PRINCIPAL COMPONENT ANALYSIS (PCA) PARA a AVALIAcAO DE DADOS QUiMICOS E GERAcAO DE HEAT MAPS: UM TUTORIAL

• Autor(a) principal: Ferreira, Dennis da Silva
• Data de Publicação: 2023
• Outros Autores: Rodrigues, Leticia da Silva [UNESP], Pereira, Fabiola Manhas Verbi [UNESP], Pereira-Filho, Edenir Rodrigues
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.21577/0100-4042.20230030; http://hdl.handle.net/11449/245302
• topic: exploratory analysis; data mining; data visualization; direct solid sample analysis; laser; X-ray fluorescence

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.