Can You Really Trust What’s on Your Plate? How Sensor Tech is Catching Food Fraudsters

By Ankit SinghReviewed by Bethan DaviesApr 17 2025

Food fraud is more than a labeling issue—it’s a global challenge that affects consumer safety, brand trust, and the integrity of supply chains. Whether it’s watered-down olive oil, misrepresented seafood, or milk contaminated with melamine, these practices are often hard to detect and costly to address.

Image Credit: Sauko Andrei/Shutterstock.com

The traditional methods used to spot fraud, like chromatography or wet chemistry, are accurate but slow, expensive, and tied to centralized labs. Today, a wave of emerging technologies is changing the game. From portable chemical sensors to AI-powered analysis, food fraud detection is becoming faster, more precise, and more accessible.

Let’s explore how these innovations are making it harder for fraud to go unnoticed—and easier for producers, regulators, and even consumers to verify authenticity.

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The Growing Threat of Food Fraud

The scale of the problem is staggering: food fraud costs the global economy an estimated $30–40 billion annually.^1,2 And it’s not limited to obscure products. Everyday items like honey, spices, seafood, and oils are common targets.

Sometimes the tampering is relatively harmless, like adding fillers. Other times, it’s dangerous. Think Sudan I dye in chili powder, or methanol in alcohol. Major incidents, such as the 2008 Chinese melamine scandal and the 2013 European horsemeat crisis, highlighted just how vulnerable global supply chains can be.

Traditional detection techniques—like GC-MS or HPLC—are highly effective, but their limitations are clear: They require specialized labs, trained personnel, and significant time. Meanwhile, food fraud continues to evolve.

This is where next-generation tools come in.³

Smarter Sensors for a Faster Response

Modern spectroscopic and chemical sensors are changing how we approach food safety. These devices analyze the molecular structure of food in real time, often without damaging the sample. And because they’re portable, they can be used in places where traditional lab equipment simply can’t go.

Raman Spectroscopy: Speed and Sensitivity

One of the most promising tools is Raman spectroscopy, which reads how light scatters off a food sample to reveal its chemical makeup. It’s fast, non-invasive, and effective at detecting a wide range of adulterants—from pesticides to melamine.

In particular, surface-enhanced Raman spectroscopy (SERS) boosts sensitivity by using nanomaterials, making it possible to detect contaminants at much lower concentrations.^3,4

These devices are now compact enough to test liquids like spirits on-site, measuring ethanol and methanol levels without opening the bottle. Even packaging no longer poses a major obstacle. Spatially offset Raman spectroscopy (SORS) allows sensors to read through layers, making them useful for products like coffee or turmeric that traditionally interfere with standard Raman readings.

Infrared Spectroscopy: A Broader View of Food Quality

While Raman excels in chemical fingerprinting, infrared (IR) spectroscopy offers a broader, more affordable approach, especially for routine quality control.

Near-infrared (NIR) and Fourier-transform infrared (FT-IR) systems are widely used in food production to measure moisture, fat, and protein. Portable NIR scanners, such as TellSpec’s Enterprise Food Scanner, can assess meat aging with up to 95 % accuracy by analyzing collagen breakdown.⁵

FT-IR also shines when it comes to spotting substitution. It can differentiate between extra virgin olive oil and diluted blends with remarkable precision, identifying adulteration at just 7 % dilution.^5,6 When paired with chemometric models, it’s even capable of detecting synthetic dyes in spices and honey that might otherwise go unnoticed.

Nuclear Magnetic Resonance (NMR) and Mass Spectrometry

For complex products like wine, chocolate, or honey, deeper molecular insight is often necessary. That’s where NMR and mass spectrometry come into play.

Nuclear magnetic resonance (NMR) provides detailed structural data, helping analysts spot everything from added sugars in honey to textural inconsistencies in chocolate caused by inferior fats.⁷

Meanwhile, mass spectrometry imaging (MSI) techniques like MALDI-MSI can map the spatial distribution of compounds in a food sample, making it possible to visualize exactly where a contaminant is present. Advanced tools like high-resolution MS (HRMS) can even detect mycotoxins at trace levels—down to 0.001 ng/mL—far beyond the reach of traditional tests.³

Chemical Sensors and Biosensors: Real-Time, On-the-Spot Testing

Not every test needs to dig deep. In many cases, a quick, targeted answer is enough, and that’s where chemical sensors and biosensors thrive.

