How Sensors Detect Food Adulteration and Mislabeling

By Ankit SinghReviewed by Frances BriggsJul 31 2025

Food fraud is getting easier, but the tools to catch it are getting smarter. From AI-powered sensors to smart packaging, technology is reshaping how adulteration and mislabeling are detected. 

Image Credit: ultramansk/Shutterstock.com

Across global supply chains, food adulteration and mislabeling continue to erode consumer trust, compromise safety, and challenge regulatory oversight. Whether it's substituting premium olive oil with cheaper blends or passing off farmed fish as wild-caught, such practices not only deceive consumers but can pose serious health risks.

Now, an array of advanced sensors are giving regulators and producers a fighting chance. As these technologies mature, the ability to verify authenticity and detect fraud is shifting from the lab bench to the supermarket shelf. Whether mimicking human senses or drawing on machine learning, food fraud is on the way out. 

The Scale of the Problem

Food adulteration is the intentional altering of a product, whether by adding, substituting, or removing ingredients, that compromises its quality. Producers usually do these things to boost profits. Mislabelling is another issue, and it can compromise the product's identity, origin, or composition. 

What makes the problem more acute today is the complexity of modern supply chains. A product might change hands dozens of times between origin and point-of-sale. The more steps, the greater the opportunity for deception, and the harder it becomes to trace.^1,2,3

This is where sensors step in. Researchers and manufacturers are increasingly reliant on sensor technology to identify fraud, contamination, and substitution in products. Sensors can offer precise and nondestructive ways to do this, and some are even portable enough for use. 

Different Sensors Have Different Advantages 

Spectroscopic Sensors

Spectroscopic sensors reveal what's inside a product without destroying it. They use the properties of electromagnetic waves interacting with food, including infrared (IR), near-infrared (NIR), Raman, and ultraviolet-visible (UV-Vis) spectroscopy.^2,4

They analyze molecular vibrations, scattering, and absorption to identify potential adulterants and verify the authenticity of food products. 

For example, Raman spectroscopy can distinguish between authentic and adulterated honey, oils, or milk by detecting unique molecular fingerprints. Similarly, Fourier-transform infrared spectroscopy (FTIR) and NIR have been proven to be especially useful in detecting adulterants in fat-rich foods, coffee, and flour.^2,4

Chromatographic Sensors

When the fraud is subtler, we need more sensitive techniques. Chromatographic sensors combine the separation of sample components with detection using optical or electrical signals.

Liquid chromatography coupled with mass spectrometry (LC-MS) and gas chromatography-mass spectrometry (GC-MS) are effective techniques for identifying adulterants within complex matrices like spices, oils, and meat products. These platforms are known for their high sensitivity and specificity, detecting both intentional mislabeling and small traces of adulteration, which helps maintain the integrity and quality of food products.²

Electronic Noses and Tongues

Inspired by biology, electronic noses (e-noses) mimic human senses to detect volatile compounds that give food its aroma. Similarly inspired, electronic tongues (e-tongues) function by measuring the electrical signals generated when food components interact with electrodes. The sensor arrays provide complex data, which is analyzed with machine learning tools to identify patterns corresponding to specific adulterants or authentic profiles.

These tools can identify spoiled fish, watered-down milk, or off-spec fruit juice. Their strength lies not in pinpointing a single chemical, but in recognizing familiar profiles that correspond to authentic or tampered foods.^1,6

Biosensors

Biosensors go a step further into biological mimicking, using enzymes, antibodies, or even nucleic acids as recognition elements, combined with electronic or optical detectors. They are used to spot pathogens, allergens, or toxins, and also to verify species identity through DNA-based assays.

Portable biosensors integrated with microfluidic chips allow rapid on-site testing, ideal for detecting food adulteration on the spot. Some biosensors have even been designed for direct integration into food packaging, providing continuous monitoring during transportation and storage.⁷

Physical Sensors

Changes to basic physical characteristics, such as refractive index, density, or dielectric properties, can also indicate food fraud. For example, if cheap fillers are added to oils, their optical properties shift in measurable ways.

Physical sensors that can detect these changes can carry out rapid, high-throughput screening.⁸

Smarter, Connected, Real-Time Sensing

A wave of innovation is happening in sensors. Traditional sensors are being integrated with digital platforms, equipped with microcontrollers and cloud connectivity, and embedded into AI. These IoT (Internet of Things) sensors can process data locally, flag anomalies, and send real-time results across supply chains.^7,9 

When sensors are equipped with this tech, they can upload results to the cloud or blockchain systems, integrating analytical chemistry, data science, and communications for instantaneous results. Being able to do this is supporting suppliers with transparency and traceability. 

Implementing Sensors Across Supply Chains

In-Plant and Point-of-Entry Testing. 

Producers and regulators now use sensors at food processing plants, import/export locations, and storage facilities. Rapid screening tools based on spectroscopy or e-nose arrays allow the food industry to conduct high-throughput inspections of bulk goods with minimal disruption to logistics.

For example, Raman spectrometers allow customs food inspectors to quickly verify the authenticity of imported teas, ensuring compliance with safety and quality standards without causing significant delays.^10,11

Retail and Consumer-Level Detection

Some devices are small enough to be used outside of factories and labs. One example is the READ FWDx, which customers or staff can use on the shop floor. These devices can simultaneously identify multiple contaminants or adulterants within minutes, making quality assurance and consumer protection more practical. Their design prioritizes accessibility, making it easier to assess product quality in everyday settings.^12,13

Packaging Innovations

Advanced packaging technologies now allow sensors or biosensor labels to be embedded within containers. These smart packages monitor environmental conditions, spoilage, or tampering and can transmit data to cloud systems. Blockchain integration ensures data integrity and supports traceability all the way to consumers, who can access information via quick response (QR) codes or Radio-Frequency Identification (RFID) tags.⁷

Download your PDF copy now!

