Predicting moisture content and density of Scots pine by microwave scanning of sawn timber

Abstract

Non-destructive testing of wood for prediction of strength is significantly influenced by wood density and moisture content. A sensor capable of measuring both density and moisture content would be a good tool to aid in predicting the strength of sawn timber. This study was carried out to investigate the possibility of calibrating a prediction model for the moisture content and density of Scots pine ( Pinus sylvestris) using microwave sensors. The material was initially at green moisture content, and thereafter dried in several steps to zero moisture content. At each step all the samples were weighted, scanned with a microwave camera (Satimo 9.4 GHz) and CT scanned with a medical CT scanner (Siemens Somatom AR.T.). The output variables from the microwave camera were used as predictors, and CT images correlated with known moisture content were used as response variables. Multivariate models to predict moisture content and density were calibrated using partial least squares (PLS) regression. The result shows that it is possible to predict both moisture content and density with very high accuracy using microwave sensors.