Quantitative calibration model for Infrared spectroscopy using continuous wavelet transform combined with genetic algorithm

A quantitative calibration model for Infrared spectroscopy using continuous wavelet transform combined with genetic algorithm is presented in this paper. We propose three scale selection methods in continuous wavelet transform and provide the comparisons with the general preprocessing methods. Experimental results show that selectively combining scales results in a quantitative model with better performance than that of either a regression model trained on the original data or developed on the pretreatment spectra, which demonstrates the applicability of the wavelet transform as a simple preprocessing step that can improve predict performance. Moreover, genetic algorithm on the wavelet transformed spectra can further improve the calibration model.