Take the example of edible oils. These are often blended with cheaper alternatives that are hard to detect with a single test. But by combining multiple types of data—like fatty acid profiles, vitamin E content, and FT-IR signals—researchers have developed multi-sensor models that flag fraudulent blends with impressive accuracy.^4,8

Other tools, like laser-induced breakdown spectroscopy (LIBS) and X-ray fluorescence (ED-XRF), can identify trace elements like arsenic or lead, especially useful for verifying the origin of spices, where soil composition leaves a chemical signature.

Then there are biosensors, which use biological components to detect specific contaminants. For instance, gold nanoparticles coated with aptamers change color in the presence of amoxicillin in milk, and a smartphone camera can read the results in minutes.

Pair this with microfluidic platforms, and you’ve got a compact system capable of trapping pathogens like Salmonella or E. coli and analyzing them without needing to culture samples.

The AI Advantage: Making Sense of All the Data

All of these tools generate an enormous amount of data, but raw numbers only go so far. That’s where artificial intelligence and machine learning step in.

AI can help identify patterns invisible to traditional analysis. For example, convolutional neural networks (CNNs) can analyze hyperspectral images and spot signs of adulteration with up to 99.85 % accuracy.³

Beyond detection, AI is also being used for fraud prediction. Germany’s ISAR system, for example, monitors imports of over 2400 food items from 220 countries. By tracking anomalies in quantity and pricing over time, it can flag potential fraud before it reaches consumers.¹

Case Studies: Success Stories That Changed the Industry

These tools aren’t just theoretical—they’ve already helped solve some of the food industry’s most high-profile cases.

Melamine in Milk (China): Turning a Tragedy into a Tech-Driven Safeguard

In 2008, one of the most devastating cases of food fraud came to light when it was discovered that milk and infant formula in China had been adulterated with melamine, a nitrogen-rich industrial compound added to artificially inflate protein readings during nutritional tests. The scandal sickened more than 50,000 infants and resulted in several deaths, sparking global outrage and exposing serious gaps in food quality oversight.^1,2

In the aftermath, the Chinese dairy industry faced intense scrutiny and pressure to restore consumer trust. This crisis became a turning point for sensor deployment in food safety. Surface-enhanced Raman spectroscopy (SERS) was introduced as a rapid and effective method to detect melamine at very low concentrations. The technology proved ideal for on-site testing—something traditional lab methods struggled to achieve.

Today, portable SERS devices are widely used at milk collection centers throughout China. These handheld tools allow producers and regulators to analyze raw milk in real time, flag suspicious samples instantly, and upload data to centralized cloud platforms for further oversight. The result is a more agile, transparent, and tech-enabled monitoring system that helps prevent another crisis of this scale.

Horsemeat in Beef (Europe): Unmasking Hidden Substitutions Through DNA and Isotope Testing

Just a few years later, another food fraud scandal shook Europe—this time involving undeclared horsemeat found in products labeled as beef. In 2013, routine testing in Ireland uncovered that frozen meat products sold across several countries contained horse DNA, sparking widespread concern about labeling integrity and supply chain transparency.²

To uncover the scope of the fraud, scientists deployed a mix of polymerase chain reaction (PCR) DNA analysis and isotope ratio mass spectrometry (IRMS)—technologies that could pinpoint not only the presence of horsemeat, but also provide clues about its geographic origin.

The results were sobering: the mislabeling wasn’t a one-off mistake but part of a larger, coordinated network of food substitution across multiple suppliers and distributors. This incident revealed how deeply fraud could be embedded in complex, multinational supply chains—and how difficult it could be to trace without advanced tools.

In response, regulatory bodies in the EU introduced stricter rules on traceability and documentation. Perhaps most notably, the scandal accelerated interest in blockchain-based traceability platforms, which offer tamper-proof digital records of a product’s origin, handling, and movement through the supply chain. Several large retailers and meat processors have since adopted these systems, allowing real-time tracking and greater transparency from farm to consumer.

The Future of Food Authentication

Looking ahead, innovation is pointing in two key directions: smarter models and stronger traceability.

