论文题目:An approach to quantify natural durability of Eucalyptus bosistoana by near infrared spectroscopy for genetic selection
论文作者:Yanjie Li Monika Sharm Clemens Altaner Laurie J.Cookson
期刊来源:Industrial Crops and Products
Natural durability within a timber species can be variable. Hence efficient assessments of natural durability are required to ensure quality either through tree breeding or segregation during production. In this study, first the relationship between extractive content and mass loss of Eucalyptus bosistoana heartwood caused by a white and a brown-rot fungus was validated. Then the ability of NIR spectroscopy as a high-throughput method to evaluate heartwood decay resistance was examined. Finally the NIR method was applied to a tree breeding trial. A correlation between extractive content and mass loss against the white-rot fungus (Perenniporia tephropora) and the brown-rot fungus (Coniophora olivacea) were found. Analysis of NIR spectra indicated that this relationship is causal with shared bands for mass loss and extractive content models at 6650, 6017, 5265 and 4659 cm−1. Partial least squares regression (PLSR), supplemented with spectra normalisation and variable selection, allowed prediction of mass loss with a residual mean square error (RMSE) of 7.48 % and 5.76 % for the white-rot and brown-rot, respectively. This level of precision allowed the characterisation of a E. bosistoana resource which showed a range of mass loss from 0 to 60 %. Genetic control was found for mass loss by the white-rot ( h2 = 0.70 and 0.24) and the brown-rot ( h2 = 0.15 and 0.13) at two sites in New Zealand. The rankings were correlated between sites, with genetic correlations () of 0.69 and 0.63 for white-rot and brown-rot, respectively, as well as to the predicted extractive content (0.82 to 0.92). However, the study indicated a significant site effect on the decay resistance of the E. bosistoana heartwood. In summary, this study has shown that the decay resistance could be assessed rapidly and efficiently using NIR technology for genetic selection.