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A aplicacao de tecnicas quimiometricas sofisticadas a grandes conjuntos de dados tem se tornado possivel devido aos continuos aprimoramentos tecnologicos em computadores comerciais. Recentemente, tais aprimoramentos tem sido obtidos principalmente atraves da introducao de processadores com multiplos nucleos. Contudo, o uso eficiente de hardware com multiplos nucleos requer o desenvolvimento de software apropriado para computacao paralela. Este artigo trata da implementacao de paralelismo empregando o Matlab Parallel Computing Toolbox, que requer somente pequenas modificacoes em codigos quimiometricos ja existentes de modo a explorar os beneficios do processamento em multiplos nucleos. Empregando essa ferramenta de software, mostra-se que implementacoes paralelas podem proporcionar expressivos ganhos computacionais. Em particular, considera-se o problema de selecao de variaveis empregando o algoritmo das projecoes sucessivas e o algoritmo genetico, bem como o uso de validacao cruzada em minimos quadrados parciais. Para ilustracao, duas aplicacoes analiticas sao apresentadas: determinacao de proteina em trigo por espectrometria de reflectância no infravermelho proximo e classificacao de oleos vegetais comestiveis por voltametria de onda quadrada. Empregando as implementacoes propostas para computacao paralela, ganhos computacionais de ate 204% foram obtidos. 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.

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