Germano Véras

Classification of biodiesel using NIR spectrometry and multivariate techniques.

This article describes the classification of biodiesel samples using NIR spectroscopy and chemometric techniques. A total of 108 spectra of biodiesel samples were taken (being three samples each of four types of oil, cottonseed, sunflower, soybean and canola), from nine manufacturers. The measurements for each of the three samples were in the spectral region between 12,500 and 4000 cm(-1). The data were preprocessed by selecting a spectral range of 5000-4500 cm(-1), and then a Savitzky-Golay second-order polynomial was used with 21 data points to obtain second derivative spectra. Characterization of the biodiesel was done using chemometric models based on hierarchical cluster analysis (HCA), principal component analysis (PCA) and soft independent modeling of class analogy (SIMCA) elaborated for each group of biodiesel samples (cotton, sunflower, soybean and canola). For the HCA and PCA, the formation of clusters for each group of biodiesel was observed, and SIMCA models were built using 18 spectral measurements for each type of biodiesel (training set), and nine spectral measurements to construct a classification set (except for the canola oil which used eight spectra). The SIMCA classifications obtained 100% accurate identifications. Using this strategy, it was feasible to classify biodiesel quickly and nondestructively without the need for various analytical determinations.

Determination of biodiesel content in biodiesel/diesel blends using NIR and visible spectroscopy with variable selection.

This work is concerned of evaluate the use of visible and near-infrared (NIR) range, separately and combined, to determine the biodiesel content in biodiesel/diesel blends using Multiple Linear Regression (MLR) and variable selection by Successive Projections Algorithm (SPA). Full spectrum models employing Partial Least Squares (PLS) and variables selection by Stepwise (SW) regression coupled with Multiple Linear Regression (MLR) and PLS models also with variable selection by Jack-Knife (Jk) were compared the proposed methodology. Several preprocessing were evaluated, being chosen derivative Savitzky-Golay with second-order polynomial and 17-point window for NIR and visible-NIR range, with offset correction. A total of 100 blends with biodiesel content between 5 and 50% (v/v) prepared starting from ten sample of biodiesel. In the NIR and visible region the best model was the SPA-MLR using only two and eight wavelengths with RMSEP of 0.6439% (v/v) and 0.5741 respectively, while in the visible-NIR region the best model was the SW-MLR using five wavelengths and RMSEP of 0.9533% (v/v). Results indicate that both spectral ranges evaluated showed potential for developing a rapid and nondestructive method to quantify biodiesel in blends with mineral diesel. Finally, one can still mention that the improvement in terms of prediction error obtained with the procedure for variables selection was significant.

Classificação periódica: um exemplo didático para ensinar análise de componentes principais

A dataset of chemical properties of the elements is used herein to introduce principal components analysis (PCA). The focus in this article is to verify the classification of the elements within the periodic table. The reclassification of the semimetals as metals or nonmetals emerges naturally from PCA and agrees with the current SBQ/IUPAC periodic table. Dataset construction, basic preprocessing, loading and score plots, and interpretation have been emphasized. This activity can be carried out even when students with distinct levels of formation are together in the same learning environment.

A portable, inexpensive and microcontrolled spectrophotometer based on white LED as light source and CD media as diffraction grid.

A portable, microcontrolled and low-cost spectrophotometer (MLCS) is proposed. The instrument combines the use of a compact disc (CD) media as diffraction grid and white light-emitting diode (LED) as radiation source. Moreover, it employs a phototransistor with spectral sensitivity in visible region as phototransductor, as well as a programmable interrupt controller (PIC) microcontroller as control unit. The proposed instrument was successfully applied to determination of food colorants (tartrazine, sunset yellow, brilliant blue and allura red) in five synthetics samples and Fe(2+) in six samples of restorative oral solutions. For comparison purpose, two commercial spectrophotometers (HP and Micronal) were employed. The application of the t-paired test at the 95% confidence level revealed that there are not significant differences between the concentration values estimated by the three instruments. Furthermore, a good precision in the analyte concentrations was obtained by using MLCS. The overall relative standard deviation (R.S.D.) of each analyte was smaller than 1.0%. Therefore, the proposed instrument offers an economically viable alternative for spectrophotometric chemical analysis in small routine, research and/or teaching laboratories, because its components are inexpensive and of easy acquisition.

Highly sensitive quantitation of pesticides in fruit juice samples by modeling four-way data gathered with high-performance liquid chromatography with fluorescence excitation-emission detection.

A study regarding the acquisition and analytical utilization of four-way data acquired by monitoring excitation-emission fluorescence matrices at different elution time points in a fast HPLC procedure is presented. The data were modeled with three well-known algorithms: PARAFAC, U-PLS/RTL and MCR-ALS, the latter conveniently adapted to model third-order data. The second-order advantage was exploited when analyzing samples containing uncalibrated components. The best results were furnished with the algorithm U-PLS/RTL. This fact is indicative of both no peak time shifts occurrence among samples and high colinearity among spectra. Besides, this latent-variable structured algorithm is capable of better handle the need of achieving high sensitivity for the analysis of one of the analytes. In addition, a significant enhancement in both predictions and analytical figures of merit was observed for carbendazim, thiabendazole, fuberidazole, carbofuran, carbaryl and 1-naphtol, when going from second- to third-order data. LODs obtained were ranged between 0.02 and 2.4μgL(-1).

