Comparison of multivariate calibration techniques applied to experimental NIR data sets

The present study compares the performance of different multivariate calibration techniques applied to four near-infrared data sets when test samples are well within the calibration domain. Three types of problems are discussed: the nonlinear calibration, the calibration using heterogeneous data sets, and the calibration in the presence of irrelevant information in the set of predictors. Recommendations are derived from the comparison, which should help to guide a nonchemometrician through the selection of an appropriate calibration method for a particular type of calibration data. A flexible methodology is proposed to allow selection of an appropriate calibration technique for a given calibration problem.54460862

Application of Fourier transform to multivariate calibration of near-infrared data

An approach for extracting the relevant features for multivariate calibration of the hydroxyl number in a polyol from a nearinfrared (NIR) spectroscopic data set by using the Fourier transform, is presented. It is carried out in the frequency domain starting from the frst 50 power-spectra (PS) coeffcients as the input to a genetic algorithm (GA). The appropriate PS coeffcients selected by the GA were used to build a multiple linear-regression (MLR) model. The performance of the new approach is compared with MLR after wavelength selection with GA, with the standard PCR and PLS methods applied to the wavelength domain, and PCR and PLS applied to the full PS domain. Furthermore, it was also compared to the `Uninformative Variable Elimination' (UVE) PLS method in the frequency domain. The results demonstrate that PS is a fast and powerful reduction method. The coeffcients selected are of two types: one that correlates with the characteristic investigated, and the other that takes into account different clusters. This also shows that the method can be used to investigate the structure of the data

A comparison of multivariate calibration techniques applied to experimental NIR data sets Part II. Predictive ability under extrapolation conditions

The present study compares the performance of different multivariate calibration techniques when new samples to be predicted can fall outside the calibration domain. Results of the calibration methods are investigated for extrapolation of different types and various levels. The calibration methods are applied to five near-IR data sets including difficulties often met in practical cases (nonlinearity, nonhomogeneity and presence of irrelevant variables in the set of predictors). The comparison leads to general recommendations about what method to use when samples requiring extrapolation can be expected in a calibration application

Comparison of multivariate calibration techniques applied to experimental NIR data sets

The present study compares the performance of different multivariate calibration techniques applied to four near-infrared data sets when test samples are well within the calibration domain. Three types of problems are discussed: the nonlienar calibrtion, the calibration using heterogeneous data sets, and the calibration in the presence of irrelevant information in the set of predictors. Recomandations are derived from the comparison which should help to guide a nonchemometrician through the selection of an appropriate calibration method for a particular type of calibration data

A novel approach for biomarker selection and the integration of repeated measures experiments from two assays

Background: High throughput 'omics' experiments are usually designed to compare changes observed between different conditions (or interventions) and to identify biomarkers capable of characterizing each condition. We consider the complex structure of repeated measurements from different assays where different conditions are applied on the same subjects