Rapid identification of wood species using XRF and neural network machine learning

Aaron N Shugar; B Lee Drake; Greg Kelley

doi:10.1038/s41598-021-96850-2

Rapid identification of wood species using XRF and neural network machine learning

Sci Rep. 2021 Sep 2;11(1):17533. doi: 10.1038/s41598-021-96850-2.

Authors

Aaron N Shugar¹, B Lee Drake², Greg Kelley³

Affiliations

¹ Garman Art Conservation Department, Buffalo State College - SUNY, Buffalo, NY, USA. shugaran@buffalostate.edu.
² Department of Anthropology, University of New Mexico, Albuquerque, NM, USA.
³ Collections, Curatorial and Conservation Branch, Indigenous Affairs and Cultural Heritage Directorate, Parks Canada, Government of Canada, Ottawa, ON, Canada.

Abstract

An innovative approach for the rapid identification of wood species is presented. By combining X-ray fluorescence spectrometry with convolutional neural network machine learning, 48 different wood specimens were clearly differentiated and identified with a 99% accuracy. Wood species identification is imperative to assess illegally logged and transported lumber. Alternative options for identification can be time consuming and require some level of sampling. This non-invasive technique offers a viable, cost-effective alternative to rapidly and accurately identify timber in efforts to support environmental protection laws and regulations.

Publication types

Research Support, Non-U.S. Gov't