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CIENTOMETRIC PROFILE OF THE CHEMOMETRICS IN BRAZIL. The interest of researchers in understanding the stage of development of specific areas of knowledge is a way to understand their scientific maturity and their capacity for consolidation. This “popularization” of the knowledge of researchers working in a specific area of knowledge also allows a greater ability to recognize where science is produced and which actors participate in this effort from a series of evaluation parameters, indicative of the productivity of researchers through the number of articles published and the consistency of production and its relationship with the impact of this in the scientific community through citations of articles. Chemometrics, a science that relates measurements obtained in chemical systems or processes through the application of statistical and mathematical methods, an area created 50 years ago, still does not have a study evaluating the evolution of the area in qualitative and quantitative terms. In this sense, this article aims to present data from the profile of Brazilian scientific production in Chemometrics, as well as the training of human resources, in its scientometric aspects to allow an understanding of the dynamics and contemporary reality of the area. In terms of national scientific production is growing proportionally faster than the rest of the world, placing Brazil among the leading producers of knowledge in the area. Researchers in Chemometrics working in Brazil not only publish a significant number of articles, but these publications have a large number of citations, in addition to the training of human resources spread throughout Brazil, distributed among most graduate courses in chemistry, but not restricted to this area. Finally, the area of Chemometrics, which is about fifty years old, is mature, with numbers of publications and training of human resources, but it is noted that a plateau has not been reached in these two numbers, because the critical mass of researchers working in the area is still increasing.

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.

Medicinal plants have been used and studied for ages, from very old registers to modern ethnopharmacology, which encompasses analytical chemistry, foods, and pharmacy. Based on international norms and governmental organizations of health, phytomedicine-for example, herbal drugs-needs to guarantee the quality control of products and identify contaminants, biomarkers, and chemical profiles, among other issues. In this sense, is necessary to develop advanced analytical methods that show interesting possibilities and obtain a great amount of data. In order to treat the data, a set of mathematical and statistical procedures named chemometrics is necessary. In terms of herbal drugs, chemometric tools may be used to identify the following in plants: parts, development stages, processing, geographic origin, authentication, and chemical markers. This review describes applications of chemometric pattern recognition tools to analyze herbal drugs in different conditions associated with analytical methods in the last six years (2015-2020).

Sugarcane spirits and cachaça are distilled beverages derived from the fermentation of sugarcane juice. The production of these spirits has significant influence on the economy of several regions in Brazil, being the third most consumed distilled beverage in the world. To ensure the safety for human consumption and also to add value to these products, it is imperative to apply quality control techniques. The complexity of food matrices along with the fact that the currently used instrumental techniques have several disadvantages, such as being expensive, laborious and time-consuming, have turned research attention to multivariate analysis techniques. In consequence, chemometric techniques have been applied in laboratories around the world aiming at data reduction, pattern recognition, cluster analysis, classification and quantification of data. This article provides an overview of the application of analytical techniques with nonsupervised and supervised pattern recognition methods for the analysis of sugarcane spirits samples. Assessments discussed include promising results for the discrimination among samples, verification of adulteration, tendencies in sensorial characteristics as well as relationships between chemical information and the geographical origins.

Aspidosperma pyrifolium Mart. is an endemic plant from Brazilian northeastern, which is widely used in folk medicine to treat dermatitis, gastritis, and malaria due to its anti-inflammatory activity. Thus, the aim of this work was to characterize by analytical techniques A. pyrifolium in different particle sizes to use in the production of tea. For characterization, thermal analysis, optical microscopy, and near-infrared spectroscopy (NIR) techniques were used. After sieving, five samples, named AP01 (> 355 μm), AP02 (≥ 180 μm), AP03 (≥ 155 μm), AP04 (≥ 75 μm), and AP05 (≥ 38 μm), were obtained. Thermogravimetry curves showed three steps of decomposition, with different percentages of mass loss. The second step of decomposition showed the highest mass loss for the five samples. DTA curves of five samples showed a first endothermic peak at 91.70, 96.22, 96.29, 91.09, and 86.32 °C for AP01, AP02, AP03, AP04, and AP05, respectively. Decomposition of all particles occurred from 200 °C. The activation energy obtained through kinetic models showed significant differences between the two methodologies employed. NIR spectra were treated using chemometric tools, including principal component analysis and two PC were necessary to properly separate the samples. The analytical techniques used in this study allowed us to properly characterize the powders and could be used in the production of tea.

