INF - Artigos publicados em periódicos
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Navegando INF - Artigos publicados em periódicos por Autor "Araújo, Mário César Ugulino"
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Item Improving the computational efficiency of the successive projections algorithm by using a sequential regression implementation: a case study involving NIR spectrometric snalysis of wheat samples(2010) Soares, Anderson da Silva; Galvão Filho, Arlindo Rodrigues; Galvão, Roberto Kawakami Harrop; Araújo, Mário César UgulinoThis 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 (rootmean- 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.Item Multi-core computation inchemometrics: case dtudies of voltammetric and NIR spectrometric analyses(2010) Soares, Anderson da Silva; Galvão, Roberto Kawakami Harrop; Araújo, Mário César Ugulino; Soares, Sófacles Figueredo Carreiro; Pinto, Luiz AlbertoThe 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.