Digital image-based classification of biodiesel.

This work proposes a simple, rapid, inexpensive, and non-destructive methodology based on digital images and pattern recognition techniques for classification of biodiesel according to oil type (cottonseed, sunflower, corn, or soybean). For this, differing color histograms in RGB (extracted from digital images), HSI, Grayscale channels, and their combinations were used as analytical information, which was then statistically evaluated using Soft Independent Modeling by Class Analogy (SIMCA), Partial Least Squares Discriminant Analysis (PLS-DA), and variable selection using the Successive Projections Algorithm associated with Linear Discriminant Analysis (SPA-LDA). Despite good performances by the SIMCA and PLS-DA classification models, SPA-LDA provided better results (up to 95% for all approaches) in terms of accuracy, sensitivity, and specificity for both the training and test sets. The variables selected Successive Projections Algorithm clearly contained the information necessary for biodiesel type classification. This is important since a product may exhibit different properties, depending on the feedstock used. Such variations directly influence the quality, and consequently the price. Moreover, intrinsic advantages such as quick analysis, requiring no reagents, and a noteworthy reduction (the avoidance of chemical characterization) of waste generation, all contribute towards the primary objective of green chemistry.

Classificação de biodiesel na região do visível

Classification of biodiesel by oilseed type using pattern recognition techniques is described. The spectra of the samples were performed in the Visible region, requiring noise removal by use of a first derivative by the Savitzky-Golay method, employing a second-order polynomial and a window of 21 points. The characterization of biodiesel was performed using HCA, PCA and SIMCA. For HCA and PCA methods, one can observe the separation of each group of biodiesel in a spectral region of 405-500 nm. SIMCA model was used in a test group composed of 28 spectral measurements and no errors are obtained.

Using near infrared spectroscopy to classify soybean oil according to expiration date.

A rapid and non-destructive methodology is proposed for the screening of edible vegetable oils according to conservation state expiration date employing near infrared (NIR) spectroscopy and chemometric tools. A total of fifty samples of soybean vegetable oil, of different brands andlots, were used in this study; these included thirty expired and twenty non-expired samples. The oil oxidation was measured by peroxide index. NIR spectra were employed in raw form and preprocessed by offset baseline correction and Savitzky-Golay derivative procedure, followed by PCA exploratory analysis, which showed that NIR spectra would be suitable for the classification task of soybean oil samples. The classification models were based in SPA-LDA (Linear Discriminant Analysis coupled with Successive Projection Algorithm) and PLS-DA (Discriminant Analysis by Partial Least Squares). The set of samples (50) was partitioned into two groups of training (35 samples: 15 non-expired and 20 expired) and test samples (15 samples 5 non-expired and 10 expired) using sample-selection approaches: (i) Kennard-Stone, (ii) Duplex, and (iii) Random, in order to evaluate the robustness of the models. The obtained results for the independent test set (in terms of correct classification rate) were 96% and 98% for SPA-LDA and PLS-DA, respectively, indicating that the NIR spectra can be used as an alternative to evaluate the degree of oxidation of soybean oil samples.

Um fotômetro de fluxo para análises clínicas a base de um diodo emissor de luz bicolor

The construction and evaluation of an inexpensive flow photometer for clinical analysis, using a bicolour LED and a phototransistor adapted for tubular flow cell, are described. The instrument presents some new features such as: automatic zero, electronic calibration and peak-hold signal. When compared with a classical photometer, it is simpler and has the advantages of a flow analysis system: lower volumes of reagents and samples, lower levels of contamination, shorter time for analysis and lower analysis costs. The instrument was used in the determination of the constituents in blood samples. The results obtained agree with those obtained by a classical photometer and the precision was better.

Evaluation of compatibility between Schinopsis brasiliensis Engler extract and pharmaceutical excipients using analytical techniques associated with chemometric tools

Compatibility studies comprise an important step in pre-formulation since they allow the identification of the excipients most compatible with herbal extracts from different analytical techniques. The objective of this work is to evaluate the compatibility between the nebulized extract of S. brasiliensis Engler with pharmaceutical excipients using analytical techniques associated with chemometric tools. The extract was nebulized through aspersion and produced from the hydroalcoholic extract of the bark of S. brasiliensis Engler. Binary mixtures were produced in various proportions using the following pharmaceutical excipients: starch, microcrystalline cellulose (Avicel® 101 and 102), lactose, magnesium stearate, PVP K-30 and talc. The samples were analyzed by optical microscopy, differential scanning calorimetry and X-ray diffraction (XRD). With the data obtained from DSC curves, matrices for hierarchical cluster analysis (HCA) and principal component analysis (PCA) were made. Using microscopy, an amorphous formation and/or crystalline components could be seen. In DSC curves, as well as in PCA and HCA analyses, possible interactions were identified with starch, lactose and magnesium stearate. This was confirmed by XRD. The starch showed the greatest interaction. The results indicate that the DSC technique associated with chemometric tools contributed to a better interpretation of compatibility studies and that microcrystalline cellulose, PVP K-30 and talc were the most compatible excipients in relation to the extract.