Cachaça is a sugarcane-derived alcoholic spirit exclusively produced in Brazil. It can be aged in barrels made from different types of wood, similar to other distilled beverages. The choice of wood type promotes different effects on color, flavor, aroma and consequently the price of cachaça, favoring fraudulent activities. This paper proposes the simultaneous identification of different wood types in aged cachaças and their adulterations with wood extracts using a digital-image based methodology employing color histograms obtained from digital images associated with pattern recognition methods, without any sample preparation step. Linear Discriminant Analysis, coupled with Successive Projections Algorithm for variable selection (SPA-LDA), obtained the best results, reaching accuracy, sensitivity, and specificity rates higher than 90.0% in the test set. This can be a rapid and reliable tool to prevent fraudulent labeling; ensuring that what is on the label reflects the quality of aged cachaças, affording security to consumers and regulatory agencies.

The "lolo" stands out among the most used inhalant drugs in Brazil. This drug is a non-specific blend of organic solvents, traditionally composed of ether and chloroform. Reports in the literature and forensic practice have revealed changes in the composition profile of this drug, based on availability of acquisition. This diversity has an effect on the efficiency of the preliminary tests used to detect illicit substances in situations that require rapid response time from the criminal investigations, such as arrests in the act. Considering the diversity of volatile substances with potential use as inhalant drugs and the limited detection abilities of preliminary exams routinely used by forensic laboratories, this present work applied NIR spectroscopy associated with chemometric models to detect the presence of organic solvents in samples of "lolo". Initially, the chemical profile of the seized samples was surveyed in the geographic region of study (Paraiba State, Brazilian northeast), and from the observation of the prevalent substances, classification models were produced using samples made in the laboratory and samples from real apprehensions. Then, an analysis protocol was developed, based on SIMCA models, to detect the predominant solvents in the regional composition profile (dichloromethane, trichloroethene and chloroform). The proposed analysis protocol obtained an overall accuracy of 94.7% in detecting halogenated hydrocarbons in suspect samples and 100% accuracy in characterizing the composition of samples composed exclusively of the studied halogenated hydrocarbons and their binary mixtures. Considering that the colorimetric tests used in the routine of forensic laboratories do not detect many components, the proposed method was technically and economically viable in preliminary tests for samples seized as suspicious of being "lolo".

This paper proposes an adaptation of the Fisher's discriminability criterion (named here as discriminant power, DP) for choosing principal components (obtained from Principal Component Analysis, PCA), which will be used to construct supervised Linear Discriminant Analysis (LDA) models for solving classification problems of food data. The proposed PCA-DP-LDA algorithm was then applied to (i) simulated data, (ii) classify soybean oils with respect to expiration date, and (iii) identify cachaca adulteration with wood extracts that simulated aging. For comparison, PCA-DP-LDA was evaluated against conventional PCA-LDA (based on explained variance) and Partial Least Squares-Discriminant Analysis (PLS-DA). Among them, PCA-DP-LDA achieved the most parsimonious and interpretable results, with similar or better classification performance. Therefore, the new algorithm can be considered a good alternative to the already well-established discriminant methods, being potentially applied where the discriminability of the principal components may not follow the same behavior of the explained variance.

The Croton argyrophyllus leaf essential oil has anti-inflammatory, antioxidant, cytotoxic among other activities. However, there are chemical composition variations in the literature. This work reports the first study of the intraspecific chemical variation of C. argyrophyllus population from Pernambuco state, Brazil. The essential oils of nine specimens (OCA1 to OCA9) were analyzed by GC/MS and NIR to identify the chemical compositions and to verify the similarities between the analyzed samples. These analyses resulted in the identification of bicyclogermacrene (mean 38.42 %), (Z)-caryophyllene (mean of 14.06 %), epi-longipinanol (mean of 9.78 %) and germacrene B (mean of 9.1 %) as the major constituents, as well as the same chemical markers for all oil samples. However, these are different to those that were previously registered in the literature for C. argyrophyllus essential oil. The data obtained from the analysis by NIR spectroscopy were treated by PCA and HCA and showed similarities in the chemical samples' profile. By statistical analyses three clusters were obtained: OCA1-6, OCA7-8 and OCA9. All these groups were potentially active against Staphylococcus aureus. However, the OCA7-8 group was the most active.

