A new method for the elimination of uninformative variables in multivariate data sets is proposed. To achieve this, artificial (noise) variables are added and a closed form of the PLS or PCR model is obtained for the data set containing the experimental and the artificial variables. The experimental variables that do not have more importance than the artificial variables, as judged from a criterion based on the b coefficients, are eliminated. The performance of the method is evaluated on simulated data. Practical aspects are discussed on experimentally obtained near-IR data sets. It is concluded that the elimination of uninformative variables can improve predictive ability.

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The application of locally weighted regression (LWR) to nonlinear calibration problems and strongly clustered calibration data often yields more reliable predictions than global linear calibration models. This study compares the performance of LWR that uses PCR and PLS regression, the Euclidean and Mahalanobis distance as a distance measure, and the uniform and cubic weighting of calibration objects in local models. Recommendations are given on how to apply LWR to near-infrared data sets without spending too much time in the optimization phase.

The present study compares the perform ance of different multivariate calibration tech niques applied to four near-infrared data sets when test samples are well within the calibration domain. Three types of problem s are discussed: the nonlinear calibration, the calibration using heterogeneous data sets, and the calibration in the presen ce of irrelevant inform ation in the set of predictors. Recommendations are derived from the com parison, which should help to guide a nonchemometrician through the selection of an appropriate calibration method for a particular type of calibration data. A¯exible methodology is proposed to allow selection of an appropriate calibration tech nique for a given calibration problem.