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X-ray computed tomography (CT) of logs means possibilities for optimizing breakdown in sawmills. This depends on accurate detection of knots to assess internal quality. However, as logs are stored, they dry to some extent, and this drying affects the density variation in the log and therefore the X-ray images. For this reason, it is hypothetically difficult to detect log features in partially dried logs using X-ray CT. This paper investigates the effect of improper heartwood-sapwood border detection, possibly due to partial drying, on knot detection in jack pine ( Pinus banksiana Lamb.) and white spruce ( Picea glauca (Moench) Voss) logs from New Brunswick, Canada. An automatic knot detection algorithm was compared to manual reference knot measurements, and the results showed that knot detection was affected by detected heartwood shape. It was also shown that logs can be sorted into two groups based on how well the heartwood-sapwood border is detected to separate logs with a high knot detection rate from those with a low detection rate. In that way, a decision can be made whether or not to trust the knot models obtained from CT scanning. This can potentially aid both sawmills and researchers working with log models based on CT. [ABSTRACT FROM AUTHOR]

Mixed models combining random coefficient effect and covariance patterns were used to investigate mechanical property variations in jack pine ( Pinus banksiana Lamb.) and white spruce ( Picea glauca (Moench) Voss) trees. Modulus of elasticity (MOE) and modulus of rupture (MOR) were measured by conducting three-point bending tests on small defect-free samples selected from different radial positions and at a height of 2.5 m above ground within the stems. The objective of the paper was to build statistical predictive models describing the radial variations in stems for wood mechanical properties using easily measurable explanatory variables that are typically available in the wood manufacturing industry: distance from pith, tree height and diameter, and spacing. The explanatory variables integrated into the models explained MOE adequately, whereas MOR appeared harder to predict with only these variables and at this resolution. For white spruce, the best mixed-effects models explained 80% and 61% of the variation in MOE and MOR, respectively. For jack pine, it was 51% and 33% for the same response variables. These results are a step toward models that could be used in sawing simulation software designed to estimate the internal properties of sawlogs and, as a result, better predict lumber and pulp chip quality. [ABSTRACT FROM AUTHOR]