CONSTRUÇÃO DE UM DISPOSITIVO DE PRESSURIZAÇÃO A VÁCUO PARA A PREPARAÇÃO DE FASES ESTACIONÁRIAS MONOLÍTICAS VIA PROCESSO SOL-GEL

The filling of capillaries via the sol-gel process is growing. Therefore, this technical note focuses on disseminating knowledge acquired in the Group of Analytical Chemistry and Chemometrics over seven years working with monolithic stationary phase preparation in fused silica capillaries. We believe that the detailed information presented in this technical note concerning the construction of an alternative high pressurization device, used to fill capillary columns via the sol-gel process, which has promising potential for applications involving capillary electrochromatography and liquid chromatography in nano scale, may be enlightening and motivating for groups interested in developing research activities within this theme.

CHEMOSTAT, UM SOFTWARE GRATUITO PARA ANÁLISE EXPLORATÓRIA DE DADOS MULTIVARIADOS

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.

APPLICATION OF PARAFAC AND OPLS-DA ANALYSES ON HPLC FINGERPRINTS FOR THE CHARACTERIZATION OF Hibiscus sabdariffaCALYXES

The calyxes of Hibiscus sabdariffa are used in traditional medicine around the world. However, quality assurance protocols and chemical variability have not been previously analyzed. In the present study, chemical characterization of a set of samples of H. sabdariffa calyxes commercialized in Colombia was accomplished with the aim to explore the chemical variability among them. Chemometrics-based analyses on the data obtained from the HPLC-UV-DAD-derived profiles were then performed. Thus, the pre-processed single-wavelength data were subjected to principal component analysis (PCA). The PCA-derived results evidenced different groups which were well-correlated to the corresponding total phenolic and total anthocyanin contents. Multi-wavelength chromatographic (HPLC-UV-DAD surfaces) data were additionally examined via parallel factor analysis (PARAFAC) as data reduction method and the obtained loadings were subsequently submitted to PCA and orthogonal partial least squares discriminant analysis (OPLS-DA). Results were thus consistent with those from single-wavelength data. PCA loadings were employed to determine those chemical components responsible for the data variance and OPLS-DA model, constructed from PARAFAC loadings, and indicated differentiation according total anthocyanin contents among samples. The present chemometric analysis therefore demonstrated to be an excellent tool for differentiation of H. sabdariffacalyxes according to their chemical composition.

PROPOSTA EXPERIMENTAL DIDÁTICA PARA O ENSINO DE ANÁLISE DE COMPONENTES PRINCIPAIS

Principal component analysis (PCA) is a chemometric method that allows for the extraction of chemical information that would otherwise be impossible to determine. Teaching chemometrics to undergraduates can contribute to the overall professional development and training of new teachers, whose profiles have been gaining attention due to the current demand for data interpretation. In this study, a didactic experiment involving PCA is proposed. Spectrophotometry was used in the ultraviolet-visible (UV-Vis) region to assess the behavior of anthocyanins extracted from red cabbage at different pH values. The results suggest the possible separation of anthocyanin structures into three distinct groups, according to their chemical characteristics displayed in acid, neutral, and basic media. The objective is to develop educational materials targeted to undergraduate courses, which encompass a larger number of concepts and introduce instrumental techniques currently being employed in both academic research and the industrial sector. Specifically, the proposed experiment introduces concepts related to spectrophotometry in the UV-Vis range and the PCA chemometric method. The materials used are easily accessible, and UV-Vis spectroscopy equipment is less expensive in comparison with other spectroscopy methods.

Evaluation of phenolic compounds content and in vitro antioxidant activity of red wines produced from Vitis labrusca grapes

Wine production in the northern Curitiba, Paraná, Brazil, specifically the communes of Colombo and Almirante Tamandaré, is based mainly on the utilization of Vitis labrusca grapes var. Bordô (Ives). Total sugar content, pH, and total acidity were analyzed in red wine samples from 2007 and 2008 vintages following official methods of analysis. Moreover, total phenolic, flavonoid, and tannin contents were analyzed by colorimetric methodologies and the antioxidant activity was determined using the 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical methodology. Phenolic compounds were identified by high performance liquid chromatography. The total phenolic content of wine samples presented concentrations varying between 1582.35 and 2896.08 mg gallic acid.L-1 since the major part corresponds to flavonoid content. In these compounds' concentration range, a direct relationship between phenolic compounds content and levels of antioxidant activity was not observed. Among the identified phenolic compounds, chlorogenic, caffeic, and syringic acids were found to be the major components. Using three principal components, it was possible to explain 81.36% of total variance of the studied samples. Principal Components Analysis does not differentiate between vintages.

