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Costa N.,EuroResinas | Amaral S.,EuroResinas | Alvim R.,EuroResinas | Nogueira M.,EuroResinas | And 2 more authors.
Journal of Applied Polymer Science | Year: 2013

The molar ratios of formaldehyde (F) to urea (U) of three resin formulations in the range from 0.90 to 1.49 have been analyzed by means of Attenuated Total Reflection-Fourier Transform Infrared (ATR-FTIR) spectroscopy and Fourier Transform-Near-Infrared (FT-NIR) spectroscopy. Application of Principal Component Analysis (PCA) to the spectra (MIR and NIR) allowed to separate them according to the molar ratio and to distinguish between two groups of resins. Soft Independent Modeling of Class Analogy (SIMCA) allowed classification of new resin samples with high model distances between the classes. Partial Least Squares Regression (PLS-R) models based on MIR (NIR) spectra resulted in high coefficients of determination (R2) values, low errors, and high residual prediction deviations (RPD). To confirm the reproducibility of the process and to carefully evaluate twice all multivariate analysis results obtained, different batches of resins have been prepared to have an additional independent sample set. The number of samples required for MIR and possible applications of MIR and NIR spectroscopy in this context including limitations have been discussed. © 2012 Wiley Periodicals, Inc.

Bingham D.D.,Loughborough University | Bingham D.D.,Bradford Institute for Health Research | Varela-Silva M.I.,Loughborough University | Ferrao M.M.,University of Coimbra | And 7 more authors.
American Journal of Human Biology | Year: 2013

Objectives: Childhood obesity is a public health concern in Portugal. Socio-demographic and behavioral factors are highly associated with obesity but are not clearly understood. This article aims to update the prevalence of overweight and obesity in Portuguese children and to explore the influence and risks of socio-demographic factors and behavioral factors. Methods: A cross-sectional study of Portuguese children aged 3-10 years from all 18 mainland districts took place between March 2009 and January 2010. The sample was composed by 17,136 children, 3-10 years of age (8,455 boys; 8,681 girls). Height, weight, and other anthropometric measurements were obtained by trained technicians. Body mass index (BMI) was calculated along with other anthropometric variables. Data analyses took place between April and September 2012. The overweight/obesity classification was established by age-and sex-specific BMI cut-off points as defined by the International obesity task force (IOTF). Parents completed questionnaires about socio-demographic and behavioral characteristics of the family. Results: Almost 28% of the Portuguese children were overweight or obese (19.7% overweight; 8.2% obese). Prevalence was greater in girls than in boys. Logistic regression models found that the odds of childhood obesity were significantly affected by biological, socio-demographic, and behavioral factors. Conclusions: The protective factors against childhood overweight/obesity in this sample of Portuguese children are: (i) being male; (ii) having been breastfeed; (iii) having been born from mothers who did not smoke during pregnancy; (iv) engaging in little sedentary behaviors (TV, PC, and playing electronic games); (iv) performing at least 1 h of moderate physical activity every day; and (v) having parents with higher educational levels who also have their BMI within the healthy ranges. Am. J. Hum. Biol. 25:733-742, 2013. © 2013 Wiley Periodicals, Inc.

Schwanninger M.,University of Natural Resources and Life Sciences, Vienna | Rodrigues J.C.,Tropical Research Institute of Portugal | Gierlinger N.,University of Natural Resources and Life Sciences, Vienna | Hinterstoisser B.,University of Natural Resources and Life Sciences, Vienna
Journal of Near Infrared Spectroscopy | Year: 2011

Partial least squares (PLS) regressions were carried out to establish a mathematical correlation between the wet-laboratory reference values and the Fourier transformed near infrared spectra. The wavenumber range resulting from the investigations in Part 1 of this series was examined in detail, evaluating several data pre-processing methods. The data set was spilt into a cross-validation set and a test set to validate each model. Then the data set first used for cross-validation was used as a test set and vice versa. The coefficients of determination and the errors were very similar, but the number of PLS vectors varied widely, depending on the pre-processing method used. Applying the evaluation step used in Part 1, namely the prediction of lignin content of 732 additional spectra (366 wood samples with unknown lignin content) revealed which pre-processing method gave the most consistent results. The most appropriate model was found with the underlying assumption that it will cover a wide range of the natural variability of spruce wood and thus be applicable to as many samples as possible. The results of the models of cross-validation and test-set validation were compared to the results of a model calculated with all spectra (CV) and thus comprising a higher variability of the wood spectra. It is shown that the three models were equal and that the final model with the following parameters is highly qualified for prediction: wavenumber range 6102 cm-1-5762 cm-1, data pre-processing method 1st Der-MSC, number of PLS vectors (rank) = 2, r2 =0.926 root mean square error of estimation (RMSEE) = 0.24%, root mean square error of cross-validation (RMSECV) = 0.25%, an estimated root mean square error of prediction (RMSEP) = 0.25% with a corrected estimated RMSEP = 0.22%, bias = 0.00038 and a residual prediction deviation (RPD) = 3.7. These results confirm the importance of careful validation. © IM Publications LLP 2011.

