INTRODUCTION:

Food systems encompass all activities and processes involved in producing, processing, distributing, consuming, and managing food waste. These systems operate at local to global scales and engage a diverse range of stakeholders ¹. Their efficiency directly affects food security, nutrition, public health, and economic stability. However, contemporary food systems face mounting threats, including climate change, extreme weather events, supply chain disruptions, and labor shortages—all of which jeopardize global food security^2,3.

As illustrated in Figure, viruses are present across all stages of the food system- production, processing, distribution, consumption, and waste management. On the consumer side, viruses are estimated to cause 30% of all foodborne illnesses⁴.

Effectively addressing viral threats across the food system requires rapid, reliable detection tools. Traditional detection methods—such as viral culture, PCR, and immunoassays- are often slow, expensive, and confined to laboratory settings, making them impractical for on-site monitoring at farms, food processing facilities, or ports of entry⁵.

Biosensors have emerged as a promising alternative. Utilizing optical, electrochemical, and piezoelectric detection mechanisms, biosensors can identify key foodborne viruses (e.g., norovirus, hepatitis A virus, and rotavirus) as well as viruses that affect crops and livestock. Compared to conventional approaches, biosensors offer faster results, lower costs, and greater portability—ideal features for field deployment ⁶.

Figure 01: Viruses commonly found throughout the Food Production and Distribution Process

Nanoparticle-based biosensors, or nanobiosensors, present a compelling solution. The integration of nanoparticles into biosensor platforms enhances detection capabilities due to their unique physicochemical properties, leading to improved sensitivity, specificity, and lower detection limits. Although recent reviews have covered nanobiosensors for detecting consumer-level foodborne viruses, a comprehensive assessment that links viral threats to technological solutions across the entire food system is still lacking⁷.

We begin by detailing the major viral challenges at each stage, followed by an overview of the key nanoparticles and viral analytes that inform biosensor design⁸. The core of the review evaluates the latest nanobiosensor innovations for detecting four major foodborne viruses, three critical livestock viruses, and several high-impact crop viruses, highlighting both performance metrics and current limitations⁹.

Viruses in Food Systems: Threats and Mitigation:

Viruses pose significant risks to public health, global trade, and economic stability by infiltrating every stage of the food system. As shown in Fig, viruses can affect a wide range of food commodities—from livestock and crops to fresh produce and processed foods—at multiple vulnerable points, including production, processing, distribution, consumption, and waste management. This widespread vulnerability underscores the need for comprehensive detection and control strategies¹⁰^,11.

In crop systems, plant viruses such as tobacco mosaic virus (TMV) and wheat streak mosaic virus (WSMV) significantly impact food security. TMV affects over 125 plant species, causing yield losses up to 75% in places like Vietnam. WSMV, especially when combined with Triticum mosaic virus (TriMV), can reduce yields by as much as 96%. Highly resilient viruses like cucumber mosaic virus (CMV)—which infects over 800 plant species—complicate containment efforts and exacerbate food shortages¹².

Foodborne viruses like norovirus (NoV) and hepatitis A virus (HAV) directly threaten consumers by contaminating fresh produce, shellfish, and ready-to-eat foods. These viruses are resistant to freezing, acidity, and standard cooking processes. NoV, for example, is infectious at doses as low as 20 particles and is notoriously difficult to detect due to its variable concentration in food samples¹³.

Effective mitigation requires real-time detection tools that can be deployed on farms, in processing plants, and at borders. While traditional nucleic acid-based diagnostics are sensitive, they are lab-based and not suitable for immediate, on-site decision-making. This gap underscores the need for innovative technologies—such as biosensors—which offer portable, rapid, and cost-effective virus detection. Implementing these tools at critical control points is essential for building a resilient and safe global food system¹⁵.

Utilization of Nanoparticles in Biosensor Technologies:

The selection of nanoparticles (NPs) for use in biosensors is guided by the specific virus to be detected, the nature of the food matrix, and the requirements of the chosen detection method. NPs play a crucial role in biosensors by facilitating signal transduction, enhancing signal strength, or isolating the target.

Figure 02: Viruses in Food Systems: Threats and Mitigation

Their functionality—shaped by their size, shape, and composition—enables tailored solutions to address the unique challenges of virus detection in various food systems. Nanoparticles used in this context generally fall into three categories: inorganic, organic, and hybrid¹⁶.

Inorganic nanoparticles are the most commonly employed in biosensors, with the choice of material dependent on the properties of the food sample¹⁷.

For electrochemical detection, which is less affected by sample color and turbidity, the focus shifts to conductivity and catalytic activity. Carbon-based materials such as graphene and carbon nanotubes deliver exceptional conductivity, enabling highly sensitive signal transduction. Metal-based NPs like platinum (PtNPs) and metal oxides are also commonly used due to their catalytic properties, which amplify signals to detect even low concentrations of viral particles¹⁸.

In addition to signal enhancement, magnetic nanoparticles (MNPs) are indispensable for sample preparation—an essential step in most food-based virus detection workflows. Their superparamagnetic nature allows for effective immunomagnetic separation, facilitating the isolation of viruses from complex and viscous samples like milk, meat slurry, or wastewater¹⁹.

Organic and hybrid nanoparticles add another layer of functionality, especially in complex food environments. Organic nanoparticles, particularly polymer-based ones, offer advantages such as high biocompatibility and structural versatility²⁰.

One of the most significant challenges in developing food-based biosensors is overcoming the matrix effect. Food samples are inherently complex, containing a mixture of proteins, fats, carbohydrates, and salts that can interfere with sensor accuracy. For instance, the high ionic strength found in shellfish homogenates can disrupt the electrostatic stability of unmodified gold nanoparticles (AuNPs), leading to their aggregation and triggering false signals. Likewise, proteins and fats in the sample can non-specifically bind to the sensor surface, blocking access to the target virus^21,57,56.

