Improving the computational efficiency of the successive projections algorithm by using a sequential regression implementation: a case study involving nir spectrometric analysis of wheat samples

Soares, Anderson S.; Galvao Filho, Arlindo R.; Galvao, Roberto K. H.; Araujo, Mario Cesar U.

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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Improving the computational efficiency of the successive projections algorithm by using a sequential regression implementation: a case study involving nir spectrometric analysis of wheat samples

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Author(s):	Soares, Anderson S. ^[1] ; Galvao Filho, Arlindo R. ^[1] ; Galvao, Roberto K. H. ^[1] ; Araujo, Mario Cesar U. ^[2] Total Authors: 4
Affiliation:	^[1] Inst Tecnol Aeronaut, Div Engn Elet, BR-12228900 Sao Jose Dos Campos, SP - Brazil ^[2] Univ Fed Paraiba, CCEN, Dept Quim, BR-58051970 Joao Pessoa, Paraiba - Brazil Total Affiliations: 2
Document type:	Journal article
Source:	Journal of the Brazilian Chemical Society; v. 21, n. 4, p. 760-763, 2010.
Web of Science Citations:	5
Abstract
This short report proposes a sequential regression implementation for the successive projections algorithm (SPA), which is a variable selection technique for multiple linear regression. An example involving the near-infrared determination of protein in wheat is presented for illustration. The resulting model predictions exhibited a correlation coefficient of 0.989 and an RMSEP (root-mean-square error of prediction) value of 0.2% m/m in the range 10.2-16.2% m/m. The proposed implementation provided computational gains of up to five-fold. (AU)

FAPESP's process:	06/58850-6 - Diagnosis, prognosis and fault accommodation for dynamical systems
Grantee:	Takashi Yoneyama
Support Opportunities:	Research Projects - Thematic Grants

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