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Quimiometria I: calibração multivariada, um tutorial
Author(s) -
Márcia M. C. Ferreira,
Alexandre Martinez Antunes,
Marisa S. Melgo,
Pedro L. O. Volpe
Publication year - 1999
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 - 10.1590/s0100-40421999000500016
Subject(s) - principal component analysis , leverage (statistics) , calibration , preprocessor , principal component regression , matlab , computer science , outlier , multivariate statistics , partial least squares regression , univariate , cross validation , mathematics , statistics , algorithm , artificial intelligence , operating system
The aim of this work is to present a tutorial on Multivariate Calibration, a tool which is nowadays necessary in basically most laboratories but very often misused. The basic concepts of preprocessing, principal component analysis (PCA), principal component regression (PCR) and partial least squares (PLS) are given. The two basic steps on any calibration procedure: model building and validation are fully discussed. The concepts of cross validation (to determine the number of factors to be used in the model), leverage and studentized residuals (to detect outliers) for the validation step are given. The whole calibration procedure is illustrated using spectra recorded for ternary mixtures of 2,4,6 trinitrophenolate, 2,4 dinitrophenolate and 2,5 dinitrophenolate followed by the concentration prediction of these three chemical species during a diffusion experiment through a hydrophobic liquid membrane. MATLAB software is used for numerical calculations. Most of the commands for the analysis are provided in order to allow a non-specialist to follow step by step the analysis

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