Extending near infrared reflectance(NIR) pulp yield calibrations to new sites and species

Recently, we demonstrated that the accuracy of pulp yield predictions for wood samples from a site (Gog) new to their calibration (Tasmania-wide Eucalyptus nitens) was greatly improved by adding five Gog samples to the calibration set. In this study we investigated the addition of Gog samples to the Tasmania-wide E. nitens set, with the aim of further improving predictive accuracy. It was demonstrated that the addition of a single Gog sample to the Tasmania-wide calibration set was sufficient to greatly reduce predictive errors and that the inclusion of at least 3 Gog samples in the Tasmania-wide set was sufficient to give relatively stable predictive errors. The addition of different sets of 5 Gog samples to the Tasmania-wide calibration, however, caused predictive errors to vary between sets. The standard deviation of pulp yield for the prediction set ( 20 Gog samples) was important, with sets having the largest standard deviations giving the best predictive statistics. Finally, the Tasmania-wide E. nitens calibration was enhanced using samples from a different species ( Eucalyptus globulus) and applied successfully to other E. globulus samples.