Enterococcus faecalis infections represent a health concern, mainly in oral diseases, in which treatments with chlorhexidine solution (0.2%) are often used; however, it presents high toxicity degree and several side effects. Based on this, the use of natural products as an alternative to treatment has been explored. Nonetheless, plant extracts have poor organoleptic characteristics that impair theirs in natura use. Therefore, this work aimed to evaluate the analytical profile, biological activity, and cytotoxicity in vitro of S. brasiliensis-loaded chitosan microparticles (CMSb) produced using different aspersion flow rates. The analytical fingerprint was obtained by FTIR and NIR spectra. Principal components analysis (PCA) was used to verify the similarity between the samples. The crystallinity degree was evaluated by X-ray diffraction (XRD). Phytochemical screening (PS) was performed to quantify phytocompounds. Antimicrobial activity was evaluated by minimum inhibitory concentration (MIC). Antibiofilm activity and bactericidal kinetics against E. faecalis (ATCC 29212 and MB 146-clinical isolated) were also assessed. The hemolytic potential was performed to evaluate the cytotoxicity. Data provided by FTIR, NIR, and PCA analyses revealed chemical similarity between all CMSb. Furthermore, the results from XRD analysis showed that the obtained CMSb present amorphous characteristic. Tannins and polyphenols were accurately quantified by the PS, but methodology limitations did not allow the flavonoid quantification. The low hemolytic potential assay indicates that all samples are safe. Antimicrobial assays revealed that CMSb were able to inhibit not only the E. faecalis ATCC growth but also the biofilm formation. Only one CMSb sample was able to inhibit the clinical strain. These results highlighted the CMSb antimicrobial potential and revealed this system as a promising product to treat infections caused by E. faecalis.

The largest uranium-phosphate deposit in Brazil also contains considerable levels of rare earth elements (REEs), which allows for the co-mining of these three ores. The most common methods for REE determination are time-consuming and demand complex sample preparation and use of hazardous reagents. Thus, the development of a safer and faster method to predict REEs in soil could aid in the assessment of these elements. We investigated the efficiency of near-infrared (NIR) spectroscopy to predict REEs in the soil of the uranium-phosphate deposit of Itataia, Brazil. We collected 50 composite topsoil samples in a well-distributed sampling grid along the deposit. The NIR measures in the soils ranged from 750 to 2500 nm. Three partial least squares regressions (PLSR) were selected to calibrate the spectra: full-spectrum partial least squares (PLS), interval partial least squares (iPLS), and successive projections algorithms for interval selection in partial least squares (iSPA-PLS). The concentrations of REEs were measured by inductively coupled plasma optical emission spectroscopy (ICP-OES). In addition to raw spectral data, we also used spectral pretreatments to investigate the effects on prediction results: multiplicative scatter correction (MSC), Savitzky-Golay derivatives (SG), and standard normal variate transformation (SNV). Positive results were obtained in PLS for La and ΣLREE using MSC pretreatment and in iSPA-PLS for Nd and Ce using raw data. The accuracy of the measurements was related to the REE concentration in soil; i.e., elements with higher concentrations tended to present more accurate results. The results obtained here aim to contribute to the development of NIR spectroscopy techniques as a tool for mapping the concentrations of REEs in topsoil.

Food analysis covers aspects of quality and detection of possible frauds to ensure the integrity of the food. The arsenal of analytical instruments available for food analysis is broad and allows the generation of a large volume of information per sample. But this instrumental information may not yet give the desired answer; it must be processed to provide a final answer for decision making. The possibility of discarding non-informative and/or redundant signals can lead to models of better accuracy, robustness, and chemical interpretability, in line with the principle of parsimony. Thus, in this tutorial review, we cover aspects of variable selection in food analysis, including definitions, theoretical aspects of variable selection, and case studies showing the advantages of variable selection-based models concerning the use of a wide range of non-informative and redundant instrumental information in the analysis of food matrices.

