A common near infrared—based partial least squares regression model for the prediction of wood density of Pinus pinaster and Larix?×?eurolepis

Wood density is defined as the ratio of mass to volume and therefore in principle it should be possible to calculate a unique partial least squares regression (PLS-R) model for several species. PLS-R models for wood density based on X-ray microdensity data were calculated for each species Pinus pinaster and Larix × eurolepis and for both species together. After cross-validation and test set validation the data sets were combined and final models were calculated. The common model gave a residual prediction deviation (RPD) of 3.1, a range error ratio (RER) of 11.7, and a SEP/SEC of 1.06. The single models for Pinus pinaster and Larix?×?eurolepis gave RPD’s of 3.5 and 3.2, RER’s of 13 and 11, and a SEP/SEC of 1.2. To the best knowledge of the authors all obtained PLS-R models are the first ones that fulfil the requirements according to AACC Method 39-00 (AACC in AACC Method, 39-00:15, 1999) to be used at least for screening (RPD?≥?2.5). Although this method and the defined limits were developed for the analysis of grains they can be used as a rough rule of thumb until limits for wood are available. The improvement of the PLS-R models, compared to published results, might be due to three facts (1) the higher number of scans collected for a single spectrum, (2) that the samples were better represented by the NIR spectra and X-ray microdensity values, and (3) that the sites for the measurement of NIR spectra and X-ray microdensity were coincided as strictly as possibly.