Characterization and classification of pequi trees (Caryocar brasiliense Camb.) based on the profile of volatile constituents using headspace solid-phase microextraction - gas chromatography - mass spectrometry and multivariate analysis

In order to determine the variability of pequi tree (Caryocar brasiliense Camb.) populations, volatile compounds from fruits of eighteen trees representing five populations were extracted by headspace solid-phase microextraction and analyzed by gas chromatography-mass spectrometry. Seventy-seven compounds were identified, including esters, hydrocarbons, terpenoids, ketones, lactones, and alcohols. Several compounds had not been previously reported in the pequi fruit. The amount of total volatile compounds and the individual compound contents varied between plants. The volatile profile enabled the differentiation of all of the eighteen plants, indicating that there is a characteristic profile in terms of their origin. The use of Principal Component Analysis and Cluster Analysis enabled the establishment of markers (dendrolasin, ethyl octanoate, ethyl 2-octenoate and β-cis-ocimene) that discriminated among the pequi trees. According to the Cluster Analysis, the plants were classified into three main clusters, and four other plants showed a tendency to isolation. The results from multivariate analysis did not always group plants from the same population together, indicating that there is greater variability within the populations than between pequi tree populations.

Visible-near infrared spectroscopy sensor for predicting curd and whey composition during cheese processing

The potential of visible-near infrared spectra, obtained using a light backscatter sensor, in conjunction with chemometrics, to predict curd moisture and whey fat content in a cheese vat was examined. A three-factor (renneting temperature, calcium chloride, cutting time), central composite design was carried out in triplicate. Spectra (300–1,100 nm) of the product in the cheese vat were captured during syneresis using a prototype light backscatter sensor. Stirring followed upon cutting the gel, and samples of curd and whey were removed at 10 min intervals and analyzed for curd moisture and whey fat content. Spectral data were used to develop models for predicting curd moisture and whey fat contents using partial least squares regression. Subjecting the spectral data set to Jack-knifing improved the accuracy of the models. The whey fat models (R = 0.91, 0.95) and curd moisture model (R = 0.86, 0.89) provided good and approximate predictions, respectively. Visible-near infrared spectroscopy was found to have potential for the prediction of important syneresis indices in stirred cheese vats.

Fractional order and non-linear system identification algorithms for biomedical applications

We discuss the modelling of dielectric responses of amorphous biological samples. Such samples are commonly encountered in impedance spectroscopy studies as well as in UV, IR, optical and THz transient spectroscopy experiments and in pump-probe studies. In many occasions, the samples may display quenched absorption bands. A systems identification framework may be developed to provide parsimonious representations of such responses. To achieve this, it is appropriate to augment the standard models found in the identification literature to incorporate fractional order dynamics. Extensions of models using the forward shift operator, state space models as well as their non-linear Hammerstein-Wiener counterpart models are highlighted. We also discuss the need to extend the theory of electromagnetically excited networks which can account for fractional order behaviour in the non-linear regime by incorporating nonlinear elements to account for the observed non-linearities. The proposed approach leads to the development of a range of new chemometrics tools for biomedical data analysis and classification.

Heteroscedastic controlled calibration model applied to analytical chemistry

In chemical analyses performed by laboratories, one faces the problem of determining the concentration of a chemical element in a sample. In practice, one deals with the problem using the so-called linear calibration model, which considers that the errors associated with the independent variables are negligible compared with the former variable. In this work, a new linear calibration model is proposed assuming that the independent variables are subject to heteroscedastic measurement errors. A simulation study is carried out in order to verify some properties of the estimators derived for the new model and it is also considered the usual calibration model to compare it with the new approach. Three applications are considered to verify the performance of the new approach. Copyright (C) 2010 John Wiley & Sons, Ltd.

The log-bimodal-skew-normal model. A geochemical application

The main objective of this paper is to study a logarithm extension of the bimodal skew normal model introduced by Elal-Olivero et al. [1]. The model can then be seen as an alternative to the log-normal model typically used for fitting positive data. We study some basic properties such as the distribution function and moments, and discuss maximum likelihood for parameter estimation. We report results of an application to a real data set related to nickel concentration in soil samples. Model fitting comparison with several alternative models indicates that the model proposed presents the best fit and so it can be quite useful in real applications for chemical data on substance concentration. Copyright (C) 2011 John Wiley & Sons, Ltd.