Ximenia americana L. is a plant of the Olacaceae family, used in the treatment of infectious diseases. Dry extracts are API’s of great interest for the pharmaceutical industry and have been used as final and intermediate products, resulting in different pharmaceutical forms. The aim of this study is to evaluate the compatibility of dry extract of X. americana with pharmaceutical excipients used in the solid forms. The extract was obtained by spray dryer, and binary mixtures of the dry extract and pharmaceutical excipients were analyzed. The studies were obtained using thermal analysis, X-ray powder diffraction and optical microscopy. With the data obtained from DSC curves, matrices for hierarchical cluster analysis (HCA) were made. The X-ray diffraction analysis showed changes in the mixtures’ profile with corn starch, lactose and magnesium stearate. Thermal behavior of the mixtures showed incompatibilities between the extract and lactose, corn starch and magnesium stearate. This study shows the importance of using instrumental analytical techniques in the early stages of development of herbal medicine to ensure the effectiveness, safety and quality of the final product.

The use of biocompatible polymers such as Hydroxypropylmethylcellulose (HPMC), Hydroxyethylcellulose (HEC), Carboxymethylcellulose (CMC), and Carbopol in solid formulations results in mucoadhesive systems capable of promoting the prolonged and localized release of Active Pharmaceutical Ingredients (APIs). This strategy represents a technological innovation that can be applied to improving the treatment of oral infections, such as oral candidiasis. Therefore, the aim of this study was to develop a tablet of Ximenia americana L. from mucoadhesive polymers for use in the treatment of oral candidiasis. An X. americana extract (MIC of 125 &mu, g&middot, mL&minus, 1) was obtained by turbolysis at 50% of ethanol, a level that demonstrated activity against Candida albicans. Differential Thermal Analysis and Fourier Transform Infrared Spectroscopy techniques allowed the choice of HPMC as a mucoadhesive agent, besides polyvinylpyrrolidone, magnesium stearate, and mannitol to integrate the formulation of X. americana. These excipients were granulated with an ethanolic solution 70% v/v at PVP 5%, and a mucoadhesive tablet was obtained by compression. Finally, mucoadhesive strength was evaluated, and the results demonstrated good mucoadhesive forces in mucin disk and pig buccal mucosa. Therefore, the study allowed a new alternative to be developed for the treatment of buccal candidiasis, one which overcomes the inconveniences of common treatments, costs little, and facilitates patients&rsquo, adhesion.

Studies regarding physical parameters and Rare Earth Elements (REEs) in soils are essential for agriculture and raw material extraction. The determination of such parameters is costly and time-consuming. On the other hand, Near-Infrared Spectroscopy (NIRS) is a low cost, non-destructive, and fast alternative to predict REEs and physical properties in soils. Moreover, the specific wavelengths or intervals correlated to target properties are not clearly identified in the spectra, drawing attention to using the variable selection algorithm to resolve this problem. In this sense, this study aimed to develop and test the bio-inspired variable selection method known as Firefly intervals Partial Least Square Regression (FF-iPLSR). This heuristic approach is based on the attractiveness between fireflies as swarm behavior when searching for food. The performance of the FF-i-PLSR was evaluated using six soil profiles from the Brazilian semiarid region using raw data and the preprocessing MSC, SNV, baseline adjusts, and Savitzky-Golay derivative. The following variables were quantified: sand, silt, clay, Th, and total REE. Comparatively, four chemometrics methods were used: full-spectrum PLS, PLS based on variable intervals (iPLS), and the Successive Projections Algorithm for iPLS (iSPA-PLS). The proposed algorithm for variable interval selection obtained a well-adjusted and more parsimonious chemometric model, mainly for silt, sand, and clay, with lower LV’s and variable intervals than the comparative models. These results indicated the robustness of the proposed algorithm. The models’ quality parameters showed that FF-iPLS achieved low average values for RMSEP, bias, and REP. Thus, the proposed algorithm of variables interval selection FF-iPLS is an interesting tool for analyzing environmental matrices such as soil. However, more studies are necessary to evaluate the algorithm’s efficiency using other samples and environmental conditions.

