How Food Analysis Is Helping Fight Deforestation

The world has lost a lot of trees in recent years. Between 2004 and 2017, more than 166,000 square miles of forest – an area roughly the size of California – was felled in the tropics and subtropics.

Why? Short answer: food.

Lush rainforests occupy valuable land that could otherwise be used to grow acres of crops like soybean, palm oil, coffee and cocoa, or grass for herds of cattle to graze on. This kind of agricultural conversion is now occurring on a mega-industrial scale; around 90% of global forest loss this century can be attributed to agricultural expansion.

To help preserve what forest remains on this planet, regulatory bodies like the European Union have proposed legislation to prohibit the selling of goods grown on recently felled land.

But how would regulators be able to tell what to ban? A handful of soybeans grown on pastoral land look much like a handful of soybeans grown on recently wooded land.

Well, one research team at Queen’s University Belfast has developed a technique that can find the difference.

To learn more about the method, Technology Networks attended RAFA 2024 to hear from one of its pioneers, Chris Elliott, a professor of food safety at Queen’s University Belfast and founder of the university’s Institute for Global Food Security.

Location, location, deforestation

“In terms of deforestation, there are six big commodities,” Elliott told the RAFA audience. “Five of them are food-related: palm oil, beef, soy, coffee and cocoa. Wood is also included. But for those five food commodities, the global trade is $1.3 trillion. Can you imagine how many fraud opportunities there are in a trillion-dollar industry? It’s absolutely massive.”

To help combat this fraud and enforce the incoming EU deforestation law, Elliot and his research team devised an inductively coupled plasma mass spectrometry (ICP-MS) technique and trained it on hundreds of soy samples taken from across the globe.

“We collected about 1,500 samples of soya from many different parts of the world,” Elliott told the RAFA audience, “and we concentrated our efforts on several countries [in central and south America], because that constitutes 95% of the world's production and export of soya.”

Elliott’s research fellow, Maria del Mar Aparicio Muriana, went into further detail about the ICP-MS method in a RAFA presentation of her own the following day.

“We selected this technique, ICP-MS, because it’s robust and it provides quantitative results with high sensitivity, and it’s user-friendly and accessible to scientists with different levels of expertise,” she told the audience. “It gives rapid analysis and is quite cost effective when compared with other techniques such as GC [gas chromatography].”

“The experimental procedure consists of three main stages,” del Mar Aparicio Muriana explained. “The first one is the sample treatment. Here, we have to grind the soybeans to obtain a fine powder, and then we perform a digestion using a strong acid and a microwave digester. After that, we analyze the sample. Using internal standards, we analyzed 35 different soya materials that are commercially available, to make sure that our analyses are reproducible and consistent. And finally, we perform the kinematic models that help us to identify the origin of the soya; 330 samples were included in our model.”

The technique currently has an average accuracy rate of 98% – a figure that could be increased with more geo-specific sample data.

“We need to keep working on that,” del Mar Aparicio Muriana told the RAFA audience, “collecting more sample and making our model more robust.”

As it is, however, the model can still reliably differentiate between soy grown in different regions of the same country.

“Brazil has six main regions of soya production, three in the north, three in the south,” Elliot explained during his RAFA presentation. “The northern region is very much linked with the deforestation. Whenever we did the modeling, we could get wonderful differentiation between soya that can come from deforested areas and from sustainable production areas.”

Elliott says the project could have comfortably stopped at this milestone, but, thanks to further funding from Queen’s University Belfast and a grant from the UK government, his research team are turning their fraud detector on the supply chains that convey deforestation-linked food.

Tackling the supply chain gangs

“About two months ago, we were informed we had received another really large grant from Queen’s University, about six million pounds,” Elliott told the RAFA audience. “[The money will be used to] look for new ways of traceability and transparency across complex supply chains. Thankfully, we included our soya work on this, so we’ll be able to do a lot more soya in terms of tracking the origins going forward.”

The Belfast team are also using their grant from the UK government to test a new handheld near-infrared spectroscopy prototype food scanner, one coupled with artificial intelligence to determine the geographic origin of a sample in rapid time.

Elliott says the project has had considerable interest from industry partners, who are concerned about the incoming EU legislation and a similar law in the UK.

“We’ve enlisted many, many companies in different parts of the world who want to do the right thing,” Elliott told the RAFA audience. “They want to know where their soya comes from, partly because they’re extremely worried about the European legislation, but also they want to claim sustainability around their own businesses.”

In the closing remarks of their respective presentations, both Elliott and del Mar Aparicio Muriana noted that the project’s next steps include obtaining ISO accreditation and inviting more partners to help inform the technique’s measurement system.

“Anybody who wants to join our coalition, please come and see us,” Elliott added.