Márcia M. C. Ferreira

Basic validation procedures for regression models in QSAR and QSPR studies: theory and application

Four quantitative structure-activity relationships (QSAR) and quantitative structure-property relationship (QSPR) data sets were selected from the literature and used to build regression models with 75, 56, 50 and 15 training samples. The models were validated by leave-one-out crossvalidation, leave-N-out crossvalidation (LNO), external validation, y-randomization and bootstrapping. Validations have shown that the size of the training sets is the crucial factor in determining model performance, which deteriorates as the data set becomes smaller. Models from very small data sets suffer from the impossibility of being thoroughly validated, failure and atypical behavior in certain validations (chance correlation, lack of robustness to resampling and LNO), regardless of their good performance in leave-one-out crossvalidation, fitting and even in external validation. A simple determination of the critical Nin LNO has been introduced by using the limit of 0.1 for oscillations in Q2, quantified as the variation range in single LNO and two standard deviations in multiple LNO. It has been demonstrated that it is sufficient to perform 10 -25 y-randomization and bootstrap runs for a typical model validation. The bootstrap schemes based on hierarchical cluster analysis give more reliable and reasonable results than bootstraps relying only on randomization of the complete data set. Data quality in terms of statistical significance of descriptor -yrelationships is the second important factor for model performance.Variable selection that does not eliminate insignificant descriptor - yrelationships may lead to situations in which they are not detected during model validation, especially when dealing with large data sets.

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.

Quimiometria II: planilhas eletrônicas para cálculos de planejamentos experimentais, um tutorial

This work describes, through examples, a simple way to carry out experimental design calculations applying an spreadsheets. The aim of this tutorial is to introduce an alternative to sophisticated commercial programs that normally are too complex in data input and output. An overview of the principal methods is also briefly presented. The spreadsheets are suitable to handle different types of computations such as screening procedures applying factorial design and the optimization procedure based on response surface methodology. Furthermore, the spreadsheets are sufficiently versatile to be adapted to specific experimental designs.

Chemometric models for the quantitative descriptive sensory analysis of Arabica coffee beverages using near infrared spectroscopy

Mathematical models based on chemometric analyses of the coffee beverage sensory data and NIR spectra of 51 Arabica roasted coffee samples were generated aiming to predict the scores of acidity, bitterness, flavour, cleanliness, body and overall quality of coffee beverage. Partial least squares (PLS) were used to construct the models. The ordered predictor selection (OPS) algorithm was applied to select the wavelengths for the regression model of each sensory attribute in order to take only significant regions into account. The regions of the spectrum defined as important for sensory quality were closely related to the NIR spectra of pure caffeine, trigonelline, 5-caffeoylquinic acid, cellulose, coffee lipids, sucrose and casein. The NIR analyses sustained that the relationship between the sensory characteristics of the beverage and the chemical composition of the roasted grain were as listed below: 1 - the lipids and proteins were closely related to the attribute body; 2 - the caffeine and chlorogenic acids were related to bitterness; 3 - the chlorogenic acids were related to acidity and flavour; 4 - the cleanliness and overall quality were related to caffeine, trigonelline, chlorogenic acid, polysaccharides, sucrose and protein.

Planilha de validação: uma nova ferramenta para estimar figuras de mérito na validação de métodos analíticos univariados

VALIDATION SPREADSHEET: A NEW TOOL FOR ESTIMATING THE ANALYTICAL FIGURES OF MERIT FOR THE VALIDATION OF UNIVARIATE METHODS. This work presents the VALIDATION SPREADSHEET, a public domain tool that can be used to evaluate the figures of merit for univariate analytical methods. A real example of BTEX determination in environmental samples is included to illustrate its use. The spreadsheet has been developed for Excel® and Open Office®, and is available on the internet at http//lqta.iqm.unicamp.br.

Prediction of sensory properties of Brazilian Arabica roasted coffees by headspace solid phase microextraction-gas chromatography and partial least squares.

Volatile compounds in fifty-eight Arabica roasted coffee samples from Brazil were analyzed by SPME-GC-FID and SPME-GC-MS, and the results were compared with those from sensory evaluation. The main purpose was to investigate the relationships between the volatile compounds from roasted coffees and certain sensory attributes, including body, flavor, cleanliness and overall quality. Calibration models for each sensory attribute based on chromatographic profiles were developed by using partial least squares (PLS) regression. Discrimination of samples with different overall qualities was done by using partial least squares-discriminant analysis (PLS-DA). The alignment of chromatograms was performed by the correlation optimized warping (COW) algorithm. Selection of peaks for each regression model was performed by applying the ordered predictors selection (OPS) algorithm in order to take into account only significant compounds. The results provided by the calibration models are promising and demonstrate the feasibility of using this methodology in on-line or routine applications to predict the sensory quality of unknown Brazilian Arabica coffee samples. According to the PLS-DA on chromatographic profiles of different quality samples, compounds 3-methypropanal, 2-methylfuran, furfural, furfuryl formate, 5-methyl-2-furancarboxyaldehyde, 4-ethylguaiacol, 3-methylthiophene, 2-furanmethanol acetate, 2-ethyl-3,6-dimethylpyrazine, 1-(2-furanyl)-2-butanone and three others not identified compounds can be considered as possible markers for the coffee beverage overall quality.

