Open AccessCHEMOSTAT: EXPLORATORY MULTIVARIATE DATA ANALYSIS SOFTWARE
Author(s) -
Gilson Augusto Helfer,
Fernanda Carla Böck,
Luciano Marder,
João Carlos Furtado,
Adilson Ben da Costa,
Marco Flôres Ferrão
Publication year - 2015
Publication title -
química nova
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.214
H-Index - 73
eISSN - 1678-7064
pISSN - 0100-4042
DOI - 10.5935/0100-4042.20150063
Subject(s) - computer science , multivariate statistics , exploratory data analysis , chemostat , multivariate analysis , software , exploratory analysis , data mining , statistics , data science , mathematics , biology , programming language , machine learning , genetics , bacteria
The objective of this work was to develop a free access exploratory data analysis software application for academic use that is easy to install and can be handled without user-level programming due to extensive use of chemometrics and its association with applications that require purchased licenses or routines. The developed software, called Chemostat, employs Hierarchical Cluster Analysis (HCA), Principal Component Analysis (PCA), intervals Principal Component Analysis (iPCA), as well as correction methods, data transformation and outlier detection. The data can be imported from the clipboard, text files, ASCII or FT-IR Perkin-Elmer “.sp” files. It generates a variety of charts and tables that allow the analysis of results that can be exported in several formats. The main features of the software were tested using midinfrared and near-infrared spectra in vegetable oils and digital images obtained from different types of commercial diesel. In order to validate the software results, the same sets of data were analyzed using Matlab© and the results in both applications matched in various combinations. In addition to the desktop version, the reuse of algorithms allowed an online version to be provided that offers a unique experience on the web. Both applications are available in English