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

Its natural aesthetics make wood an attractive material for construction and design. However, there is no detailed understanding of the relationships between human perception of the appearance and measurable features of wood surfaces that could be used for controlling sawn timber production. This study investigated whether wood surfaces can be classified according to their visual appearance on the basis of wood feature measurements. Cluster analysis was used to discover a classification based on a set of feature pattern variables in a sample of 300 softwood floorboards. A finely graded visual appearance sorting provided a reference. Discriminant analysis was applied to identify the relevant variables from the tested set and to assess predictability of the classification. The results indicated that visual appearance sorting could be approximated quite well by the variable-based classification after pregrouping according to board position in the log. Ambivalent results were obtained for group prediction within the validation sample. While for boards from some groups prediction was mostly or entirely correct, boards from other groups were largely misclassified. An effect of the available sample was one of the surmised causes, making repetition of the analysis based on a larger sample a desirable focus of further research.     «

Its natural aesthetics make wood an attractive material for construction and design. However, there is no detailed understanding of the relationships between human perception of the appearance and measurable features of wood surfaces that could be used for controlling sawn timber production. This study investigated whether wood surfaces can be classified according to their visual appearance on the basis of wood feature measurements. Cluster analysis was used to discover a classification based on...     »