Two young researchers at the University of Albany, New York, have secured a $275,000 American National Science Foundation grant to continue work on ‘mass spectrometry’ to create a distinct chemical fingerprint for tree species.
Rabi Musah, a chemist and professor at the university, and Allix Coon, a PhD graduate, say their method of wood species identification relies on an analytical technique used to identify the composition of a sample by measuring the mass-to-charge ratio of atoms and molecules – chemically.
The approach is comparable to a person’s fingerprint matched against a database. Trees have chemical fingerprint signatures that are species-specific. Through mass spectrometry, coupled with advanced machine learning tools, the research team has created a chemical signature database of domestic woods for tree species identification purposes.
The wood samples can be tested in their native form (such as logs, planks, shavings, or finished goods), and the results will be available in 15 minutes.
“When a database of plant chemical signatures is created, we will have a positive ‘hit’ on the identity of the plant sample,” Allix Coon said.
Professor Musah and Allix Coon are co-founders of Sangali, a start-up company based at the university and focused on developing the technique and bringing it to market.
“Various national and international agencies have instituted restrictions on trade to combat the illegal harvesting of threatened and endangered wood species,” Professor Musah said.
“But the success of such restrictions hinges on accurate identification,” she said. “The Sangali laboratory aims to fulfill the critical need for accurate, reliable, and rapid species and origin identification of wood, whether in the form of a log or any products fashioned from it.”
The National Science Foundation grant will help acquire verified domestic wood species, run the mass spectral analysis and develop a robust database.
In addition, Sangali is investigating the technique’s ability to identify mixed-species composite materials and determine the geographic origin of wood species. Other ongoing work includes the development and refinement of the machine learning models that support the analysis.
Research has also started to provide analysis on-site, rather than in a laboratory, through a portable device that rapidly scans the wood product for species identification.
Professor Musah describes illegal logging as a “crime that happens in plain sight.”
“Hundreds or thousands of shipping containers move timber products across the globe,” she said.
Amidst a remarkable surge in the global wood processing industry, valued at more than US $150 million, the sector is continuously threatened by illegal wood products, especially in tropical regions, which not only jeopardise economies but also pose threats to plant and wildlife species and climate change.
