Fraudulent practices involving food have endured across centuries, with origins dating back to the medieval period when spices, a prized commodity, were frequently adulterated or misrepresented. In recent times, food fraud has adopted new and clever guises. In the 2008 Chinese melamine and the 2013 European horse meat incidents, malevolent actors manipulated labels and ingredients to misrepresent the quality and origin of the food products, threatening public trust in global supply chains.
“People who adulterate food, they're often pretty smart,” says Chris Elliott, Professor of Food Safety at Queen’s University Belfast, UK, and founder of the Institute for Global Food Security. “They set out to actually evade detection by different scientific methods.”
Mass spectrometry (MS) has emerged as an essential tool in food authenticity and fraud detection, thanks to its unparalleled specificity, accuracy, and sensitivity. In an expert discussion forum hosted by Separation Science, Elliot and colleagues Chiara Cordero from the University of Turin, Italy, and Stéphane Bayen from McGill University, Canada, consider how advances in MS technology not only enable the detection of unique ‘fingerprints’ that can authenticate food, but also hold potential for simultaneous determination of quality, contaminants, and place of origin.
Thwarting food fraud
Mass spectrometry traditionally excels in targeted analysis, where known substances are identified and quantified. For example, using targeted liquid chromatography-tandem mass spectrometry (LC-MS/MS), analysts can reliably quantify pesticide residues in food samples. This technique is particularly suited for analyzing a predefined set of pesticides, making it a valuable tool for ensuring food safety.
However, targeted methods fall short when it comes to identifying new or unknown substances, additives, or contaminants that may be introduced to food or arise from chemical changes caused by factors such as storage duration and environmental conditions.
“We have moved towards a more proactive approach in our laboratory, what we call non-targeted analysis,” says Bayen. “We appy this approach to honey or maple syrup; take a few milligrams of sample, mix it with some solvent, filter it, and inject it inside the LC-QTOF-MS (where QTOF stands for quadrupole time of flight). It takes only a few minutes to obtain the chemical fingerprint of this food product.”
Typically, applying broad, non-targeted screening processes is challenging, especially in the context of food matrices. This is due to the inherent intricacy and variety of the samples, along with the wide range of analyte concentrations present. To address these issues, the forum panelists advocate using techniques such as two-dimensional liquid chromatography, which can separate overlapping peaks and create distinct chemical fingerprints for different products.
“We can see thousands of signals and chemicals inside the sample, which is important in terms of food fraud,” Bayen notes. “Obtaining such a complex fingerprint, made of thousands of trace chemicals, it is almost virtually impossible for fraudsters to imitate.”
Cordero has championed the concept of chemical fingerprinting for close to a decade, leading major advances in ‘electronic noses’ that meld MS with computational methods, including machine learning. Part of her research zeroes in on analyzing volatile chemicals using two-dimensional gas chromatography (GC×GC) paired with headspace solid-phase microextraction (HS-SPME). She employs this approach on food items like olive oil and cocoa beans to discern factors including origin and quality.
“Mass spectrometry gives a specificity that is fundamental for using these fingerprints for authentication,” explains Cordero. “But at the same time, every feature in these patterns can be potentially identified and quantified for other types of profiling.”
Bayen, whose latest study reveals the presence of commercial flame retardants in honey through non-targeted analysis, shares Cordero's sentiment about MS emerging as a versatile instrument for food analysis.
“We work with honey, and in a single run with LC-MS, we can see compounds such as phenolic compounds that can relate to the floral origin,” says Bayen. “You can see markers of freshness and information about the packaging of the product. That's where mass spectrometry is quite unique—it provides all this information at the same time.”
From lab to locale
Elliot, who led the independent review of the UK food system after the 2013 horse meat scandal, highlights during the panel discussion a significant hurdle for mass spectrometry in food analysis: transitioning instrumentation from research labs to actual food production locales.
“I have many years experience trying to transfer methodologies between laboratories, and a lot of it is about standardization of protocols,” states Elliot. “Often when you do transfer methodologies, you will have reference samples, quality control samples. But in the world of food authenticity, they don't really exist—you can't go and buy ten reference samples for honey.”
Recently, Elliot and his colleagues revealed a technology that can classify salmon according to their origin and production methods with 100% accuracy. The researchers took data from rapid evaporative ionization mass spectrometry (REIMS) and inductively coupled plasma mass spectrometry (ICP-MS) measurements and used artificial intelligence-powered algorithms to analyze multiple variables at the same time, boosting accuracy compared to any single method currently available.
Bayen sees the emerging trend toward ‘big data’ in food forensics as both an opportunity and a major obstacle when it comes to transferring methodologies between labs.
“If you think about a full authenticity study, you can quickly achieve several hundreds of gigabits, but that’s not the main challenge,” Bayen states. “Everyone has a different approach to processing data, and few people report their data processing conditions. We need work in this field, perhaps some standardization.”
A call for openness
Sharing insights from an academic perspective, Cordero underscores the necessity of education, training, and practical application as the cornerstone for successfully adopting new innovations in analytical laboratories. She notes that the fresh outlooks often exhibited by undergraduate students can stand in stark contrast to the more resistant stances of industry veterans.
“With new technologies that require a change in perspective, for example, in the data processing workflows, there can be skepticism from experienced analysts,” says Cordero. “Discussions like this are also a tool to promote the widespread adoption of new technologies in more laboratories. This could be a good option to improve the quality of what we eat every day.”
Discover more insights and details about food authenticity and fraud by viewing the original expert discussion forum, exclusively on Separation Science.
Adam Dickie is a science writer for Separation Science. He can be reached at email@example.com.
Prof. Chris Elliott, PhD, FRSC, FRSB, MRIA, OBE (Queen’s University Belfast) is Professor of Food Safety and founder of the Institute for Global Food Security at Queen’s University Belfast. He has published more than 680 peer-reviewed articles relating to the detection and control of agriculture, food, and environmental-related contaminants and fraud.