Análise discriminante linear em duas dimensões e fluorescência sincrônica total para classificação de óleos alimentícios

SÓFACLES FIGUEREDO CARREIROSOARES; BEATRIZ S. FERNANDEZ BAND; MATÍAS INSAUSTI; MARIO CESAR UGULINO DE ARAUJO; ADENILTON C. DA SILVA; ROBERTO K. GALVÃO

Florianapolis, SC, Brasil

Congreso; 18° Encontro Nacional de Química Analítica; 2016

ENQA

The two-dimensional linear discriminant analysis (2D-LDA) algorithm was originally proposed in the context of face image processing for the extraction of features with maximal discriminant power.However, despite its promising performance in image processing tasks, the 2D-LDA algorithm has not yet been used in applications involving chemical data. The present paper bridges this gap by investigating the use of 2D-LDA in lassification problems involving three-way spectral data. The investigation wasconcerned with simulated data, as well as real-life data sets involving the classification of dry-cured Parma ham according to ageing by surface tofluorescence spectrometry and the classification of edible vegetable oils according to feedstock using total synchronous fluorescence spectrometry. Theresults were compared with those obtained by using the spectral data with no feature extraction, U-PLSDA (Partial Least Squares Discriminant Analysis applied to the unfolded data), and LDA employing TUCKER-3 or PARAFAC scores. In the simulated data set, all methods yielded a correct classification rateof 100%. However, in the Parma ham and vegetable oil data sets, better classification rates were obtained