Advanced search
Start date
Betweenand
Related content
(Reference retrieved automatically from SciELO through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Multi-core computation in chemometrics: case studies of voltammetric and NIR spectrometric analyses

Full text
Author(s):
Anderson da Silva Soares [1] ; Roberto K. H Galvão [2] ; Mário César U Araújo [3] ; Sófacles F. C Soares [4] ; Luiz Alberto Pinto
Total Authors: 5
Affiliation:
[1] Instituto Tecnológico de Aeronáutica. Divisão de Ciência da Computação Divisão de Engenharia Eletrônica - Brasil
[2] Instituto Tecnológico de Aeronáutica. Divisão de Engenharia Eletrônica - Brasil
[3] Universidade Federal da Paraíba. CCEN. Departamento de Química - Brasil
[4] Universidade Federal da Paraíba. CCEN. Departamento de Química - Brasil
Total Affiliations: 5
Document type: Journal article
Source: Journal of the Brazilian Chemical Society; v. 21, n. 9, p. 1626-1634, 2010-00-00.
Abstract

The application of sophisticated chemometrics techniques to large datasets has been made possible by continuing technological improvements in off-the-shelf computers. Recently, such improvements have been mainly achieved by the introduction of multi-core processors. However, the efficient use of multi-core hardware requires the development of software that properly address parallel computing. This paper is concerned with the implementation of parallelism using the Matlab Parallel Computing Toolbox, which requires only simple modifications to existing chemometrics code in order to exploit the benefits of multi-core processing. By using this software tool, it is shown that parallel implementations may provide substantial computational gains. In particular, the present study considers the problem of variable selection employing the successive projections algorithm and the genetic algorithm, as well as the use of cross-validation in partial least squares. For demonstration, two analytical applications are presented: determination of protein in wheat by near-infrared reflectance spectrometry and classification of edible vegetable oils by square-wave voltammetry. By using the proposed parallel computing implementations, computational gains of up to 204% were obtained. (AU)

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