One exciting development is federated learning (FL)—a method that lets AI systems learn from multiple data sources without sharing the actual data. This approach supports real-time fraud prediction while respecting privacy and General Data Protection Regulation (GDPR) compliance.¹⁰

At the same time, blockchain technology is being used to build tamper-proof supply chain records. With backing from the EU’s Horizon Europe program, initiatives are underway to apply smart labels and digital tracking to high-risk products like saffron and olive oil.

On the consumer side, portable 3D-printed sensors and IoT-connected tools are putting testing power directly into people’s hands, allowing real-time checks for contaminants like melamine or pesticide residues without the need for a lab.^11,12

Final Thoughts

Food fraud isn’t just a regulatory challenge—it’s a technological one. But with the rise of smart sensors, AI-driven analysis, and real-time testing tools, we’re moving toward a more transparent and trustworthy food system.

What used to take days in a lab can now happen in minutes, often right where the food is made, packaged, or sold. And as these technologies become more widely adopted, they won’t just catch fraud—they’ll prevent it.

Want to Learn More?

Interested in what’s next? Take a closer look at some of the below related articles:

• Food Sensors, Explained: Smarter Safety from Farm to Fork
• Insights into Food Fraud
• ATR-FTIR in the Food and Beverage Industry
• Genetic Barcoding in Seafood: Ensuring Authenticity and Traceability in Aquaculture
• What are the Most Common Adulterated Foods?
• Wine Analysis: From Quality Control to Authenticity Verification

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References and Further Reading

1. Ulberth, F. (2020). Tools to combat food fraud – A gap analysis. Food Chemistry, 330, 127044. DOI:10.1016/j.foodchem.2020.127044. https://www.sciencedirect.com/science/article/pii/S0308814620309067
2. Houlton, S. Fighting food fraud. Chemistry World. https://www.chemistryworld.com/features/fighting-food-fraud/8569.article
3. Ziani, I. et al. (2025). Integrating AI and advanced spectroscopic techniques for precision food safety and quality control. Trends in Food Science & Technology, 156, 104850. DOI:10.1016/j.tifs.2024.104850. https://www.sciencedirect.com/science/article/pii/S0924224424005260
4. Jr, J. W. (2024). A Review of the Latest Spectroscopic Research in Food and Beverage Analysis. Spectroscopy Online. https://www.spectroscopyonline.com/view/latest-spectroscopic-research-in-food-and-beverage-analysis
5. Spectroscopic Techniques for Food Analysis to Combat Adulteration and Fraud. Applied Sciences from Technology Networks. https://www.technologynetworks.com/applied-sciences/articles/spectroscopic-techniques-for-food-analysis-to-combat-adulteration-and-fraud-294550
6. Sharma, R. et al. (2024). Rapid and sensitive approaches for detecting food fraud: A review on prospects and challenges. Food Chemistry, 454, 139817. DOI:10.1016/j.foodchem.2024.139817. https://www.sciencedirect.com/science/article/pii/S0308814624014675
7. Wetzel, W. (2024). A Look at Spectroscopy and Food Adulteration: Current and Future Challenges. Spectroscopy Online. https://www.spectroscopyonline.com/view/a-look-at-spectroscopy-and-food-adulteration-current-and-future-challenges
8. Liu, M. et al. (2025). Authentication of Edible Oil by Real-Time One Class Classification Modeling. Foods, 14(7), 1235. DOI:10.3390/foods14071235. https://www.mdpi.com/2304-8158/14/7/1235
9. Giussani, B., & Riu, J. (2023). Biosensors and Smart Analytical Systems in Food Quality and Safety: Status and Perspectives. Foods, 12(12), 2292. DOI:10.3390/foods12122292. https://www.mdpi.com/2304-8158/12/12/2292
10. Gavai, A. et al. (2023). Applying federated learning to combat food fraud in food supply chains. Npj Science of Food, 7(1), 1-9. DOI:10.1038/s41538-023-00220-3. https://www.nature.com/articles/s41538-023-00220-3
11. Future Of Food Safety: Next-Gen Technologies On The Rise. (2024). FoodSafetyTech. https://foodsafetytech.com/feature_article/future-of-food-safety-next-gen-technologies-on-the-rise/
12. TOOLS GUIDES AND REPORTS. FoodAuthenticity. https://www.foodauthenticity.global/tools-guides-reports

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