Enhancing Detection Through AI

Sensors generate huge amounts of data. To rationalise such large information streams, machine learning has stepped up to interpret the complex output into something more easily processed. Algorithms can classify samples, identify deviations, and even predict adulteration risks.

Using iterative learning models, systems continue to improve their accuracy, adapting to new fraud strategies or emerging adulterants. Additionally, AI enables the integration and interpretation of results from multiple sensor types. A combined system can assess optical signals, biosensor responses, and VOC profiles, generating robust conclusions even in challenging testing scenarios.^5,9

Addressing the Challenges of Sensor Deployment

As with all technological advances, these fraud-busting sensors have outstanding problem areas. It is difficult for reliable, environmentally stable sensors to be produced that can monitor extremely diverse food matrices, and researchers are continuing to try to strike the perfect balance. As sensors move from the laboratory to real-world settings, scientists focus on minimizing false positives, reducing sensitivity to confounding variables, and improving portability.⁷

Sensor systems must also adapt to evolving criminal practices, handle various adulterants, and address data privacy concerns while managing large volumes of information.⁷

Recent Advances, What's Next?

However, sensor technology is entering a new phase of development. Optical and electrochemical biosensors are more sensitive and selective than ever, and portable spectroscopic devices are approaching market readiness. Blockchain-linked sensors combined with real-time analytics promise continuous, tamper-proof supply chain monitoring. Modern point-of-care devices can assess contamination risks in food and water in under ten minutes, supporting more frequent and broader screening.^7,12,13

Research demonstrates that next-generation sensor systems, augmented by AI, offer improved accuracy and support autonomous decision-making. Industry and regulatory bodies are strategically investing in these tools not just for detection but for deterrence, promoting food integrity and accountability across markets.^5,10,11

As food systems grow more complex, these tools offer something increasingly valuable: more confidence in what we eat.

References and Further Reading

1. Pulluri, K. K., & Kumar, V. N. (2022). Qualitative and Quantitative Detection of Food Adulteration Using a Smart E-Nose. Sensors, 22(20), 7789. DOI:10.3390/s22207789. https://www.mdpi.com/1424-8220/22/20/7789
2. Anagaw, Y. K. et al. (2024). Food adulteration: Causes, risks, and detection techniques—Review. SAGE Open Medicine, 12, 20503121241250184. DOI:10.1177/20503121241250184. https://journals.sagepub.com/doi/10.1177/20503121241250184
3. Haji, A., Desalegn, K., & Hassen, H. (2023). Selected food items adulteration, their impacts on public health, and detection methods: A review. Food Science & Nutrition, 11(12), 7534-7545. DOI:10.1002/fsn3.3732. https://onlinelibrary.wiley.com/doi/full/10.1002/fsn3.3732
4. He, Y. et al. (2021). Detection of adulteration in food based on nondestructive analysis techniques: a review. Critical Reviews in Food Science and Nutrition, 1–21. DOI:10.1080/10408398.2020.1777526. https://www.tandfonline.com/doi/full/10.1080/10408398.2020.1777526
5. Othman, S. et al. (2023). Artificial intelligence-based techniques for adulteration and defect detections in food and agricultural industry: A review. Journal of Agriculture and Food Research, 12, 100590. DOI:10.1016/j.jafr.2023.100590. https://www.sciencedirect.com/science/article/pii/S2666154323000972
6. Mehdizadeh, S. A. et al. (2023). Development of an Innovative Optoelectronic Nose for Detecting Adulteration in Quince Seed Oil. Foods, 12(23), 4350. DOI:10.3390/foods12234350. https://www.mdpi.com/2304-8158/12/23/4350
7. Sobhan, A. et al. (2025). IoT-Enabled Biosensors in Food Packaging: A Breakthrough in Food Safety for Monitoring Risks in Real Time. Foods, 14(8), 1403. DOI:10.3390/foods14081403. https://www.mdpi.com/2304-8158/14/8/1403
8. Agrawal, U. et al. (2024). Design & development of adulteration detection system by fumigation method & machine learning techniques. Scientific Reports, 14(1), 1-16. DOI:10.1038/s41598-024-64025-4. https://www.nature.com/articles/s41598-024-64025-4
9. Gundavarapu, M. R. et al. (2023). IoT-Powered Intelligent Framework for Detecting Food Adulteration: A Smart Approach. E3S Web of Conferences. 430, 01074. ICMPC 2023. DOI:10.1051/e3sconf/202343001074. https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/67/e3sconf_icmpc2023_01074.pdf
10. 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
11. Vinothkanna, A. et al. (2024). Advanced detection tools in food fraud: A systematic review for holistic and rational detection method based on research and patents. Food Chemistry, 446, 138893. DOI:10.1016/j.foodchem.2024.138893. https://www.sciencedirect.com/science/article/pii/S0308814624005429
12. Fauzia, M. (2025). Scientists create food sensor that detects unwanted bacteria, chemicals. Phys.org. https://phys.org/news/2025-07-scientists-food-sensor-unwanted-bacteria.html
13. UT Dallas scientists create food sensor that detects unwanted bacteria, chemicals. (2025). The Spokesman Review. https://www.spokesman.com/stories/2025/jul/15/ut-dallas-scientists-create-food-sensor-that-detec/

Disclaimer: The views expressed here are those of the author expressed in their private capacity and do not necessarily represent the views of AZoM.com Limited T/A AZoNetwork the owner and operator of this website. This disclaimer forms part of the Terms and conditions of use of this website.