Skew-normal linear calibration: a Bayesian perspective

In this paper, we present a Bayesian approach for estimation in the skew-normal calibration model, as well as the conditional posterior distributions which are useful for implementing the Gibbs sampler. Data transformation is thus avoided by using the methodology proposed. Model fitting is implemented by proposing the asymmetric deviance information criterion, ADIC, a modification of the ordinary DIC. We also report an application of the model studied by using a real data set, related to the relationship between the resistance and the elasticity of a sample of concrete beams. Copyright (C) 2008 John Wiley & Sons, Ltd.

A chemometric study of the 5-HT1A receptor affinities presented by arylpiperazine compounds

Arylpiperazine compounds are promising 5-HT1A receptor ligands that can contribute for accelerating the onset of therapeutic effect of selective serotonin reuptake inhibitors. In the present work, the chemometric methods HCA, PCA, KNN, SIMCA and PLS were employed in order to obtain SAR and QSAR models relating the structures of arylpiperazine compounds to their 5-HT1A receptor affinities. A training set of 52 compounds was used to construct the models and the best ones were obtained with nine topological descriptors. The classification and regression models were externally validated by means of predictions for a test set of 14 compounds and have presented good quality, as verified by the correctness of classifications, in the case of pattern recognition studies, and b, the high correlation coefficients (q(2) = 0.76, r(2) = 0.83) and small prediction errors for the PLS regression. Since the results are in good agreement with previous SAR studies, we can suggest that these findings can help in the search for 5-HT1A receptor ligands that are able to improve antidepressant treatment. (c) 2007 Elsevier Masson SAS. All rights reserved.

Desenvolvimento de um biossensor potenciométrico, à base de soja, para a determinação de uréia

In this work, the objective in study was the development of a biossensor potencyometric for urea detection, starting from the extracted urease of soy grains. Initially, was made a chemometrics study, through a planning factorial 24, objectified to find great conditions for the extraction of the urease without its properties were affected. Starting from this study, the best conditions were determined for the obtaining of rich extracts in urease, allowing the biossensors making with good characteristics. These were made using a platinum electrode as transducer with the dispersed urease in chitosan head office and reticulated in glutaraldehyde vapor. The biossensors obtained presented a limit of urea detection the same to 0,33 mM and lineal strip between 0,33 and 3 mM of the substratum. The time of answer was considered loud, mainly, in low concentrations of the substratum, where it was taken about 5 minutes by analysis. For high concentrations that time was reduced for not more than one minute. The time of life was limited by the adherence of the enzymatic membrane to the transducer, but it was possible to maintain the biossensor with operation for one month with about 50 accomplished measures. Application of the biossensor for analyses of fertilizers to the urea base presented excellent result for a sample with few interfering, but it was different when the used fertilizer was originating from of a complex sample. Even so the label was not expressed the text of nitrogen it was totally coming of the urea. An evaluation of the kinetic parameters of the catalytic reaction of the biossensor showed coherence with the results exposed in the literature

Comparação entre metodologias de extração de HPAs em água utilizando um planejamento fatorial com quantificação no CG/EM

PAHs (Polycyclic Aromatic Hydrocarbons) are a group of organic substances which receive considerable attention because of the carcinogenic and mutagenic properties of some of them. It is therefore important to determine the PAHs in different environmental matrices. Several studies have shown the use of gas chromatography coupled to mass spectrometry as a technique for quantification of PAHs by presenting excellent detection limits. This study aimed to develop an analytical methodology for the determination of 16 PAHs listed by the USEPA, test two methods for extraction of PAHs in water from a 23 factorial design, quantify them through the analytical technique coupled to gas chromatography mass spectrometry (GC/MS) using the method developed, and finally apply the results in chemometrics. The sample was synthesized and subjected to tests of the 23 factorial design, which has the factors: the type of extraction technique (ultrasound and digester), the ratio solvent / sample (1:1 and 1:3) and the type of solvent (dichloromethane / hexane and acetone / dichloromethane). The responses of eight combinations of the factorial design were obtained from the quantification by external calibration in GC/MS. The quantification method was developed from an optimized adaptation of the USEPA Method 8270. We used the full scan mode as a way of acquiring the mass spectra of 16 PAHs. The time in which the samples were subjected to ultrasound was fixed at 10 min and held an investigation to establish the conditions of power and time in the digester. We had the best response in the investigation of the digester power of 100 watts and the time of six minutes. The factorial design of liquid-liquid extraction showed that the most representative factors were: the use of the digester as extraction technique, the ratio solvent / sample 1:1 and the use of a 1:1 mixture of dichloromethane / hexane as a solvent more suitable. These results showed that the 1:1 mixture of dichloromethane / hexane is an excellent mixture to recover the extraction of PAHs an aqueous sample using the microwave digester. The optimization of the method of separation, identification and quantification of PAHs in the GC/MS was valid for 16 PAHs present in each chromatogram of the samples