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Issue 41, 2016
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The successive projections algorithm for interval selection in partial least squares discriminant analysis

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Abstract

This paper proposes a new interval selection approach for PLS-DA modelling, which is developed as an extension of the recently introduced iSPA-PLS method for multivariate calibration. The proposed iSPA-PLS-DA algorithm is tested in two case studies concerning the classification of five types of vegetable oils employing square-wave voltammetry and the classification of five species of bacteria (Escherichia coli, Enterococcus faecalis, Streptococcus salivarius, Streptococcus oralis, and Staphylococcus aureus) using digital images. For comparison, the iPLS-DA algorithm for interval selection is also employed, in standard and backward modes. In both case studies, iSPA-PLS-DA provided correct classification rates larger than or equal to those obtained by PLS-DA using all variables and iPLS-DA with single or multiple intervals.

Graphical abstract: The successive projections algorithm for interval selection in partial least squares discriminant analysis

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Publication details

The article was received on 28 Jun 2016, accepted on 15 Sep 2016 and first published on 15 Sep 2016


Article type: Paper
DOI: 10.1039/C6AY01840H
Citation: Anal. Methods, 2016,8, 7522-7530
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    The successive projections algorithm for interval selection in partial least squares discriminant analysis

    D. Douglas de Sousa Fernandes, V. E. Almeida, L. Pinto, G. Véras, R. K. Harrop Galvão, A. A. Gomes and M. C. Ugulino Araújo, Anal. Methods, 2016, 8, 7522
    DOI: 10.1039/C6AY01840H

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