Innovations in Food Testing Tech: Ensuring Safety From Chemical and Microbial Contaminants
As the complexity of food systems grows, so does the need for accurate and efficient detection methods.
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Food safety is a key priority in the food industry due to the health and economic risks from contamination and adulteration. Each year, millions of food samples are tested worldwide to detect contaminants, verify labeling and ensure compliance with safety regulations.
This article explores the latest advancements in food testing – from cutting-edge chemical analysis to AI-driven data analytics – highlighting how they are enabling faster, more reliable testing to ensure the safety of the global food supply chain.
Types of food contamination
Contaminants can enter food at any stage and may result from environmental exposure, poor hygiene, equipment failure or fraudulent practices.
As Dr. Luís Abrunhosa, an assistant researcher at the University of Minho explained, “Food contaminants are substances that can inadvertently enter food and pose health risks to consumers. Depending on their origin, they are classified as biological, chemical or physical contaminants.”
Biological contaminants
Food contaminants of biological origin are harmful microorganisms or biological substances that can cause foodborne illnesses and are some of the most common and dangerous types of contaminants found in food.
Examples include bacteria such as Salmonella, Listeria monocytogenes, Escherichia coli and Clostridium botulinum, viruses such as norovirus and hepatitis A, and parasites including Giardia, Trichinella or Toxoplasma gondii.
A particular threat are contaminants resultant of microbial activity, such as mycotoxins produced by fungi, toxins from Clostridium or Staphylococcus and marine biotoxins.
Chemical contaminants
Chemical contaminants can occur naturally or result from human activity. They can be present in air, water or soil, so they can enter the food chain and bioaccumulate.
These contaminants pose health risks if present at higher concentrations, but even low concentrations over long exposure can result in endocrine disturbance, fetal development issues and carcinogenic effects, among others. 1,2
Veterinary drugs, such as antibiotics and hormones, may persist in meat, dairy and eggs, posing risks to human health such as accelerated antibiotic resistance, allergies, nephropathy, mutagenic effects and reproductive disorders.3 While pesticide residue in fruits and vegetables can lead to neurological disorders, endocrine disruption and cancer.4
Emerging contaminants
Contaminants can also originate along the food chain supply, such as in processing and packaging. “Processed foods raise concerns about process contaminants such as acrylamide, which forms during high-temperature cooking,” said Dr. Vera Homem, an assistant professor at the Faculty of Engineering of the University of Porto.
Modern practices targeting circularity and sustainability are increasing the problem, as explained by Homem: “The use of biosolids as agricultural fertilizers and the reuse of treated wastewater for irrigation, pose new risks, (fragrances, UV-filters, human pharmaceutical residues and other anthropogenic chemicals).”
As these new policies gain traction, it is important to expand research and risk assessment frameworks to better understand the potential impact of these contaminants on food safety and public health. Furthermore, changes in eating patterns are happening faster than ever, with the incorporation of new ingredients and the growth of plant-based foods.
“One of the main concerns is the emergence of unknown risks associated with novel ingredients, such as alternative proteins derived from fungi, algae or insects. These ingredients may introduce unexpected allergens or naturally occurring toxins that have not been well studied,” Dr. Homem added.
Understanding whether contamination is occurring and to what levels can help identify if current regulations for products such as plant-based foods are still effective, or if change is needed.
Key analytical technologies in food testing
Food testing is crucial for detecting these contaminants and ensuring compliance with safety regulations. It plays a central role in protecting public health and maintaining consumer confidence. The increasing risks of contaminants, combined with growing incidents of food fraud, highlight the need for accurate, sensitive and rapid detection technologies.
Cutting-edge analytical technologies form the backbone of modern food testing strategies. These techniques have evolved to offer higher sensitivity, greater throughput, automated workflows and even on-site capabilities.
Mass spectrometry and chromatography in food testing
Chromatographic techniques such as liquid and gas chromatography (LC/GC) separate the individual components of complex food samples. These techniques are key to detecting pesticides, veterinary drugs, preservatives and process contaminants like acrylamide.Innovations such as ultra-high performance liquid chromatography (UHPLC) are enabling faster separations and better resolution, allowing, for example, the simultaneous detection of hundreds of contaminants.5 In addition, automated sample injection systems, automation in high-throughput labs and with green chromatography techniques are contributing to faster, more precise determinations with reduced environmental impact.
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Chromatographic techniques coupled with mass spectrometry (MS) systems (GC-MS/LC-MS) combine the separation capabilities of chromatography with the identification power of MS, allowing an analyst to detect trace-level contaminants such as pesticide residues, antibiotics, mycotoxins and illegal additives. They are also useful in identifying food fraud and adulterants based on molecular profiles.
However, these methods can be labor-intensive and environmentally detrimental, requiring complex, multi-step sample preparation and significant organic solvent use. Researchers from the University of Messina aimed to overcome these issues with a “greener” approach, using LC-GC-MS to detect polycyclic aromatic hydrocarbons (PAHs) in extra virgin olive oil (EVOO).6
They implemented a simplified sample preparation protocol that involved a single dilution step, significantly reducing solvent use to less than 8 mL per sample. By comparing the traditional selected-ion-monitoring mode with the more advanced pseudo-multiple reaction monitoring (p-MRM) mode, they demonstrated that p-MRM enhanced specificity and signal-to-noise ratios, allowing for more accurate PAH detection in the lipid-rich EVOO matrix.
This approach underscores the potential of advanced mass spectrometry techniques to improve sensitivity and reduce environmental impact in food safety testing.
Spectroscopic approaches to food testing
Spectroscopic techniques such as infra-red (IR), near infra-red (NIR) and Raman spectroscopy allow analysts to analyze how molecules interact with light, providing unique spectral fingerprints. These techniques are often used in quality control, authentication and rapid screening for adulterants and food fraud.
