model LS-SVM, employing the full spectrum, obtained the best result compared to the other models (PLS and siPLS) to quantify the CMP in the milk samples.

During biodiesel storage, chemical reactions may occur, producing sludges. The aim of this study was to characterize the chemical and microbial composition of the sludge found in a biodiesel storage tank. The material was collected in a biodiesel production plant. The sludge chemical characterization was performed by Fourier transform infrared (FTIR) spectroscopy, flame atomic absorption spectroscopy (FAAS), gas chromatography (GC), and the microbial investigation used culture-dependent techniques. The deteriogenic potential of the native microbial community was evaluated using the sludge as a microbial inoculum in a 60 day experiment. The microbial growth, biodiesel degradation, pH alterations, and the detection of esters in the aqueous phase were evaluated. The chemical analysis indicated the sludge composition as fatty acids esters and metallic ions; sterols glycosides were not detected. Seven bacteria and five fungi species were obtained from the sludge. The microbial growth analysis indicated that the native community does not have high biodiesel deteriogenic capacity.

This article describes the development of a mobile colorimetric analysis tool. The application, called PhotoMetrix, employs the techniques of simple linear correlation for univariate analysis and principal components analysis (PCA) for multivariate exploratory analysis. The image data are captured by the main camera of the device and converted into red, green and blue (RGB) histograms. As regards the application, the iron determinations were performed in vitamin supplements (univariate module) and differentiation of banknotes was performed by PCA (multivariate module). For the iron determinations, three samples of vitamins at concentrations of 14, 40 and 50 mg of iron per tablet were tested and the results were not statistically significant (p > 0.05) compared to the reference method. The differentiation of banknotes was performed on Brazilian and Argentinean banknotes. The results showed clustering of the same types of banknotes, and through the loadings graph it was possible to observe the variables through the formation of clusters.

Tannins from trees are an abundant renewable resource. The use of tannins is safe for the environment and human health, making it possible to develop environmentally friendly products, such as adhesives, flocculant agents for water treatment, flame retardant foams and anticorrosives. In this study, a methodology was developed for the identification of six commercial tannin extracts (chestnut, valonea, tara, myrobalan, quebracho and black wattle) using digital images on mobile devices. The identification of the extracts was based on principal component analysis using the PhotoMetrix® mobile application. In the evaluation of the principal components, it was possible to define the separation between the six classes of extracts by color evaluation, according to the extract origin. In the analysis of PC1 × PC2, black wattle was very close to quebracho and chestnut was close to valonea; these extracts are chemically similar. The methodology developed for the analysis of tannin extracts obeys the principles of green chemistry requiring no reagents, and is fast, non-destructive and inexpensive.

This study deals with B, S, and P determination in mineral fertilizer and S in commercial sulfur-formulation using inductively coupled plasma optical emission spectrometry. According to multivariate optimization using a Doehlert matrix, the analytes extraction in fertlizer can be conducted in open flask and heating on hot plate or in closed flask in microwave oven. However, when S is present in its elemental form in the fertilizer, the sample must be decomposed in closed flask in microwave oven for accurate S quantification. The uncertainties of the method were estimated according to the bottom-up approach, which were 7.1, 4.3, and 7.8% for B, S and P, respectively. The main sources of uncertainty identified were the intermediate precision, accuracy, regression of the calibration curve and errors originated from volumetric flasks and pipettes. Satisfactory results were obtained in the analysis of certified reference materials and sample from inter-laboratory programs as well as S recovery in a commercial elemental sulfur sample.

In this work, Doehlert experimental design was used to optimize the Transesterification Double Step Process (TDSP) method of methyl soybean oil biodiesel production which starts with a basic catalysis followed by an acidic catalysis. The conversion values were calculated from NMR spectra. Response surface was used to show the results of the interactions between the variables. This experimental design evaluated variables like catalyst and alcohol amount for the basic catalysis and time and temperature for the acidic catalysis. According to results obtained after Doehlert design application the alcohol amount was the main factor that influenced on the basic catalysis but for the acidic catalysis both time and temperature are important and their effects are opposite. It resulted on excellent conversions for both steps obtaining for the basic catalysis about 100% when was used like optimal conditions catalyst amount equal to 0.40 g and volume of methanol equal to 60 mL and for the acidic catalysis about 99% when was used like optimal conditions temperature of 65 °C and 90 minutes for reaction time.