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.

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.

This paper proposes a new interval selection approach for PLS-DA modelling, which is developed as an extension of the recently introduced iSPA-PLS method for multivariate calibration. The proposed iSPA-PLS-DA algorithm is tested in two case studies concerning the classification of five types of vegetable oils employing square-wave voltammetry and the classification of five species of bacteria (Escherichia coli, Enterococcus faecalis, Streptococcus salivarius, Streptococcus oralis, and Staphylococcus aureus) using digital images. For comparison, the iPLS-DA algorithm for interval selection is also employed, in standard and backward modes. In both case studies, iSPA-PLS-DA provided correct classification rates larger than or equal to those obtained by PLS-DA using all variables and iPLS-DA with single or multiple intervals.

This study aims to identify the biodiesel feedstock (cottonseed, sunflower, corn or soybean oil) in biodiesel/diesel blends using digital images and chemometric methods. For this purpose, colour histograms (extracted from digital images) coupled with supervised pattern recognition techniques: Soft Independent Modelling of Class Analogy (SIMCA), Partial Least Squares Discriminant Analysis (PLS-DA) and the Successive Projections Algorithm for variable selection associated with Linear Discriminant Analysis (SPA-LDA) were used. SPA-LDA coupled with intensity histograms provided better results by selecting 12 variables alone, achieving only one error of classification in the external validation (test) set. Thus, the proposed methodology presents a noteworthy eco-friendly approach for identifying the biodiesel feedstock in biodiesel/diesel blends using a simple, fast, inexpensive and non-destructive analytical tool.

Herbal medicines currently represent an important part of the world pharmaceutical market, which shows growing interest in the use of herbal medicines. However, the production of such medicines involves a complex series of steps, which determine the production viability and the quality of the final product. Ximenia americana L. is a plant occurring in several regions of the world, with well-known and applied medicinal properties. Thus, the aim of this work was to develop and evaluate the physical and physical-chemical quality of tablets produced with X. americana L. extract. The extract was spray-dried from a hydroethanolic extractive solution and characterized as to its phytochemical composition. The chemical marker was determined and quantified using validated chromatographic methods. These methods indicated the presence of gallic acid at a concentration of 1.61 mg g(-1). Formulations were proposed and analyzed for their flow and compaction properties. The best formulation was used to obtain a batch of tablets, which was evaluated for its quality characteristics and showed to be within the pharmacopoeial specifications for average weight, hardness, friability, and disintegration time. The dissolution profile of the tablets produced was obtained, showing the release of about 70% of the vegetable extract content within 30 minutes. Results showed that it was possible to obtain herbal tablets containing a high content of vegetal extract by direct compression, developing a rapid process of formulation and production and guaranteeing the quality characteristics of the final product.

In this work, a new approach is proposed to verify the differentiating characteristics of five bacteria (Escherichia coli, Enterococcus faecalis, Streptococcus salivarius, Streptococcus oralis, and Staphylococcus aureus) by using digital images obtained with a simple webcam and variable selection by the Successive Projections Algorithm associated with Linear Discriminant Analysis (SPA-LDA). In this sense, color histograms in the red-green-blue (RGB), hue-saturation-value (HSV), and grayscale channels and their combinations were used as input data, and statistically evaluated by using different multivariate classifiers (Soft Independent Modeling by Class Analogy (SIMCA), Principal Component Analysis-Linear Discriminant Analysis (PCA-LDA), Partial Least Squares Discriminant Analysis (PLS-DA) and Successive Projections Algorithm-Linear Discriminant Analysis (SPA-LDA)). The bacteria strains were cultivated in a nutritive blood agar base layer for 24 h by following the Brazilian Pharmacopoeia, maintaining the status of cell growth and the nature of nutrient solutions under the same conditions. The best result in classification was obtained by using RGB and SPA-LDA, which reached 94 and 100 % of classification accuracy in the training and test sets, respectively. This result is extremely positive from the viewpoint of routine clinical analyses, because it avoids bacterial identification based on phenotypic identification of the causative organism using Gram staining, culture, and biochemical proofs. Therefore, the proposed method presents inherent advantages, promoting a simpler, faster, and low-cost alternative for bacterial identification.

