Abstract

Due to the multidimensional complexity and redundancy between wavelengths in the visible and near infrared (Vis-NIR) region, the speed and accuracy of data analysis can be affected. This study aims to investigate the feasibility of simplifying high dimensional data based on transformation of the spectra and local correlation maximization (LCM). These two methods will be applied to determine the prediction accuracy of air-dry density of Ulmus pumila wood. In this study, the reflectance spectra (Refl.) were subjected to the reciprocal (1/Refl.) and logarithm reflectance to improve the spectra signal for prediction. LCM was developed for selecting spectral sensitive regions that were important in the prediction of density. A local correlation coefficient (r) criterion was developed such that if the r ≥ 0.75 (between wavelength and density), then partial least squares and support vector machine (SVM) were employed as the prediction method. Likewise, 2D correlation spectroscopy plots were used to further reduce the data matrix by removing redundant wavelengths. The results showed that (1) although the sensitive region of density was different, the region of r ≥ 0.80 was mainly in the Vis and NIR spectral region. Additionally, the performance of models developed from the sensitive region was better than that of data used from the less-sensitive region. (2) The SVM model was optimized by a genetic algorithm based on the log (1/Refl.) of the sensitive region. In conclusion, it was found that the spectral transformation presented better density estimation results ( = 0.909, root mean square error of calibration = 0.014) than when less sensitive wavelengths were used in the data matrix.