Search

The aim of this study is to statistically identify and distinguish wood samples corresponding to different areas of annual rings in trees of temperate regions, using the corresponding thermogravimetric (TG) and their first TG derivative (DTG) curves, and specifically to verify whether late and early wood chestnut samples are different with statistical significance, taking into account their TG and DTG curves. These significant differences are sought by applying statistical procedures based on functional data analysis (FDA), such as the functional ANOVA and the FDA classification methods. Each TG curve is firstly smoothed using the local polynomial regression estimator, and its first derivative is estimated. Then, functional ANOVA based on random projections (RP) is used to identify significant differences between TG or DTG curves of early and late wood samples. In order to know the extent of the differences between early and late wood samples, they are discriminated (and the correct classification proportion obtained) by employing a kernel nonparametric functional data analysis technique, based on the Bayes' rule, as well as functional generalized linear models and functional generalized additive models, allowing to classify materials using more than one type of thermal curves simultaneously. The results are compared with those obtained using some classical multivariate supervised classification methods: linear discriminant analysis, naive Bayes (NBC) and quadratic classification (QDA). The partial least squares (PLS) dimension reduction procedure was previously applied to the TG curves in order to employ these multivariate methods. The application of RP ANOVA shows significant differences between late and early wood regarding mass loss and mass loss rate under combustion. The use of PLS multivariate methods or FDA classification approaches applied to the TG and DTG curves allows to distinguish very accurately between late and early wood. The proposed method could be applied to other species to identify thermooxidative differences, combined with other experimental methods to find their chemical and physical causes. [ABSTRACT FROM AUTHOR]

This work addresses the problem of supervised classification of industrial wood species (seven different types in the present study) through their thermo-oxidative stability. This is evaluated by pressure differential scanning calorimetry (PDSC) using the ASTM E2009. The maximization of the ratio of correct classification and the reduction of the costs of this activity are intended. This supervised classification problem was carried out using two different proposals: applying novel nonparametric functional data analysis techniques, based on kernel estimation, to the original PDSC curves, and using machine learning classification approaches applied to different multivariate data sets. The multivariate data sets were obtained, on the one hand, by estimating the fractal (Hausdorff) dimension of the PDSC curves by several methods, jointly with selecting the parameters from fitting a nonlinear model to the PDSC curves and, on the other hand, applying principal component analysis or partial linear squares to the thermograms. The results obtained show that the PDSC curves can be used to discriminate wood samples when these innovative and traditional statistical techniques are applied. In the best of the cases, a probability of correct classification that equals to 0.92 was obtained. PDSC represents a new alternative to the use of images, spectra, and other thermal signals as thermogravimetric analysis for classification purposes.Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Abstract: Wood is one of the most complicated materials to be classified in different classes or species. In this paper, the thermogravimetric (TG) curves of 49 wood samples are used to classify them in 7 predetermined species. Different functional and multivariate statistical supervised classification methods are used for this task: a nonparametric Nadaraya–Watson kernel functional estimator (K-NFDA), using the complete TG curves, and multivariate supervised classification approaches, such as linear discriminant analysis (LDA), k Nearest Neighbors (k-NN), Naïve Bayes (NBC), Neural Networks (NN), and Support Vector Machines (SVM). Before applying the multivariate techniques, the TG curves are discretized using principal component analysis (PCA) or fitting a four-component generalized logistic model. The results show that the classical method of LDA using the logistic parameters had the best performance, although high correct classification percentages were also obtained with the rest of the approaches. The work is completed with a comprehensive simulation study, comparing the classification techniques in different scenarios. Artificial TG curves are generated using the logistic model and additional conclusions on wood classification are established. Due to the heterogeneity of wood, this simulation study is very useful to describe worst-case scenarios and to assess more accurately the proposed classification methodologies. [Copyright &y& Elsevier]

In this study, thermogravimetric (TG) and differential scanning calorimetry (DSC) curves, obtained by means of a simultaneous TG/DSC analyzer, and statistical functional nonparametric methods are used to classify different wood species. The temperature ranges, where the highest probability of correct classification is reached, are also computed. As each observation is a curve, a nonparametric functional discriminant technique based on the Bayes rule and the Nadaraya-Watson regression estimator is used. It assigns a future observation to the highest probability predefined class (supervised classification). The smoothing parameter needed in this nonparametric method is selected according to the cross-validation technique. The method proposed is applied to a sample of 49 wood items (7 per wood class) and also to classify between hardwoods and softwoods. In all the cases, the samples have been successfully classified, obtaining better results with the TG curves. The results are compared with those obtained with other nonparametric methods based on boosting algorithm. A discussion about the relation of the obtained results with the referenced wood component degradation temperature ranks is presented. [ABSTRACT FROM AUTHOR]

A new statistical functional data analysis (FDA) approach to perform interlaboratory tests is proposed and successfully applied to thermogravimetry (TG) and differential scanning calorimetry (DSC). This functional approach prevents the typical losses of information associated to the dimension reduction processes. It allows the location and variability of the thermal curves obtained by the application of a particular test procedure. The intra- and inter-laboratory variability and location have been estimated using a FDA approach as well as the traditional reproducibility and repeatability studies. To evaluate the new approach, 105 TG curves and 90 calorimetric curves were obtained from calcium oxalate monohydrate. The obtained curves correspond to seven simulated laboratories, 15 curves per laboratory. Functional mean and variance were estimated. From a functional point of view, these descriptive statistics consider each datum as a curve or function of infinite dimension. Confidence bands were computed using smooth bootstrap resampling. A laboratory consistency study is performed in a functional context. The functional depth approach based on bootstrap resampling is a useful tool to identify outliers among the laboratories. The new FDA approach permits to identify as outliers the thermal curves obtained with old or wrong calibrations. Functional analysis of variance test based on random projections and the false discovery rate procedure (FDR) provides which laboratories obtain significant different thermal curves. This approach can be applied to perform interlaboratory test programs where the response of the test result is functional, as, for example, DSC and TG tests, without having to assume that data follow a Gaussian distribution. [ABSTRACT FROM AUTHOR]