Principal Component Analysis (Pca) Para a Avaliação De Dados Químicos E Geração De Heat Maps: Um Tutorial

Abstract

PRINCIPAL COMPONENT ANALYSIS (PCA) FOR CHEMICAL DATA EVALUATION AND HEAT MAPS PREPARATION: A TUTORIAL. This tutorial shows a step-by-step guide on handling big datasets using principal component analysis (PCA). A dataset of chemical elements, concentration, emission spectrum, and energy-dispersive X-ray fluorescence (EDXRF) of e-waste were used as examples. Five routines were proposed to apply data processing and PCA calculation focusing data from laser-induced breakdown spectroscopy (LIBS), EDXRF, and heat maps preparation. These routines can be used in various softwares such as MatLab, Octave, R, and Python. PCA was applied in three examples; the first was for concentrations, and the other two were for spectra. An example of heat maps assembling a hyperspectral image of a printed circuit was also described. In addition, a playlist was created on YouTube using the available examples. Therefore, with this tutorial, it may be possible to learn how to deal with a large volume of data by applying PCA. The authors hope to contribute to those researching in the area.

Direct solid sample analysis of low-cost jewelry using spectroanalytical techniques: exploratory chemical data evaluation and metal migration with synthetic sweat

“…Furthermore, to transform this data set obtained in the information, PCA 25 can be carried out, in which the matrix X containing the set of multidimensional data is decomposed into scores that relate the samples and loadings linked to the variables. 31 Fig. 5 shows, for instance, sample S5 (a keychain; see Fig.…”

“…Furthermore, to transform this data set obtained in the information, PCA 25 can be carried out, in which the matrix X containing the set of multidimensional data is decomposed into scores that relate the samples and loadings linked to the variables. 31 The scores maps 32,33 presented in Fig. 5 show a color division range, which assigns the colors to the respective positive in red and negative in blue values, representing the differences of the sample composition.…”