A total of 30 Caesalpinia echinata (pernambuco) sticks were ranked based on their suitability for making high quality bows and were assigned to one of the three following categories: 0=very poor to poor, 1=good to very good, and 2=excellent. From the end of each stick a sample was cut for wood property and near infrared (NIR) spectroscopic analysis. Wood properties measured included air-dry density, extractives content, microfibril angle, stiffness and wood color. NIR spectra were evaluated by principal component analysis (PCA) and on the PC scores. Poor quality samples were discriminated from those of good to very good and excellent quality; however, samples from the two higher quality groups were mixed. Based on relationships observed between PC scores and wood properties, we suggest that, of the measured properties, density and stiffness were the most important in sample discrimination based on quality. Samples ranked in the excellent category had high average density (1119 kg m-3) and stiffness (25.2 GPa) and relatively low extractives content (21.2%) compared to samples in the very poor to poor category (density= 938 kg m-3, stiffness=18.9 GPa and extractives content=24.9%).
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.