Schwanninger M.,University of Natural Resources and Life Sciences, Vienna | Rodrigues J.C.,Tropical Research Institute of Portugal | Gierlinger N.,University of Natural Resources and Life Sciences, Vienna | Hinterstoisser B.,University of Natural Resources and Life Sciences, Vienna
Journal of Near Infrared Spectroscopy | Year: 2011

Although it might be thought that the determination of lignin content in wood by near infrared (NIR) spectroscopy is well known and has been used for several years, model statistics (mainly errors of prediction) found in the literature recommend further study. It is shown that partial least squares regression (PLS-R) models can be improved, namely the number of PLS vectors and the error of prediction can be substantially decreased by careful selection of the combination of wavenumber range(s) and pre-processing methods and validation of the models. To cover a wide range of the natural variability, the total lignin content of 200 Norway spruce wood samples was determined by wet-laboratory chemical methods. From the same milled samples Fourier transform near infrared (FT-NIR) spectra were recorded using a NIR fibre-optic probe. NIR bands, property weighting spectra and correlation coefficients were used to pre-select convenient wavenumber ranges. PLS regressions were carried out to establish a mathematical correlation between the data sets of wet-laboratory chemical methods and the FT-NIR spectra, leading to a number of "good" models with similar coefficients of determination [r2] > 0.90 and root mean square errors of cross-validation (RMSECV) below 0.3%. External validation of the models also gave r2 > 0.90 and root mean square errors of prediction (RMSEP) below 0.3%. As several models showed similar statistics, a further simple step, called evaluation, was introduced to assist in being able to make a decision about what model should be used. Therefore, in addition, FT-NIR spectra of another 366 wood samples were measured to evaluate the pre-selected combinations of wavenumber ranges and pre-processing methods. With this simple additional step (evaluation) the model with the highest predictability could be selected easily. © IM Publications LLP 2011.

Schwanninger M.,University of Natural Resources and Life Sciences, Vienna | Rodrigues J.C.,Tropical Research Institute of Portugal | Fackler K.,Vienna University of Technology
Journal of Near Infrared Spectroscopy | Year: 2011

Near infrared (NIR) spectra of wood and wood products contain information regarding their chemical composition and molecular structure. Both influence physical properties and performance, however, at present, this information is under-utilised in research and industry. Presently NIR spectroscopy is mainly used following the explorative approach, by which the contents of chemical components and physico-chemical as well as mechanical properties of the samples of interest are determined by applying multivariate statistical methods on the spectral data. Concrete hypotheses or prior knowledge on the chemistry and structure of the sample-exceeding that of reference data-are not necessary to build such multivariate models. However, to understand the underlying chemistry, knowledge on the chemical/functional groups that absorb at distinct wavelengths is indispensable and the assignment of NIR bands is necessary. Band assignment is an interesting and important part of spectroscopy that allows conclusions to be drawn on the chemistry and physico-chemical properties of samples. To summarise current knowledge on this topic, 70 years of NIR band assignment literature for wood and wood components were reviewed. In addition, preliminary results of ongoing investigations that also led to new assignments were included for discussion. Furthermore, some basic considerations on the interactions of NIR radiation with the inhomogeneous, anisotropic and porous structure of wood, and what impact this structure has on information contained in the spectra, are presented. In addition, the influence of common data (pre)-processing methods on the position of NIR bands is discussed. For more conclusive band assignments, it is recommended that wood is separated into its components. However, this approach may lead to misinterpretations when evaluation methods other than direct comparison of spectra are used, because isolation and purification of wood components is difficult and may lead to chemical and structural alterations when compared to the native state. Furthermore, "pure" components have more distinct and symmetric bands that influence the shape of the spectra. This extended review provides the reader with a comprehensive summary of NIR bands, as well as some practical considerations important for the application of NIR to wood. © IM Publications LLP 2011.

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