Quantitative analysis of near infrared spectra by wavelet coefficient regression using a genetic algorithm

In this paper, we present wavelet coefficient regression (WCR) in combination with a genetic algorithm (GA) as a method for multicomponent analysis by Near Infrared Spectrometry. The results are compared with other multivariate calibration methods like Fourier coefficient regression (FCR), principal component regression (PCR) and absorbance value regression at selected wavelengths (AVR). It is shown that in comparison to conventional methods, WCR is quite unique by the fact that it is self-adaptive. This means that the steps of pretreatment, selection of specific wavelength regions and calibration are performed automatically in one step.