Um método alternativo para quantificação de sulfametoxazol (SMZ) e trimetoprima (TMP), empre gando espectrometria por refletância difusa no infra vermelho por transformada de Fourier (DRIFTS) e regressão por mínimos quadrados parciais (PLS) foi desenvolvido. Regressão por mínimos quadrados parciais por intervalo (iPLS) e por sinergismo de intervalos (siPLS) foram aplicadas para selecionar as faixas espectrais que produziram modelos com menores erros na previsão, em comparação ao modelo que emprega todo o espectro. Quinze comprimidos de formulações comerciais e quarenta e nove amostras sintéticas foram usados. As faixas de concentração consideradas foram de 400-900 mg g-1 para o SMZ e de 80-240 mg g-1 para a TMP. Os espectros por refletância difusa no infravermelho por transformada de Fourier (DRIFTS) foram adquiridos na faixa 600-4000cm-1 com resolução de 4 cm-1. O presente procedimento foi comparado com cromatografia líquida de alta eficiência (HPLC). Os resultados obtidos para os erros quadráticos médios de previsão (RMSEP), durante a validação dos modelos para as amostras de sulfametoxazol (SMZ) e trimetoprima (TMP) usando siPLS, demonstram que esta abordagem trata-se de uma técnica válida para análise quantitativa de formulações farmacêuticas. Os modelos de regressão, obtidos a partir dos intervalos selecionados pelo algoritmo, apresentaram menores erros quando comparados ao modelo PLS global. Para as melhores regiões selecionadas pelosiPLS, Os valores RMSEP de 13,03 mg g-1 para SMZ e de 4,88 mg g-1 para TMP foram obtidos a partir da seleção das melhores regiões espectrais pelo siPLS.

An alternative method for the quantification of sulphametoxazole (SMZ) and trimethoprim (TMP) using diffuse reflectance infrared Fourier-transform spectroscopy (DRIFTS) and partial least square regression (PLS) was developed. Interval Partial Least Square (iPLS) and Synergy Partial Least Square (siPLS) were applied to select a spectral range that provided the lowest prediction error in comparison to the full-spectrum model. Fifteen commercial tablet formulations and forty-nine synthetic samples were used. The ranges of concentration considered were 400 to 900 mg g-1SMZ and 80 to 240 mg g-1 TMP. Spectral data were recorded between 600 and 4000 cm-1 with a 4 cm-1 resolution by Diffuse Reflectance Infrared Fourier Transform Spectroscopy (DRIFTS). The proposed procedure was compared to high performance liquid chromatography (HPLC). The results obtained from the root mean square error of prediction (RMSEP), during the validation of the models for samples of sulphamethoxazole (SMZ) and trimethoprim (TMP) using siPLS, demonstrate that this approach is a valid technique for use in quantitative analysis of pharmaceutical formulations. The selected interval algorithm allowed building regression models with minor errors when compared to the full spectrum PLS model. A RMSEP of 13.03 mg g-1for SMZ and 4.88 mg g-1 for TMP was obtained after the selection the best spectral regions by siPLS.

The objective of this work was to develop a free access exploratory data analysis software application for academic use that is easy to install and can be handled without user-level programming due to extensive use of chemometrics and its association with applications that require purchased licenses or routines. The developed software, called Chemostat, employs Hierarchical Cluster Analysis (HCA), Principal Component Analysis (PCA), intervals Principal Component Analysis (iPCA), as well as correction methods, data transformation and outlier detection. The data can be imported from the clipboard, text files, ASCII or FT-IR Perkin-Elmer “.sp” files. It generates a variety of charts and tables that allow the analysis of results that can be exported in several formats. The main features of the software were tested using midinfrared and near-infrared spectra in vegetable oils and digital images obtained from different types of commercial diesel. In order to validate the software results, the same sets of data were analyzed using Matlab© and the results in both applications matched in various combinations. In addition to the desktop version, the reuse of algorithms allowed an online version to be provided that offers a unique experience on the web. Both applications are available in English.

Um método para determinação simultânea de ácido clavulânico (CA) e amoxicilina (AMO) foi desenvolvido utilizando a técnica de espectroscopia no infravermelho médio com transformada de Fourier acoplada ao acessório de reflexão total atenuada (ATR/FTIR). Utilizaram-se 27 e 8 amostras para os conjuntos de calibração e previsão, respectivamente. Os modelos de calibração foram desenvolvidos utilizando os algoritmos por mínimos quadrados parciais (PLS), PLS por intervalo (iPLS), PLS por sinergismo (siPLS) e PLS por exclusão (biPLS). Os melhores modelos foram aqueles que utilizaram o algoritmo biPLS. Obtiveram-se erro padrão relativo de previsão (RSEP) de 3,8 e 5,1% para CA e AMO, respectivamente. Os resultados obtidos pela metodologia proposta foram comparados com os obtidos por cromatografia líquida de alta eficiência (HPLC) e nenhuma diferença significativa foi observada. O método proposto utilizando ATR/FTIR associado a métodos de análise multivariados foi satisfatório para determinação de CA e AMO em produtos farmacêuticos.

A method for simultaneous determination of clavulanic acid (CA) and amoxicillin (AMO) was developed using Fourier transform mid infrared technique coupled with attenuated total reflectance (ATR/FTIR) accessory. 27 samples were used as calibration set and 8 samples were used for prediction set. Calibration models were developed using partial least squares (PLS), interval PLS (iPLS), synergy PLS (siPLS) and backward PLS (biPLS). Multiplicative scatter correction and the mean centering were used and produced the best models. Relative standard error of prediction (RSEP) of 3.8% for CA and 5.1% for AMO were obtained using biPLS algorithm for ATR/FTIR data. Results obtained by the proposed methodology were compared with those using high performance liquid chromatography (HPLC) and no significant differences were obtained. The proposed method using ATR/FTIR combined to multivariate analysis methods was suitable for the simultaneous determination of CA and AMO in commercial pharmaceutical products.

