Issue 7, 2013

Application of random forest regression to spectral multivariate calibration

Abstract

The performance of the random forest (RF) algorithm on the spectroscopic data was studied and compared by bootstrap aggregating of classification and regression trees (bagging CART), partial least squares (PLS) and nonlinear support vector machine (SVM) algorithms. The performances of these algorithms were investigated on four real data sets; these data sets were: (1) UV-Visible spectra of two cardiovascular drugs (hydrochlorothiazide and valsartan); (2) visible spectra of copper, cobalt and nickel complexes with 4-(2-pyridylazo) resorcinol (PAR) as chromogenic reagent; (3) near infrared spectra of corn samples, and (4) near infrared diffuse transmission spectra of pharmaceutical tablets. Results indicate that besides its comparable accuracy and mathematical simplicity, it is computationally fast and robust to noise. Therefore, RF is a useful tool for regression studies and has potential for modeling linear and nonlinear multivariate calibration.

Graphical abstract: Application of random forest regression to spectral multivariate calibration

Article information

Article type
Paper
Submitted
20 Jun 2012
Accepted
06 Feb 2013
First published
07 Feb 2013

Anal. Methods, 2013,5, 1863-1871

Application of random forest regression to spectral multivariate calibration

J. B. Ghasemi and H. Tavakoli, Anal. Methods, 2013, 5, 1863 DOI: 10.1039/C3AY26338J

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