article i nfo The successive projections algorithm (SPA) is aimed at selecting a subset of variables with small multi- collinearity and suitable prediction power for use in Multiple Linear Regression (MLR). The resulting SPA-MLR models have advantages in terms of simplicity and ease of interpretation as compared to latent-variable models, such as Partial-Least-Squares (PLS). However, PLS tends to be less sensitive to instru- mental noise. The present paper proposes an extension of SPA to combine the noise-reduction properties of PLS with the possibility of discarding non-informative variables in SPA. For this purpose, SPA is modified in order to select intervals of variables for use in PLS. The proposed iSPA-PLS algorithm is evaluated in two case studies involving near-infrared spectrometric analysis of wheat and beer extract samples. As compared to full-spectrum PLS, the resulting iSPA-PLS models exhibited better performance in terms of both cross-validation and external prediction. On the other hand, iSPA-PLS and SPA-MLR presented similar cross-validation performance, but the iSPA-PLS models clearly outperformed SPA-MLR in the external pre- diction. Such results indicate thatiSPA-PLS may be more robust with respect to differences between the ex- ternal prediction set and the calibration set used in the cross-validation procedure.

Germano Veras, Gean B. Costa, Jessika Pequeno de Freitas, Ana Claudia Dantas de Medeiros, Adriano Araujo Gomes and David Douglas de Sousa Fernandes Departamento de Quimica, Centro de Ciencias e Tecnologia, Universidade Estadual da Paraiba, 58.429–500 Campina Grande—PB, Brazil. E-mail: germano.veras@pq.cnpq.br Programa de Pos-Graduacao em Ciencias Farmaceuticas, Universidade Estadual da Paraiba, 58.429–500 Campina Grande—PB, Brazil Departamento de Quimica, Universidade Federal da Paraiba, 58.051–970, Joao Pessoa—PB, Brazil Programa de Pos-Graduacao em Ciencias Agrarias, Universidade Estadual da Paraiba, 58.429–500 Campina Grande—PB, Brazil

In this work, rapid and non-destructive methodology is proposed for screening of biodiesel/diesel blends with respect to the base oil, using near infrared spectroscopy and multivariate data analysis, since for both pure biodiesel and blends, the biodiesel/diesel are targets for tampering. Blends of diesel with cotton, sunflower and soybean oils were employed in this study. Two approaches were evaluated in the building of the classification model, using full-spectrum Soft Independent Modelling of Class Analogy (SIMCA), and Principal Component Analysis–Linear Discriminant Analysis (PCA–LDA). The other approaches were the use of variable selection employing Genetic Algorithm (GA), Successive Projection Algorithm (SPA) and Stepwise all coupled with the LDA model. The results showed which preprocessed NIR spectra and chemometrics are a viable alternative the conventional methods that involve the consumption of large volumes of reagents. Multivariate data analysis methods using selected variables showed a better performance than the methods using a full spectrum.

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.

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 2.4 μg L-1. Fil: Montemurro, Milagros. Universidad Nacional del Litoral; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; Argentina Fil: Pinto, Licarion. Universidade Federal da Paraíba; Brasil Fil: Véras, Germano. Universidade Estadual da Paraíba; Brasil Fil: de Araújo Gomes, Adriano. Universidade Federal da Paraíba; Brasil Fil: Culzoni, Maria Julia. Universidad Nacional del Litoral; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; Argentina Fil: Ugulino de Araújo, Mario Cesar. Universidade Federal Da Paraíba; Brasil Fil: Goicoechea, Hector Casimiro. Universidad Nacional del Litoral; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; Argentina

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.

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.

The titration by using the perchloric acid as titrant and malachite green as indicator is the classical method for the determination of metronidazole, 2-(2-methyl-nitroimidazol-1-yl) ethanol, in drugs. However, this procedure is slow, laborious and consumes great amount of reagents and samples. Moreover, it requires careful manipulation because the reagents cause irritation in the skin and in the eyes. To overcome these drawbacks an automatic system is proposed. It uses three-way solenoids valves, an open chamber and it exploits concentration gradients generated into the chamber in a multicommutated way. As the proposed system embodies the characteristics of flow-batch systems and of multicommutation techniques in flow, it is here named flow-batch-multicommutated titrator. The system is fully controlled by microcomputer running software written in LabView 5.1 graphic language. It processes 60 samples per hour with relative standard deviation smaller than 2.3% (n = 6) and in each titration it consumes 2.5 and 1.5 ml of sample and titrant, respectively. By applying the paired t-test, no statistic differences at the 95% probability level between the results of the proposed system and classical titration were observed. © 2004 Elsevier B.V. All rights reserved.