Evalution of phenolic compounds in Brazilian propolis from different geographic regions.

Abstract Chemometrics has been shown quite efficient to uncover relationships between chemical composition of a sample and its geographical origin. Forty propolis samples originated from the the South and South East of Brazil were analyzed by HPLC and 18 compounds of interest were studied which included: caffeic, p-coumaric and ferulic acids, and some of their derivatives, pinobanksin, a derivative of kaempferol and five phenolic compounds (assigned as 3-prenyl-4-hydroxycinnamic acid (PHCA ); 2,2-dimethyl-6-carboxyethnyl-2H-l-benzopyran (DCBE); 3,5-diprenyl-4-hydroxycinnamic acid (DHCA ); compound E (still unknown) and 6-propenoic-2,2-dimethyl-8-prenyl-2H-l-benzopyran acid (DPB). Principal Component Analysis (PCA) indicated three different groups of propolis samples, having the same typical chromatogram, evaluated by HPLC. Samples from the South East group were rich in derivatives of kaempferol. Samples from the South group I had a high content of DPB compound, but a low concentration of kaempferol derivatives and of DCBEN compound. Samples from the South group II were characterized by a high concentration of DCBEN , DHCA , p-coum-aric and DPB compounds. Therefore, the identification of new compounds in Brazilian propolis can give useful information about the plant sources of a given geographic region.

LQTA-QSAR: A New 4D-QSAR Methodology

A novel 4D-QSAR approach which makes use of the molecular dynamics (MD) trajectories and topology information retrieved from the GROMACS package is presented in this study. This new methodology, named LQTA-QSAR (LQTA, Laboratório de Quimiometria Teórica e Aplicada), has a module (LQTAgrid) that calculates intermolecular interaction energies at each grid point considering probes and all aligned conformations resulting from MD simulations. These interaction energies are the independent variables or descriptors employed in a QSAR analysis. The comparison of the proposed methodology to other 4D-QSAR and CoMFA formalisms was performed using a set of forty-seven glycogen phosphorylase b inhibitors (data set 1) and a set of forty-four MAP p38 kinase inhibitors (data set 2). The QSAR models for both data sets were built using the ordered predictor selection (OPS) algorithm for variable selection. Model validation was carried out applying y-randomization and leave-N-out cross-validation in addition to the external validation. PLS models for data set 1 and 2 provided the following statistics: q(2) = 0.72, r(2) = 0.81 for 12 variables selected and 2 latent variables and q(2) = 0.82, r(2) = 0.90 for 10 variables selected and 5 latent variables, respectively. Visualization of the descriptors in 3D space was successfully interpreted from the chemical point of view, supporting the applicability of this new approach in rational drug design.

Dual amperometric biosensor device for analysis of binary mixtures of phenols by multivariate calibration using partial least squares

A simple and reliable method for rapid evaluation of mixtures of phenolic compounds (phenol/chlorophenol, cathecol/phenol, cresol/chlorocresol and phenol/cresol) using a dual amperometric device is described. This new approach is based on the difference between the sensitivity of laccase and tyrosinase for different phenolic compounds. A multichannel potentiostat was used to monitor simultaneously laccase- and tyrosinase-based biosensors, and the data were treated using the partial least squares (PLS) chemometric algorithm. This system showed an excellent efficiency for the resolution of the phenolic mixtures. For example, in the phenol/chlorophenol mixture it was studied the determination of individual species in a concentration range from 1.0×10 −6 to 10.0×10 −6 mol l −1 obtaining relative standard deviations of 3.5 and 3.1% for phenol and chlorophenol, respectively. The excellent correlation between the estimated and the real concentrations can also be observed by the correlation coefficients (0.9958 and 0.9981 for phenol and chlorophenol, respectively). These results show that proposed methodology can be successfully employed to the simultaneous determination of phenolic compounds in mixtures, even in more diluted solutions.

Relationships of the minerals and fatty acid contents in processed turkey meat products

Abstract In this paper, the chemical composition of a set of processed foods made of turkey meat, including meatball, blanquet, hamburger, smoked chest, ham, smoked ham, roule and frankfurter, are reported. Each product was analyzed for content of saturated fats, mono- and polyunsaturated fats, non-identified fats, calcium, iron, phosphorus, magnesium, potassium, sodium and zinc. In average, fatty acids are present in approximately equivalent percent concentrations, i.e. saturated:monounsaturated:polyunsaturated ≅1:1:1. Sodium, the major mineral ranged from 681 to 1327 mg per 100 g of processed meat. Iron and calcium concentration ranges were 0.4–2.2 and 3.0–43.6 mg/100 g, respectively. The results were analyzed by the multivariate techniques, hierarchical cluster analysis (HCA) and principal component analysis (PCA). It was shown that HCA can group all the samples, according to their types, and into some extent also according their basic composition (only dark meat, white meat, meat with shortening added and frankfurter as single cluster). On the other hand PCA could better expose the relationship between the products according to their fatty acids and mineral composition. PC 1 discriminates fatty/lean products, while PC 2 discriminates the frankfurters by its content of salts added, Ca and Fe from milk and soy, all added during processing, and finally PC 4 which discriminates the white/dark meat products through Zn concentration from dark meat.