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
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.
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| Main Authors: | , , , |
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| Format: | Digital revista |
| Language: | English |
| Published: |
Sociedade Brasileira de Química
2010
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| Online Access: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-50532010000400024 |
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