A variable elimination method to improve the parsimony of MLR models using the successive projections algorithm

Roberto Kawakami Harrop Galvao, Mario Cesar Ugulino Araujo, Wallace Duarte Fragoso, Edvan Cirino Silva, Gledson Emidio Jose, Sofacles Figueredo Carreiro Soares, Henrique Mohallem Paiva

Research output: Contribution to journalArticlepeer-review

225 Citations (Scopus)


The successive projections algorithm (SPA) is a variable selection technique designed to minimize collinearity problems in multiple linear regression (MLR). This paper proposes a modification to the basic SPA formulation aimed at further improving the parsimony of the resulting MLR model. For this purpose, an elimination procedure is incorporated to the algorithm in order to remove variables that do not effectively contribute towards the prediction ability of the model as indicated by an F-test. The utility of the proposed modification is illustrated in a simulation study, as well as in two application examples involving the analysis of diesel and com samples by near-infrared (NIR) spectroscopy. The results demonstrate that the number of variables selected by SPA can be reduced without significantly compromising prediction performance. In addition, SPA is favourably compared with classic Stepwise Regression and full-spectrum PLS. A graphical user interface for SPA is available at www.ele.ita.br/similar to kawakami/spa/. (C) 2008 Elsevier B.V. All rights reserved.

Original languageEnglish
Pages (from-to)83-91
Number of pages9
JournalChemometrics and Intelligent Laboratory Systems
Issue number1
Publication statusPublished - 15 May 2008


  • multiple linear regression
  • variable selection
  • successive projections algorithm
  • near-infrared spectrometry
  • diesel analysis
  • com analysis
  • multivariate calibration
  • selection
  • prediction


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