Several procedures for the determination of clinical–biochemical parameters are based on commercial kits supplied with only one standard solution because linear calibration graphs are involved. When these procedures are carried out in flow analysis non-linearity of the analytical graph may be observed due to time restrictions. Since at least three standard solutions are then needed, direct implementation of commercial kits in flow injection analysers can be hindered. To overcome these drawbacks, a computer controlled flow injection system exploiting concentration gradients and requiring only one standard solution is proposed for non-linear calibration. Limitations due to non-linearity of the analytical graph were overcome using a dye solution to calibrate the concentration gradient of the injected sample and standard solution. As an application, the spectrophotometric kinetic determination of total protein in blood serum based on commercial kits was selected. The system handled 60 samples per hour and required 17 μl and 1.0 ml of sample and biuret reagent, respectively, per determination. No baseline drift was observed during 8 h operating periods. Within the 40–80 g l −1 range, the overall relative standard deviation of results was estimated as 3.0% ( n = 5). The paired t -test was used and no statistic differences at the 95% probability level between the results for the reference and the proposed procedures was found.

A monosegmented flow system involving sample and titrant sequential injections and the establishment of a tandem plug between two air bubbles is proposed for automated titrations. The end point is found after successive variations of the sample and titrant volumes, determined by an algorithm based on the one-dimensional optimization method of Fibonacci. The set up includes a multi-port solenoid valve with four inlets and one outlet. The feasibility of the system was demonstrated in the spectrophotometric determination of the total acidity in vinegar with NaOH and phenolphthalein used as the titrant and indicator, respectively. The sample throughput was ca. 30 h-1 and consumption of the sample and titrant was about 0.7 ml. The results were in agreement with the expected values, with relative errors lower than ±0.4%. After 12 successive analyses of typical samples with a 47.1 - 53.4 total acidity, expressed as g l-1 acetic acid, the relative standard deviations of the results were estimated within the 1.2 - 2.1% range.

An automated flow-rate meter easy to construct and operate is described. This is contralled by a micracomputer connected to an analog/digitai converter interface and by a contrai software. It uses two acrylic flow cells, each comprising a bicolor light-emitting diode anda phototransistor piaced on opposite sides. The performance of this flow-rate meter was evaiuated by appiying it to three flow manifoids: one monosegmented flow titration system by binary sampling; one conventionai monosegmented flow system, and one singie-iine FIA system. The FIA system flow rate was assessed by means of a Vanderslice's expression. The time range of measurement of the flow rates was of 3.5-19.5 seconds and relative errars of 0.2-2.6% as well as relative stanCorrespondence to: Mario Cesar U. Araujo. © 1999 John Wiley & Sons, Inc. CCC 0895-7533/99/020097-08 dard deviations fram 0.4 to 3.1 % were attained. The flow-rate meter allows one to optimize precision and velocity of measurements just by changing one of its parameters. Moreover, the flow-rate meter can be used with flow systems, therefore ailowing one to perform on-line measurements, which facilitates a quick and effective contrai of the channeis flow rate. © 1999 John Wiley & Sons, Inc. Lab Robotics and Automation 11: 97-104, 1999

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.

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. 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.

A method for detecting adulterations of biodiesel/diesel blends (B5) with soybean oil using UV-Vis spectrometry is proposed. The study involves 90 samples comprising B5 blends with and without the addition of soybean oil (0.5 to 2.5% v/v). Suitable discrimination was achieved by using SIMCA (soft independent modeling of class analogy), KNN (K-nearest neighbors), PLS-DA (partial least squares discriminant analysis) and SPA-LDA (linear discriminant analysis with spectral variables selected by the successive projections algorithm) classifiers.

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.