Optimization of wood flour acetylation by factorial design and partial least squares regression

Acetylation was performed to reduce the polarity of wood and increase its compatibility with polymer matrices for the production of composites. These reactions were performed first as a function of acetic acid and anhydride concentration in a mixture catalyzed by sulfuric acid. A concentration of 50%/50% (v/v) of acetic acid and anhydride was found to produced the highest conversion rate between the functional groups. After these reactions, the kinetics were investigated by varying times and temperatures using a 3² factorial design, and showed time was the most relevant parameter in determining the conversion of hydroxyl into carbonyl groups.

Classification of brain tumor extracts by high resolution ¹H MRS using partial least squares discriminant analysis

High resolution proton nuclear magnetic resonance spectroscopy (¹H MRS) can be used to detect biochemical changes in vitro caused by distinct pathologies. It can reveal distinct metabolic profiles of brain tumors although the accurate analysis and classification of different spectra remains a challenge. In this study, the pattern recognition method partial least squares discriminant analysis (PLS-DA) was used to classify 11.7 T ¹H MRS spectra of brain tissue extracts from patients with brain tumors into four classes (high-grade neuroglial, low-grade neuroglial, non-neuroglial, and metastasis) and a group of control brain tissue. PLS-DA revealed 9 metabolites as the most important in group differentiation: γ-aminobutyric acid, acetoacetate, alanine, creatine, glutamate/glutamine, glycine, myo-inositol, N-acetylaspartate, and choline compounds. Leave-one-out cross-validation showed that PLS-DA was efficient in group characterization. The metabolic patterns detected can be explained on the basis of previous multimodal studies of tumor metabolism and are consistent with neoplastic cell abnormalities possibly related to high turnover, resistance to apoptosis, osmotic stress and tumor tendency to use alternative energetic pathways such as glycolysis and ketogenesis.

Comparing the artificial neural network with parcial least squares for prediction of soil organic carbon and pH at different moisture content levels using visible and near-infrared spectroscopy

Visible and near infrared (vis-NIR) spectroscopy is widely used to detect soil properties. The objective of this study is to evaluate the combined effect of moisture content (MC) and the modeling algorithm on prediction of soil organic carbon (SOC) and pH. Partial least squares (PLS) and the Artificial neural network (ANN) for modeling of SOC and pH at different MC levels were compared in terms of efficiency in prediction of regression. A total of 270 soil samples were used. Before spectral measurement, dry soil samples were weighed to determine the amount of water to be added by weight to achieve the specified gravimetric MC levels of 5, 10, 15, 20, and 25 %. A fiber-optic vis-NIR spectrophotometer (350-2500 nm) was used to measure spectra of soil samples in the diffuse reflectance mode. Spectra preprocessing and PLS regression were carried using Unscrambler® software. Statistica® software was used for ANN modeling. The best prediction result for SOC was obtained using the ANN (RMSEP = 0.82 % and RPD = 4.23) for soil samples with 25 % MC. The best prediction results for pH were obtained with PLS for dry soil samples (RMSEP = 0.65 % and RPD = 1.68) and soil samples with 10 % MC (RMSEP = 0.61 % and RPD = 1.71). Whereas the ANN showed better performance for SOC prediction at all MC levels, PLS showed better predictive accuracy of pH at all MC levels except for 25 % MC. Therefore, based on the data set used in the current study, the ANN is recommended for the analyses of SOC at all MC levels, whereas PLS is recommended for the analysis of pH at MC levels below 20 %.

Least squares regression with errors in both variables: case studies

Analytical curves are normally obtained from discrete data by least squares regression. The least squares regression of data involving significant error in both x and y values should not be implemented by ordinary least squares (OLS). In this work, the use of orthogonal distance regression (ODR) is discussed as an alternative approach in order to take into account the error in the x variable. Four examples are presented to illustrate deviation between the results from both regression methods. The examples studied show that, in some situations, ODR coefficients must substitute for those of OLS, and, in other situations, the difference is not significant.

Inovatividade, envolvimento, atitude e experiência na adoção da compra on-line

Este trabalho procura analisar as inter-relações entre a inovatividade, o envolvimento, a atitude dentro do modelo Theory of Planned Behavior (TPB) decomposto desenvolvido na psicologia social, e a experiência com a Internet com o processo de adoção da compra pela internet. Foi elaborado um modelo integrativo que possibilitasse explicar a relação entre esses fatores e a compra pela internet, e foi desenvolvida uma pesquisa de campo considerando uma amostra não probabilística de estudantes. Foi utilizado o método multivariado de modelagem de equações estruturais, aplicado por meio da técnica Partial Least Squares (PLS) para a verificação, explicação e comparação das relações entre os construtos. Os resultados mostram que a intenção da compra pela internet é diretamente influenciada pela atitude e pela inovatividade, e a atitude é influenciada pelo envolvimento. Não foi encontrada relação entre a experiência com a internet e a compra pela internet.

