Real-time spectral classification of compression wood in Picea abies

Compression wood is formed by the living tree to compensate for external lo ads, It creates wood fibers with properties undesirable in sawn products. A utomatic detection of compression wood can lead to production advantages. A wood surface was scanned with a spectrometer, and compression wood was det ected by analyzing the spectral composition of light reflected from the woo d surface within the visible spectrum. Linear prediction models for compres sion wood in Norway spruce (Picea abies) were produced using multivariate a nalysis and regression methods. The resulting prediction coefficients were implemented in a scanning system using the MAPP2200 smart image sensor comb ined with an imaging spectrograph. This scanning system is capable of makin g a pixelwise classification of a wood surface in real time. Classification of one spruce plank was compared with analysis by scanning electron micros copy, showing that the automatic classification was correct in 11 of 14 cas es.