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The paper focuses on solving a common and important problem of NIR quantitative analysis in multi-component systems: how to significantly reduce the size of the calibration set while not impairing the predictive precision. To cope with the problem orthogonal discrete wavelet packet transform (WPT), the least correlation design and correlation coefficient test (r-test) have been combined together....
Through randomly arranging samples of a calibration set, treating their NIR spectra with orthogonal discrete wavelet transform, and selecting suitable variables in terms of correlation coefficient test (r-test), it is possible to extract features of each component in a multi-component system respectively and partial least squares (PLS) models based on these features are capable of predicting the concentration...
In ordinary multivariate calibration methods, when the calibration set is determined to build the model describing the relationship between the dependent variables and the predictor variables, each sample in the calibration set makes the same contribution to the model, where the difference of representativeness between the samples is ignored. In this paper, by introducing the concept of weighted sampling...
In multivariate calibration methods like partial least squares (PLS), especially when the spectra data consists of measurements at hundreds and even thousands of analytical channels, it is widely accepted that before a multivariate regression model is built, a well-performed variable selection can be helpful to improve the predictive ability of the model. In the present paper, the idea of variable...
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