Discrimination of wood species based on a carbon nanotube/polymer composite chemiresistor array

Abstract

AbstractFast and efficient alternative methods for wood species identification are needed to combat illegal logging and to control fair trade. One of the possibilities of rapid wood recognition is via chemiresistor gas sensor arrays (“electronic nose”), the application of which is described in the present paper. Carbon nanotube composites (CNTs) of eight insulating polymers were prepared through solution processing and spin casting. The optimum amount of CNTs in the composites was determined by resistance measurement and the CNTs were characterized by scanning electron microscopy. In the case of static headspace analysis, the sensor responses were reproducible and discernible for the wood species. This was demonstrated based on five wood species (Pterocarpus indicus,Acacia auriculiformis,Gmelina arborea,Vitex parvifloraandDiospyros philippinensis). Discrimination of the data was achieved through principal component analysis (PCA) and hierarchical cluster analysis (HCA). PCA score plots and groupings in HCA dendrograms rendered possible the discrimination of these wood species. The potential application of the sensor array approach for wood species identification is high.