Least-squares support vector machines (LS-SVM) were used as an alternative multivariate calibration method for the simultaneous quantification of some common adulterants found in powdered milk samples, using near-infrared spectroscopy. Excellent models were built using LS-SVM for determining R², RMSECV and RMSEP values. LS-SVMs show superior performance for quantifying starch, whey and sucrose in powdered milk samples in relation to PLSR. This study shows that it is possible to determine precisely the amount of one and two common adulterants simultaneously in powdered milk samples using LS-SVM and NIR spectra.

In this work a fast method for the determination of the total sugar levels in samples of raw coffee was developed using the near infrared spectroscopy technique and multivariate regression. The sugar levels were initially obtained using gravimety as the reference method. Later on, the regression models were built from the near infrared spectra of the coffee samples. The original spectra were pre-treated according to the Kubelka-Munk transformation and multiplicative signal correction. The proposed analytical method made possible the direct determination of the total sugar levels in the samples with an error lower by 8% with respect to the conventional methodology.

Neste trabalho são apresentados modelos de regressão multivariada, empregando espectros por reflexão difusa no infravermelho com transformada de Fourier (DRIFTS), para análise quantitativa do sistema ternário formado pelos carbonatos de cálcio e magnésio e pelo óxido de magnésio. Através de um diagrama ternário, foram definidas as misturas para aquisição dos espectros por reflexão no infravermelho médio. Empregando-se a regressão por mínimos quadrados parciais (PLS), foram construídos modelos com os dados espectrais centrados na média e/ou escalados pela variância, utilizando o conjunto de espectros de calibração. Para selecionar os melhores modelos, foram comparados os valores de RMSEP (root mean square error of prediction). Os resultados comprovam que bons modelos de calibração multivariada para a determinação de carbonatos de cálcio e magnésio e de óxido de cálcio podem ser obtidos a partir dos espectros por reflexão no infravermelho. Estas determinações são particularmente úteis no estudo da decomposição térmica de rochas dolomíticas.

In the present work multivariate regression models were developed for the quantitative analysis of ternary systems using Diffuse Reflectance Infrared Fourier Transform Spectroscopy (DRIFTS) to determine the concentration in weight of calcium carbonate, magnesium carbonate and magnesium oxide. Nineteen spectra of standard samples previously defined in ternary diagram by mixture design were prepared and mid-infrared diffuse reflectance spectra were recorded. The partial least squares (PLS) regression method was applied to the model. The spectra set was preprocessed by either mean-centered and variance-scaled (model 2) or mean-centered only (model 1). The results based on the prediction performance of the external validation set expressed by RMSEP (root mean square error of prediction) demonstrated that it is possible to develop good models to simultaneously determine calcium carbonate, magnesium carbonate and magnesium oxide content in powdered samples that can be used in the study of the thermal decomposition of dolomite rocks.

A combinação da espectroscopia no infravermelho próximo (NIR) e calibração multivariada (método dos mínimos quadrados parciais - PLS) para a determinação do teor de proteína total em amostras de café cru, foi investigada. Os teores de proteína total foram inicialmente determinados usando-se como método de referência o de Kjeldhal, e, posteriormente foram construídos modelos de regressão a partir dos espectros na região do infravermelho próximo das amostras de café cru. Foram coletados 159 espectros das amostras de café cru utilizando um acessório de reflectância difusa, na faixa espectral de 4500 a 10000cm-1. Os espectros originais no NIR sofreram diferentes transformações e pré-tratamento matemático, como a transformação Kubelka-Munk; correção multiplicativa de sinal (MSC); alisamento (SPLINE); derivada primeira; média móvel e o pré-tratamento dos dados escalados pela variância. O método analítico proposto possibilitou a determinação direta, sem destruição da amostra, com obtenção de resultados rápidos e sem o consumo de reagentes químicos de forma a preservar o meio ambiente. O método proposto forneceu resultados com boa capacidade de previsão do teor de proteína total, sendo que os erros médios foram inferiores a 6,7%.

The combination of near infrared spectroscopy (NIR) and multivariate calibration using the partial least square - PLS method for the determination of the total protein level in raw coffee samples was investigated. The total protein levels were initially determined using the Kjeldhal method as the reference method. Regression models were built from the spectra in the NIR region of the raw coffee samples. Spectra of 159 samples were recorded, using an accessory of diffuse reflectance, in the range of 4500 and 10000cm-1 with 4cm-1 resolution. To the raw spectral data, different transformations and mathematical pretreatment such as Kubelka-Munk transformation; multiplicative sign correction (MSC); spline smoothing; first derivative and boxcar averaging were applied. The data was also preprocessed by scaling each column by its variance. The analytical method proposed is non invasive and the cost and time of analysis are very much reduced, making possible the fast and direct determination of the total protein content. The prediction error of protein levels given by the regression model were in average lower than 6.7%, indicating that this method is a good alternative for protein determination in raw coffee samples.