Antecedentes da intenção de uso de sistemas de home broker sob a ótica dos investidores do mercado acionário

Na pesquisa aqui relatada, visa-se investigar os antecedentes da intenção de uso de sistemas de home broker sob a ótica dos investidores do mercado acionário. Para atingir esse objetivo, por meio de referencial teórico baseado em teorias de aceitação de sistemas de informação, difusão da inovação, confiança em ambientes virtuais e satisfação do usuário, foi elaborado um modelo teórico e foram propostas hipóteses de pesquisa. Por meio de técnicas de equações estruturais baseadas em Partial Least Squares (PLS), a partir de 152 questionários válidos, coletados via web survey junto a investidores do mercado acionário brasileiro, foram testados o modelo proposto e as hipóteses de pesquisa. Identificaram-se, assim, os fatores compatibilidade, utilidade percebida e facilidade de uso percebida como antecedentes estatisticamente significantes do fator satisfação do usuário com o sistema de home broker, o qual, por sua vez, teve efeito estatisticamente significante na intenção de uso do sistema. São apresentadas, ainda, as implicações acadêmicas e gerenciais do trabalho, assim como suas limitações e uma agenda de pesquisa para essa importante área do conhecimento.

Espectroscopia de infravermelho na determinação da textura do solo

A aplicação de técnicas espectroscópicas que utilizam a radiação infravermelha (NIRS-Near Infrared Spectroscopy e DRIFTS-Diffuse Reflectance Fourier Transformed Spectroscopy) na análise inorgânica do solo tem sido proposta desde a década de 1970, mas até os dias atuais são raros os métodos implementados rotineiramente no Brasil. Isso deve-se à dificuldade em construir modelos de calibração, por meio de métodos estatísticos multivariados, utilizando-se amostras reais de solo, de constituição complexa, que varia geograficamente e de acordo com o manejo. Por isso, os objetivos deste trabalho foram construir modelos de calibração em NIRS e DRIFTS para a quantificação das frações de argila e areia, em amostras de solos de classes diferentes - Latossolo Vermelho (predominante), Nitossolo, Argissolo Vermelho e Neossolo Quartzarênico - e avaliar qual dessas duas técnicas é mais adequada para essa finalidade, assim como a interferência do agrupamento de amostras e da seleção de variáveis espectrais na qualidade desses modelos. Para isso, valores de referência obtidos pelo método do densímetro, método largamente utilizado nos laboratórios de análise de solo, foram correlacionados com valores de absorbância em NIRS e DRIFTS pela ferramenta estatística PLS (Partial Least Squares), obtendo-se altos coeficientes de determinação (R²), de 0,95, 0,90 e 0,91 para argila, silte e areia, respectivamente, na validação externa. Isso confirma a aplicabilidade das técnicas espectroscópicas na análise granulométrica do solo para fins agrícolas. O agrupamento das amostras segundo a localização e a seleção de variáveis espectrais pouco influenciou na qualidade dos modelos. A técnica espectroscópica mais indicada para essa finalidade foi a DRIFTS.

Alternativas quimiométricas para a resolução de problemas analíticos clássicos: determinação espectrofotométrica de misturas de zircônio e háfnio

The determination of zirconium-hafnium mixtures is one of the most critical problem of the analytical chemistry, on account of the close similarity of their chemical properties. The spectrophotometric determination proposed by Yagodin et al. show not many practical applications due to the significant spectral interference on the 200-220 nm region. In this work we propound the use of a multivariate calibration method called partial least squares ( PLS ) for colorimetric determination of these mixtures. By using PLS and 16 calibration mixtures we obtained a model which permits determination of zirconium and hafnium with accuracy of about 1-2% and 10-20%, respectively. Using conventional univariate calibration the inaccuracy of the determination is about 10-25% for zirconium and above 57% for hafnium.

