No abstract

No abstract

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.

A comparative study involving a global linear method (partial least squares), a local linear method (locally weighted regression), and a nonlinear method (neural networks) has been performed in order to implement a calibration model on an industrial process. The models were designed to predict the water content in a reactor during a distillation process, using in-line measurements from a near-infrared analyzer. Curved effects due to changes in temperature and variations between the different batches make the problem particularly challenging. The influence of spectral range selection and data preprocessing has been studied. With each calibration method, specific procedures have been applied to promote model robustness. In particular, the use of a monitoring set with neural networks does not always prevent overfitting. Therefore, we developed a model selection criterion based on the determination of the median of monitoring error over replicate trials. The back-propagation neural network models selected were found to outperform the other methods on independent test data.