Evaluation of spectral collection strategies for identification of Dalbergia spp. using handheld laser‐induced breakdown spectroscopy.

The illegal timber trade has significant impact on the survival of endangered tropical hardwood species like Dalbergia spp. (rosewood), a world‐wide protected genus from the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES). Due to increased threat to Dalbergia spp., and lack of action to reduce threats, port of entry analysis methods are required to identify Dalbergia spp. Handheld laser‐induced breakdown spectroscopy (LIBS) has been shown to be capable of identifying species and establishing provenance of Dalbergia spp. and other tropical hardwoods, but analysis methods for this work have yet to be investigated in detail. The present work investigates five well‐known algorithms—partial least squares discriminant analysis (PLS‐DA), classification and regression trees (CART), k‐nearest neighbor (k‐NN), random forest (RF), and support vector machine (SVM)—two training/test set sampling regimes, and data collection at two signal‐to‐noise (S/N) ratios to assess the potential for handheld LIBS analyses. Additionally, imbalanced classes are addressed. For this application, SVM and RF yield near identical results (though RF takes nearly 100 longer to compute), while the S/N ratio has a significant effect on model success assuming all else is equal. It was found that forming a training set with replicate low S/N analyses can perform as well as higher precision training sets for true prediction, even if the predicted samples have low signal to noise! This work confirms handheld LIBS analyzers can provide a viable method for classification of hardwood species, even within the same genus. The illegal timber trade has significant impact on the survival of endangered tropical hardwood species like Dalbergia spp. Handheld laser‐induced breakdown spectroscopy has been shown capable of identifying species and establishing provenance of tropical hardwoods, including Dalbergia. This work investigates collection strategies at two S/N ratios and balanced classed, across two training/test set sampling regimes, five established classification algorithms for 60 total analyses. It was found that support vector machines and random forests yield κ > 0.9 and that more data collected at higher S/N can yield better true prediction. [ABSTRACT FROM AUTHOR]