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Its natural aesthetics make wood an attractivematerial for construction and design. However, there is nodetailed understanding of the relationships between humanperception of the appearance and measurable features ofwood surfaces that could be used for controlling sawntimber production. This study investigated whether woodsurfaces can be classified according to their visualappearance on the basis of wood feature measurements.Cluster analysis was used to discover a classification basedon a set of feature pattern variables in a sample of 300softwood floorboards. A finely graded visual appearancesorting provided a reference. Discriminant analysis wasapplied to identify the relevant variables from the tested setand to assess predictability of the classification. The resultsindicated that visual appearance sorting could be approximatedquite well by the variable-based classification afterpregrouping according to board position in the log.Ambivalent results were obtained for group predictionwithin the validation sample. While for boards from somegroups prediction was mostly or entirely correct, boardsfrom other groups were largely misclassified. An effect ofthe available sample was one of the surmised causes,making repetition of the analysis based on a larger sample adesirable focus of further research.<p>Validerad; 2015; Nivå 2; 20140609 (olof)</p

Classification of wood surfaces according to visual appearance by multivariate analysis of wood feature data