Multivariate Analysis of Near-Infrared Spectra by Wavelet Domain Regression Using Genetic Algorithm

To take advantages of multiscale property of near infrared (NIR) spectra, a new hybrid algorithm (GA-WPLS) was proposed for developing the multivariate regression model in the wavelet domain instead of the spectra domain. At first, wavelet packet transform (WPT) algorithm and its reconstruction algorithm are employed to split the raw spectra into different frequency components in wavelet domain. Then the prediction models are developed by the WPT-based partial least squares (WPLS) algorithm where each component is characterized by the weighted regression coefficient. Through performance comparison of these WPLS-based models, the optimized decomposition level can be determined. At last, based on the components obtained with the optimized decomposition level, the genetic algorithm is used to select the informative components as the input data of WPLS-based regression model. To validate the GA-WPLS algorithm, it was applied to measure the original extract concentration of beer. Compared with the conventional PLS algorithm, the GA-WPLS algorithm can greatly improve the prediction ability of NIR multivariate models with the prediction errors decreasing by up to 72.3%, indicating that it is an efficient way for developing promising model using NIR spectra.