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.

通过使用遗传算法选择波长以建立基于偏最小二乘（PLS）回归的多元校准模型的优化问题。使用未经处理的和二阶导数光谱表示的两个近红外（NIR）数据集来预测桉树纸浆产量。在变量数量（即波长）从10到100的范围内运行优化过程，以确定PLS回归模型的最佳波长和潜在变量数量。R平方的线性函数用于校准和预测集被用作优化问题的目标函数。遗传算法选择的最佳波长有助于显着提高PLS回归模型的性能，不仅适用于校准集，还适用于预测集。潜在变量的最佳数量在很宽的范围内变化，从允许的最大值（20）到下限6。还通过统计确定了每个数据集的代表性波长，并通过波段分配过程将其分配给相应的木材组件，这表明了很强的一致性。