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.

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One of the foremost challenges facing dye analysis of historical textiles is that the gold standard technique – high performance liquid chromatography (HPLC) – is inherently destructive, costly, and relatively few institutions have the necessary equipment and expertise. This is especially problematic considering historical samples are unique, sampling is undesirable, and historical textiles can be exceptionally fragile. One proposed solution to this is the implementation of non-destructive, spectroscopic, techniques, such as Fiber Optic Reflectance Spectroscopy (FORS). In this work, 204 well-provenanced red Norwich textiles were measured with FORS and analyzed, aiming to test if the technique would be able to discern the chromophore combinations / recipes used to dye historical textiles. Cluster analysis algorithms and spectroscopic domain knowledge were coupled with selective HPLC validation to assess overall ability of FORS to identify chromophore combinations. The UV/VIS region, particularly 380-469 nm, showed a narrow visible region that was primarily responsible for clustering behavior that correlates with HPLC-validated samples. In contrast, the near infrared (NIR) region of the spectrum contained little meaningful information in multivariate space. This indicates that FORS shows promise for identifying chromophores in textile samples. Suspecting the observed spectral inflection point shift around 600 nm was correlated to the presence of mordants, complementary X-ray fluorescence (XRF) analysis was used, with the corresponding statistical treatment, which showed no correlation. From this work, three main conclusions can be drawn: 1) FORS adequately identifies visual information, which shows reasonable correlation to HPLC-validated dye recipes, warranting further investigation, and indicating utility for confirmatory analysis, and strongly suggesting this is a good tool for persons with visual impairments; 2) XRF analysis confirms that ~600 nm inflection point shift and mordant are not correlated when measuring dyed textiles; 3) many documented structural-to-spectral relationships established in the conservation literature are too weak in dyed textiles for statistical analysis and, by extension, expert spectral identification.