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

Journal of the Brazilian Chemical Society

Anderson Da Silva Soares²  Roberto K. H Galvão¹  Mário César U Araújo¹  Sófacles F. C Soares¹  Luiz Alberto Pinto¹ 

[1] ,Instituto Tecnológico de Aeronáutica Divisão de Ciência da Computação Divisão de Engenharia Eletrônica São José dos Campos SP ,Brazil

关键词: parallel computation;    successive projections algorithm;    genetic algorithm;    partial least squares;    voltammetric analysis;    near-infrared spectrometric analysis;   

DOI  :  10.1590/S0103-50532010000900005

来源: SciELO

【 摘 要 】

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.

【 授权许可】

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