Novas estratégias para seleção de variáveis por intervalos em problemas de classificação

• Autor(a) principal: Fernandes, David Douglas de Sousa
• Data de Publicação: 2016
• Tipo de documento: Tese
• Idioma: por
• Título da fonte: Biblioteca Digital de Teses e Dissertações da UFPB
• Texto Completo: https://repositorio.ufpb.br/jspui/handle/tede/9007
• topic: Seleção de intervalos; Algoritmo das projeções sucessivas; Análise discriminantes por mínimos quadrados parciais; Modelagem independente e flexível por analogia de classe; Classificação; Interval selection; Successive projections algorithm; Discriminant analysis partial least squares; Soft independent modeling by class analogy; Classification; CIENCIAS EXATAS E DA TERRA::QUIMICA

In Analytical Chemistry it has been recurring in the literature the use of analytical signals recorded on multiple sensors combined with subsequent chemometric modeling for developing new analytical methodologies. For this purpose, it uses generally multivariate instrumental techniques as spectrometry ultraviolet-visible or near infrared, voltammetry, etc. In this scenario, the analyst is faced with the option of selecting individual variables or variable intervals so to avoid or reduce multicollinearity problems. A well-known strategy for selection of variable intervals is to divide the set of instrumental responses into equal width intervals and select the best interval based on the performance of the prediction of a unique range in the regression by Partial Least Squares (iPLS). On the other hand, the use of interval selection for classification purposes has received relatively little attention. A common practice is to use the iPLS regression method with the coded class indices as response variables to be predicted; that is the basic idea behind the release of the Discriminant Analysis by Partial Least Squares (PLS-DA) for classification. In other words, interval selection for classification purposes has no development of native functions (algorithms). Thus, in this work it is proposed two new strategies in classification problems using interval selection by the Successive Projections Algorithm. The first strategy is named Successive Projections Algorithm for selecting intervals in Discriminant Analysis Partial Least Squares (iSPA-PLS-DA), while the second strategy is called Successive Projections Algorithm for selecting intervals in Soft and Independent Modeling by Class Analogy (iSPA-SIMCA). The performance of the proposed algorithms was evaluated in three case studies: classification of vegetable oils according to the type of raw material and the expiration date using data obtained by square wave voltammetry; classification of unadulterated biodiesel/diesel blends (B5) and adulterated with soybean oil (OB5) using spectral data obtained in the ultraviolet-visible region; and classification of vegetable oils with respect to the expiration date using spectral data obtained in the near infrared region. The proposed iSPA-PLS-DA and iSPA-SIMCA algorithms provided good results in the three case studies, with correct classification rates always greater than or equal to those obtained by PLS-DA and SIMCA models using all variables, iPLS-DA and iSIMCA with a single selected interval, as well as SPA-LDA and GA-LDA with selection of individual variables. Therefore, the proposed iSPA-PLS-DA and iSPA-SIMCA algorithms can be considered as promising approaches for use in classification problems employing interval selection. In a more general point of view, the possibility of using interval selection without loss of the classification accuracy can be considered a very useful tool for the construction of dedicated instruments (e.g. LED-based photometers) for use in routine and in situ analysis.