Differentiation of Human, Dog, and Cat hair Fibers using DART TOFMS and Machine Learning

Hair is found in over 90% of crime scenes and has long been analyzed as trace evidence. However, recent reviews of traditional hair fiber analysis techniques, primarily morphological examination, have cast doubt on its reliability. To address these concerns, this study employed machine learning algorithms, specifically Linear Discriminant Analysis (LDA) and Random Forest, on Direct Analysis in Real Time time-of-flight mass spectra collected from human, cat, and dog hair samples. The objective was to develop a chemistry- and statistics-based classification method for unbiased taxonomic identification of hair. The results of the study showed that LDA and Random Forest were highly effective in separating mass spectra collected from hair samples with accuracies ranging from 94-98%. This approach holds significant promise for forensic investigations, archaeology, and artifact analysis

Differentiation of Human, Dog, and Cat hair Fibers using DART TOFMS and Machine Learning

Hair is found in over 90% of crime scenes and has long been analyzed as trace evidence. However, recent reviews of traditional hair fiber analysis techniques, primarily morphological examination, have cast doubt on its reliability. To address these concerns, this study employed machine learning algorithms, specifically Linear Discriminant Analysis (LDA) and Random Forest, on Direct Analysis in Real Time time-of-flight mass spectra collected from human, cat, and dog hair samples. The objective was to develop a chemistry- and statistics-based classification method for unbiased taxonomic identification of hair. The results of the study showed that LDA and Random Forest were highly effective in separating mass spectra collected from hair samples with accuracies ranging from 94-98%. This approach holds significant promise for forensic investigations, archaeology, and artifact analysis

Reliability of wood identification using DART-TOFMS and the ForeST© database: A validation study

The Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) has listed over 100 species of trees whose trade is primarily in timber, and whose existence may be endangered due to either indiscriminate or illegal logging. Species determination of timber is essential for enforcing logging quotas and species protections laws, but it is a challenging endeavor. Here we present a technique that relies on mass spectra chemotypes for species assignment and demonstrate that it provides high reliability.The reliability of timber identification by mass spectrometry relies on multiple factors: 1) access to a robust wood spectra database (e.g., ForeST ©: Forensic Spectra of Trees) derived from a curated xylarium, 2) analysis of the correct wood tissue (i.e., heartwood vs sapwood), 3) reproducibility of the spectra, 4) repeatability of the analysis and 5) skill in interpreting the data. The goal of timber identification is to assign wood products to species, but in some cases the precision of the analysis will be to the genus or even family level. This may be due, for example, to shared chemotypes among closely related tree species. These higher taxa assignments do not represent an error (false positive or false negative), but rather reflect the complexity of wood chemistry in relation to phylogeny. Such higher taxa determinations are often sufficient for enforcement purposes, as many timber trees are protected at the genus or above

Identification of selected CITES-protected Araucariaceae using DART TOFMS

Determining the species source of logs and planks suspected of being Araucaria araucana (Molina) K.Koch (CITES Appendix I) using traditional wood anatomy has been difficult, because its anatomical features are not diagnostic. Additionally, anatomical studies of Araucaria angustifolia (Bertol.) Kuntze, Araucaria heterophylla (Salisb.) Franco, Agathis australis (D.Don) Lindl., and Wollemia nobilis W.G.Jones, K.D.Hill & J.M.Allen have reported that these taxa have similar and indistinguishable anatomical characters from A. araucana. Transnational shipments of illegal timber obscure their geographic provenance, and therefore identification using wood anatomy alone is insufficient in a criminal proceeding. In this study we examine the macroscopic appearance of selected members of the Araucariaceae and investigate whether analysis of heartwood chemotypes using Direct Analysis in Real Time (DART) Time-of-Flight Mass Spectrometry (TOFMS) is useful for making species determinations. DART TOFMS data were collected from 5 species (n =75 spectra). The spectra were analyzsed statistically using supervised and unsupervised classification algorithms. Results indicate that A. araucana can be distinguished from the look-alike taxa. Another statistical inference of the data suggests that Wollemia nobilis is more similar and within the same clade as Agathis australis. We conclude that DART TOFMS spectra can help in making species determination of the Araucariaceae even when the geographic provenance is unknown

Identification of selected CITES-protected Araucariaceae using DART TOFMS

Determining the species source of logs and planks suspected of being Araucaria araucana (Molina) K.Koch (CITES Appendix I) using traditional wood anatomy has been difficult, because its anatomical features are not diagnostic. Additionally, anatomical studies of Araucaria angustifolia (Bertol.) Kuntze, Araucaria heterophylla (Salisb.) Franco, Agathis australis (D.Don) Lindl., and Wollemia nobilis W.G.Jones, K.D.Hill & J.M.Allen have reported that these taxa have similar and indistinguishable anatomical characters from A. araucana. Transnational shipments of illegal timber obscure their geographic provenance, and therefore identification using wood anatomy alone is insufficient in a criminal proceeding. In this study we examine the macroscopic appearance of selected members of the Araucariaceae and investigate whether analysis of heartwood chemotypes using Direct Analysis in Real Time (DART) Time-of-Flight Mass Spectrometry (TOFMS) is useful for making species determinations. DART TOFMS data were collected from 5 species (n =75 spectra). The spectra were analyzsed statistically using supervised and unsupervised classification algorithms. Results indicate that A. araucana can be distinguished from the look-alike taxa. Another statistical inference of the data suggests that Wollemia nobilis is more similar and within the same clade as Agathis australis. We conclude that DART TOFMS spectra can help in making species determination of the Araucariaceae even when the geographic provenance is unknown.Fil: Evans, Philip D.. University of British Columbia; CanadáFil: Mundo, Ignacio Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales. Provincia de Mendoza. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales. Universidad Nacional de Cuyo. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales; ArgentinaFil: Wiemann, Michael C.. Center For Wood Anatomy Research; Estados UnidosFil: Chavarria, Gabriela D.. United States Fish And Wildlife Service; Estados UnidosFil: McClure, Pamela J.. United States Fish And Wildlife Service; Estados UnidosFil: Voin, Doina. United States Fish And Wildlife Service; Estados UnidosFil: Espinoza, Edgard O.. United States Fish And Wildlife Service; Estados Unido