Utilization of genetic algorithms to optimize Eucalyptus globulus pulp yield models based on NIR spectra.

An optimization problem was developed by using a genetic algorithm to select wavelengths for establishing multivariate calibration models based on partial least squares (PLS) regression. Two near infrared (NIR) data sets represented by untreated and second derivative spectra were used to predict Eucalyptus globulus pulp yield. The optimization process was run with the number of variables (i.e., wavelengths) varied from 10 to 100 to determine the optimum wavelengths and number of latent variables for PLS regression model. A linear function of R-squares for calibration and prediction sets was utilized as the objective function of the optimization problem. The optimum wavelengths selected by genetic algorithm helped to considerably improve the performance of the PLS regression model, not only for the calibration sets but also for the prediction sets. The optimum number of latent variables varied over a wide range, from the maximum allowed (20) to a lower limit of six. Representative wavelengths for each data set were also statistically determined and assigned to corresponding wood components through a band assignment process, which showed strong agreement. [ABSTRACT FROM AUTHOR]