The use of advanced computational tools is allowing for faster and more precise spectral data interpretation, and hyperspectral imaging combines spectroscopy and imaging for detailed food analysis,7 allowing the identification of both internal and exterior food problems in fruits and vegetables, dairy, meat products, seafood, cereals and legumes.
Heavy metals such as lead and mercury can be detected by atomic absorption spectroscopy (AAS) by measuring the absorbed light by each element and inductively coupled plasma mass spectrometry (ICP-MS) offers sensitive detection of trace metals.
In a study in Nano Letters, researchers reported a high-tech imaging method utilizing surface-enhanced Raman spectroscopy (SERS) to detect pesticide residues at trace levels on fruit.8 The method involves a silver-coated membrane that can be placed directly over produce to enhance SERS sensitivity. In tests involving apples sprayed with the pesticides thiram and carbendazim, the membrane enabled the detection of both compounds even after washing, simulating typical food handling practices.
The technique was shown to effectively resolve distinct light scattering signatures for each pesticide and was capable of detecting residues on both the fruit’s surface and within the outer layer of pulp.
Biological contaminant detection
Biological contaminants are commonly detected by polymerase chain reaction (PCR), amplifying DNA for specific identification. Recent innovations include multiplex PCR kits for simultaneous detection of multiple pathogens, portable PCR devices and metagenomics for full-spectrum microbiome analysis.9,10
Enzyme-linked immunosorbent assays (ELISAs) use antibodies to detect specific pathogens, proteins or toxins. The use of multiplex ELISA kits allows the simultaneous detection of several agents, and fluorescence-based detection methods and automated readers are increasing the sensitivity and throughput of these methods.
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Bacterial contaminants can also be detected by a biogel test that utilizes bacteriophages.11 Developed by a team of researchers from McMaster University, the test was able to rapidly detect E. coli in milk and other fluids, inducing a color change within hours. The method offers a cost-effective and efficient approach to food safety testing, as well as applications in medical diagnostics, and could enable pathogen detection in resource-limited settings.
The rise of AI and predictive analytics in food safety
The growing role of advanced computational tools, such as artificial intelligence (AI) and machine learning, is revolutionizing the field.
“Artificial intelligence can analyze complex datasets from food analysis, enabling faster and more accurate identification of contaminants. For example, machine learning algorithms can be trained to recognize patterns in mass spectrometry or chromatography data, making it easier to detect anomalies in food composition,” Dr. Homem explained. This approach is currently been used in screening and identification of unknown chemical in food samples12 or refining and speeding the detection known contaminants, such as mycotoxins, in complex food matrixes.13
In a recent study, micro-confocal Raman spectroscopy was combined with optimized machine learning methods to enhance the detection and identification of foodborne pathogens.14 Researchers developed a dual-scale Convolutional Neural Network model to discriminate between seven types of pathogens across five genera, achieving a prediction accuracy of over 98.4%.
By refining clustering algorithms to identify phylogenetic relationships among pathogens, the study demonstrated that machine-learning-driven Raman spectroscopy could serve as a powerful tool for rapid microbial contamination detection in food, addressing limitations in distinguishing biochemically similar serotypes.
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Predictive modeling also enables risk assessment by analyzing historical data to identify potential contamination hotspots in the food supply chain. On the other hand, AI-driven automation has significantly improved the efficiency of analytical techniques by optimizing sample preparation, reducing false positives and speeding up data interpretation.15
As these tools continue to evolve, they will play a fundamental role in strengthening food safety monitoring and early warning systems for contamination risks.
Key challenges in food testing
Despite these advances, continuous improvements remain necessary.
“Key challenges include the simultaneous detection of multiple contaminants, while effectively suppressing food matrix effects to enhance the speed, sensitivity, accuracy and reproducibility in analytical determinations,” stated Dr. Abrunhosa.
The cost of meeting these challenges is also a challenge unto itself.
“The high costs and limited accessibility to most advanced and reliable testing methods, particularly in resource-limited countries, present significant barriers to widespread monitoring and regulatory enforcement,” Abrunhosa added.
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Another challenge lies in the widening gap between the fast-paced innovation in food production and the capacity of current safety regulations and testing methods to keep up. The introduction of novel ingredients, sustainable practices and climate-driven changes are introducing new risks that traditional safety frameworks are not fully equipped to detect.
To address these emerging threats, a more dynamic and integrated approach is needed. One that combines real-time monitoring, predictive analytics and adaptive regulatory strategies. Strengthening collaboration between scientists, regulators and technology developers will be critical to ensuring food safety systems evolve in tandem with innovation, maintaining consumer trust and public health.
The future of food testing
Food testing technologies have advanced significantly, from lab-based methods to real-time, portable systems, enhancing our ability to detect even trace amounts of contaminants. As the complexity of food systems grows, so does the need for accurate and efficient detection methods.
Looking ahead, the future of food analysis will likely be shaped by emerging technologies and digital innovation. According to Homem, “The future of food analysis is expected to be driven by advances in rapid detection technologies, portable analytical instruments and digital food safety monitoring. Miniaturized mass spectrometry and biosensors are being developed to provide real-time testing in the field, reducing reliance on centralized laboratories and enabling faster decision-making in food safety incidents.”
“AI and big data analytics will continue to revolutionize food safety, providing predictive insights and automating complex analysis. Another promising development is the integration of multiomics approaches, such as metabolomics and proteomics, which provide deeper insights into food composition and contamination mechanisms. As these innovations progress, they will play a critical role in ensuring food safety, improving regulatory compliance and maintaining consumer confidence,” Homem added.
