Wet-pocket classification in Abies lasiocarpa using spectroscopy in the visible and near infrared range

摘要: The potential of visible and near infrared (Vis-NIR) spectroscopy to distinguish wet-pockets from normal subalpine fir (Abies lasiocarpa Hook) wood was evaluated. Two specimen classes were used, namely, with more than half the surfaces covered by (WW), completely free (NW). A partial least square (PLS) regression model derived calibrated predict moisture content ranging 0 210%, its usefulness for moisture-based sorting green lumber assessed. Samples sorted into two after Vis-NIR scanning via models: (1) soft independent modeling class analogy (SIMCA) (2) PLS discriminant analysis. SIMCA using second derivatives wavelengths spanning 650 1150 nm successfully classified 98% WW NW in state, while it resulted misclassification 96% specimens air-drying. PLS 650–1150 nm, correctly state 100% air-drying, respectively. These results clearly demonstrate applicability discriminate wood.