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The resistance of wood against fungal decay, in short the natural durability, is one of the main criteria for defining the potential use of a wood species. Wood collections, or xylaria, offer the unique opportunity to screen many specimens and species for the latter purpose yet sample size is often limited and standardized tests are often not possible neither desired given the historical and cultural value of these specimens. Two different methods to determine the natural durability are tested and presented here, more specifically the mini-block test and chemical fingerprinting by Direct Analysis in Real-Time Time-Of-Flight Mass Spectrometry (DART TOFMS). Fungal decay by Trametes versicolor was determined for 577 mini-blocks collected from xylarium specimens and 602 mini-blocks from commercial species, not belonging to the xylarium collection, were included as a benchmark. Mass loss percentages of the different species are similar to reported values, supporting the use of the mini-block test when standardized testing is hardly feasible. Furthermore, as expected there is also a significantly negative relationship between density and the mass loss percentages from the mini-block test (r-Spearman = − 0.65***). Finally, partial least square-based prediction of recorded mass loss by using the DART TOFMS chemical fingerprints is promising (R2-adjusted = 0.40***), yet the accuracy differs between species. [ABSTRACT FROM AUTHOR]

Using chemical fingerprints for timber species identification is a relatively new, but promising technique. However, little is known about the effect of pre-processing spectral data parameter settings on the timber species classification accuracy. Therefore, this study presents an extensive and automated analysis method using the random forest machine learning algorithm on a set of highly valuable timber species from the Meliaceae family. Metabolome profiles were collected using direct analysis in real-time (DART™) ionisation coupled with time-of-flight mass spectrometry (TOFMS) analysis of heartwood specimens for 175 individuals (representing 10 species). In order to analyse variability in classification accuracy, 110 sets of data pre-processing parameter combinations consisting of mass tolerance for binning and relative abundance cut-off thresholds were tested. Furthermore, for each set of parameters (designated "binning/threshold setting"), a random search for one hyperparameter of interest was performed, i.e. the number of variables (in this case ions) drawn randomly for each random forest analysis. The best classification accuracy (82.2%) was achieved with 47 variables and a binning and threshold combination of 40 mDa and 4%, respectively. Entandrophragma angolense is mostly confused with Entandrophragma candollei and Khaya anthotheca, and several Swietenia species are confused with each other due to the high similarity of their chemical fingerprints. Entandrophragma cylindricum, Entandrophragma utile, Khaya ivorensis, Lovoa trichilioides and Swietenia macrophylla are easy to discriminate and show less misclassifications. The choice of parameter settings, whether it is in the data pre-processing (binning and threshold) or classification algorithm (hyperparameters), results in variability in classification accuracy. Therefore, a preliminary parameter screening is proposed before constructing the final model when using the random forest algorithm for classification. Overall, DART-TOFMS in combination with random forest is a powerful tool for species identification. [ABSTRACT FROM AUTHOR]

A correction to this paper has been published: https://doi.org/10.1007/s00226-021-01302-9 [ABSTRACT FROM AUTHOR]