摘要： A new method is proposed for the discrimination of wood species by combining near-infrared reflectance spectroscopy (NIRS) and laser-induced breakdown spectroscopy (LIBS) and using chemometrics for data analysis. The method was applied to the analysis of 42 samples from six different species: Amburana cearensis, Copaifera lucens, Phyllocarpus riedelii, Cariniana legalis, Bowdichia virgilioides, and Aspidosperma pyricollum. The spectra from both techniques were merged on a single data matrix and pretreated by standard normal variate (SNV) and Savitzky– Golay first derivative with smoothing. Principal component analysis was applied to the exploratory data analysis and showed a clear formation of sample groups according to the wood species only when the data from both analytical techniques and the data pretreatment were used. Sample discrimination using partial least squares discriminant analysis was proved possible, but with an average misclassification of about 10%. Sample grouping and discrimination were shown to be probably related to different concentrations of iron, copper, zinc, and/or sodium (affecting the LIBS spectra) and lignin, water, cellulose, and/or hemicellulose (affecting the NIRS spectra).