Clay and soil organic matter drive the multi-elemental wood composition of a tropical tree species: Implications for wood tracking

While tracing the origin of products by analyzing their multi-element composition has been successfully applied in several commodities, the potential to do the same with wood has yet to be determined. Laura Boeschoten and her team studied the soil reactive element pools and the multi-element composition of sapwood and heartwood for 37 Azobé (Lophira alata) trees at two forest sites in Cameroon, measuring 46 elements using inductively coupled plasma mass spectrometry (ICP-MS), and conducted the first study linking the multi-elemental composition of forest tree wood to chemical and physical properties of soil in an effort to combat the illegal timber trade. Laura spoke to Spectroscopy about this study and the potential of the technique.

Your article (1) deals with the use of forensic methods, in particular ICP-MS, to independently trace the origin of timber in order to combat the illegal timber trade. Briefly explain why you chose this technique over others.

ICP-MS has shown great potential for identifying the origin of products, for example, for wine and cabbage. One of the advantages of the method is that you can measure a large number of elements simultaneously, thus easily providing a lot of information. That’s why we thought it would be interesting to try this for wood as well.

How can the multi-elemental composition provide a chemical fingerprint for wood tracing?

The trees have a different multi-elemental composition depending on the region. We are looking for differences between locations, to find something like the fingerprint or barcode of a specific site. Based on this, you can compare the chemical composition of wood of unknown origin to a benchmark data set. This tells you where your sample is most similar, which can help trace the location when the origin is unknown or needs to be verified.

Please describe in detail how you applied random forest analysis to determine the origin of the timber.

We used a classification model to test if we could correctly assign our trees to their respective origin. We chose a random forest model for this because it is a robust machine learning method that compares many different decision trees and then gives you the best classification model for your data set. The random forest model can thus predict the most probable origin of the wood according to the chemical composition.

Please describe your process of discovery and development, and how it differed from what you did before or others.

We were the first to show the potential of multivariate analysis to trace the origin of wood. The idea was developed a few years ago when our team was researching other methods for the same purpose, and they saw the method being applied to other products. We then tested the variation of wood and tried to understand what drives the differences in wood chemistry between different origins based on soil composition to be able to apply the method to wood.

Please summarize your findings.

We found that wood from two locations could be attributed to the correct origin with great success, and we found that wood chemistry was primarily associated with soil clay content and soil organic matter.

What are the implications of your research and how do you see it combating the illegal timber trade, in the short and long term?

We hope to test this method on a larger scale and on other species, to better understand how it works and where it has the most potential. It will then be a great addition to the wood scribing toolbox; with other methods, we will be able to independently verify the origin of the wood. This is an essential step in the fight against the illegal timber trade, as currently the origin is only verified by external documents and labels, both of which are susceptible to falsification.

Have you encountered any particular difficulties in your work?

Most of the challenges of this project were related to field work. Of course, we needed a lot of wood and dirt to test this method, so we had all the challenges of setting up a field campaign. You need all the right people and the right equipment in the right place at the right time, which was sometimes difficult. And then Covid started, so we outsourced most of our field work to local partners. This strengthened our collaborations, but also made it more difficult to understand what was happening on the ground.

What kind of feedback have you received regarding this work?

We have received a lot of positive feedback on our project: forestry companies, competent authorities, non-profit organizations and the Dutch customs laboratory have even been involved from the start and are very enthusiastic about the progress.

What are the next steps in this research?

As explained previously, the next step will be to test this method on a larger scale with more species. This will improve our understanding of the full potential of the method.

Reference

(1) LE Boeschoten, U. Sass-Klaassen, M. Vlam, RNJ Comans, GF Koopmans, B. Rocha Venâncio Meyer-Sand, SN Tassiamba, MT Tchamba, HT Zanguim, PT Zemtsa and PA Zuidema, Science. About. 849, 157877 (2022). https://doi.org/10.1016/j.scitotenv.2022.157877.

Laura Boeschoten obtained her BSc and MSc at Utrecht University (Netherlands) and is currently a PhD candidate at Wageningen University (Netherlands), in the Timtrace project, led by Prof. Pieter Zuidema. She studies the chemical methods of tracing wood. Direct correspondence with: [email protected]