Quimiometria I: calibração multivariada, um tutorial

The aim of this work is to present a tutorial on Multivariate Calibration, a tool which is nowadays necessary in basically most laboratories but very often misused. The basic concepts of preprocessing, principal component analysis (PCA), principal component regression (PCR) and partial least squares (PLS) are given. The two basic steps on any calibration procedure: model building and validation are fully discussed. The concepts of cross validation (to determine the number of factors to be used in the model), leverage and studentized residuals (to detect outliers) for the validation step are given. The whole calibration procedure is illustrated using spectra recorded for ternary mixtures of 2,4,6 trinitrophenolate, 2,4 dinitrophenolate and 2,5 dinitrophenolate followed by the concentration prediction of these three chemical species during a diffusion experiment through a hydrophobic liquid membrane. MATLAB software is used for numerical calculations. Most of the commands for the analysis are provided in order to allow a non-specialist to follow step by step the analysis.

Aplicação de algoritmos genéticos na seleção de variáveis em espectroscopia no infravermelho médio: determinação simultânea de glicose, maltose e frutose

Genetic algorithm was used for variable selection in simultaneous determination of mixtures of glucose, maltose and fructose by mid infrared spectroscopy. Different models, using partial least squares (PLS) and multiple linear regression (MLR) with and without data pre-processing, were used. Based on the results obtained, it was verified that a simpler model (multiple linear regression with variable selection by genetic algorithm) produces results comparable to more complex methods (partial least squares). The relative errors obtained for the best model was around 3% for the sugar determination, which is acceptable for this kind of determination.

Aplicação de alguns modelos quimiométricos à espectroscopia de fluorescência de raios-X de energia dispersiva

The objective of this work was to accomplish the simultaneous determination of some chemical elements by Energy Dispersive X-ray Fluorescence (EDXRF) Spectroscopy through multivariate calibration in several sample types. The multivariate calibration models were: Back Propagation neural network, Levemberg-Marquardt neural network and Radial Basis Function neural network, fuzzy modeling and Partial Least Squares Regression. The samples were soil standards, plant standards, and mixtures of lead and sulfur salts diluted in silica. The smallest Root Mean Square errors (RMS) were obtained with Back Propagation neural networks, which solved main EDXRF problems in a better way.

Estudo QSPR sobre os coeficientes de partição: descritores mecânico-quânticos e análise multivariada

Quantum chemistry and multivariate analysis were used to estimate the partition coefficients between n-octanol and water for a serie of 188 compounds, with the values of the q 2 until 0.86 for crossvalidation test. The quantum-mechanical descriptors are obtained with ab initio calculation, using the solvation effects of the Polarizable Continuum Method. Two different Hartree-Fock bases were used, and two different ways for simulating solvent cavity formation. The results for each of the cases were analised, and each methodology proposed is indicated for particular case.

Estudo do coeficiente de partição octanol-água de bifenilas policloradas (PCBs) utilizando parâmetros topológicos

A model based on chemical structure was developed for the accurate prediction of octanol/water partition coefficient (K OW) of polychlorinated biphenyls (PCBs), which are molecules of environmental interest. Partial least squares (PLS) was used to build the regression model. Topological indices were used as molecular descriptors. Variable selection was performed by Hierarchical Cluster Analysis (HCA). In the modeling process, the experimental K OW measured for 30 PCBs by thin-layer chromatography - retention time (TLC-RT) has been used. The developed model (Q² = 0,990 and r² = 0,994) was used to estimate the log K OW values for the 179 PCB congeners whose K OW data have not yet been measured by TLC-RT method. The results showed that topological indices can be very useful to predict the K OW.

Modelo de calibração da concentração de metilmetacrilato em solução aquosa utilizando espectroscopia de absorção no ultravioleta

Dilutions of methylmetacrylate ranging between 1 and 50 ppm were obtained from a stock solution of 1 ml of monomer in 100 ml of deionised water, and were analyzed by an absorption spectrophotometer in the UV-visible. Absorbance values were used to develop a calibration model based on the PLS, with the aim to determine new sample concentrations. The number of latent variables used was 6, with the standard errors of calibration and prediction found to be 0,048 ml/100 ml and 0,058 ml/100 ml. The calibration model was successfully used to calculate the concentration of monomer released in water, where complete dentures were kept for one hour after polymerization.

Utilização de regressão multivariada para avaliação espectrofotométrica da demanda química de oxigênio em amostras de relevância ambiental

In this work, a partial least squares regression routine was used to develop a multivariate calibration model to predict the chemical oxygen demand (COD) in substrates of environmental relevance (paper effluents and landfill leachates) from UV-Vis spectral data. The calibration models permit the fast determination of the COD with typical relative errors lower by 10% with respect to the conventional methodology.