Figure 03: Utilization of Nanoparticles in Biosensor Technologies

Nanobiosensor Technologies for Monitoring Foodborne Viruses:

Integrating biosensors into food monitoring systems significantly enhances food safety by enabling real-time virus detection and supporting prompt intervention strategies. This section highlights the use of biosensors—particularly nanosensors—for virus detection in two of the five key segments of the food system: production and consumption. These segments are prioritized because they represent the main entry points for viruses into the food supply chain^22,54.

In the production segment, the discussion centers on biosensors used to detect viral threats to livestock, including foot-and-mouth disease virus (FMDV), avian influenza virus (AIV), and African swine fever virus (ASFV), as well as crop-affecting viruses such as maize chlorotic mottle virus (MCMV). Early detection through these biosensors supports better disease management, enhances productivity, and safeguards agricultural output^23,55.

Nanobiosensors for Foodborne Viruses:

The global rise in foodborne illnesses remains a significant public health concern, underscoring the urgent need for advanced monitoring systems to ensure food safety. In recent years, biosensor technology—particularly nanoparticle-based biosensors—has shown great potential in meeting this challenge. These nanobiosensors offer rapid and highly sensitive detection of viral contaminants in food, playing a crucial role in preventing outbreaks²⁴.

This section highlights recent developments in nanoparticle-based biosensors designed to detect the four most common foodborne viruses: norovirus, hepatitis A virus, hepatitis E virus, and rotavirus. These viruses pose major threats to global health due to their high disease burden, low infectious dose, and ability to persist through food processing and handling^25,53.

Norovirus Nanobiosensors:

Norovirus (NoV) is a non-enveloped, positive-sense single-stranded RNA virus responsible for over 50% of microbial foodborne illnesses globally. NoV spreads via contaminated food, water, surfaces, or person-to-person contact^26,52. With no vaccine or specific treatment available, early and sensitive detection is essential for outbreak prevention.

Figure 04 illustrates examples of nanobiosensors developed for the detection of these key foodborne viruses.

Impedimetric Nanosensors:

· WS₂@AuNPs Sensor: Developed by Baek et al., this sensor uses WS₂ nanoflowers decorated with gold nanoparticles functionalized with NoV-specific peptides. NoV binding alters the impedance signal. It showed a low limit of detection (LOD) in buffer (2.37 copies/mL) but reduced sensitivity in oyster samples (LOD of 6.21 copies/mL), highlighting matrix effects.

· Au–PAni Nanocomposite Sensor: Nasrin et al. improved stability and sensitivity using polyaniline-gold composites and streptavidin–biotin chemistry for antibody immobilization. This approach achieved an impressive LOD of 60 ag/mL but involved a complex fabrication process, potentially limiting scalability²⁷.

Fluorescence Nanosensors:

· Alzahrani et al. combined gold nanoparticles and carbon quantum dots to create a fluorescence-based sensor with an LOD of 80.3 pg/mL—ten times more sensitive than commercial kits. However, it was only validated in human serum, not food matrices.

Colorimetric & Electrochemical Sensors:

· Alhadrami et al. developed a rapid, qualitative detection method using lactoferrin-coated cotton swabs and antibody-conjugated AuNPs, enabling visual detection on food surfaces like lettuce and cucumber. Though low-cost and fast, it lacks quantitative precision²⁸.

Magnetic-Fluorescent Hybrid Sensors:

· Another approach utilizes Fe₃O₄ magnetic nanoparticles to capture NoV, followed by detection through fluorophore-releasing liposomes. This strategy achieved an LOD of 136 copies/mL, nearing RT-qPCR sensitivity, with results in just 20 minutes. However, the method's complexity may limit its use to laboratory settings.

Rotavirus Nanobiosensors:

Rotavirus, a double-stranded RNA virus, is a major cause of severe diarrhea in young children worldwide, especially in regions lacking proper sanitation and access to clean water. Its genome is segmented, which allows for genetic reassortment and results in high antigenic diversity. This diversity makes it essential for diagnostic tools to target conserved viral capsid regions, such as the VP6 protein, to ensure broad detection across multiple rotavirus serotypes^29,46,51.

Several nanosensors have been developed for rotavirus detection. For example, Y. Zhang et al. created a portable immunochromatographic assay (ICA) using gold-silver (Au@Ag) core–shell nanoparticles (NPs) that act as surface-enhanced Raman scattering (SERS) labels. Functionalized with anti-rotavirus monoclonal antibodies, the device achieved a limit of detection (LOD) of 8 pg/ mL—ten times more sensitive than naked-eye detection^30,45.

Another approach by Rippa and colleagues involved a plasmonic sensor array using octupolar triangular gold nanopillars. These nanopillars, functionalized with rotavirus-specific antibodies, generate strong localized surface plasmon resonance (LSPR) fields, enhancing light-matter interactions and sensor sensitivity. This sensor detected rotavirus at approximately 126 plaque-forming units per milliliter. Although it showed specificity, it was only tested against two other viruses, and its synthesis, which uses electron beam lithography, is costly and complex^31,47.

Nanobiosensors for Crop Virus Detection:

Plant health, alongside animal health, is a critical pillar in the food production system. Viral diseases in crops cause billions of dollars in yield losses annually, severely affecting global food security and agricultural economies^{32, 48}.

One notable example is Rice tungro disease, caused by a combination of rice tungro bacilliform virus (RTBV) and rice tungro spherical virus (RTSV). This disease, spread by the green leafhopper, leads to severe stunting and reduced tillering in rice plants, sometimes resulting in complete crop failure in susceptible varieties—a major concern for food security in rice-dependent regions of Asia^32,40,41-43

Challenges and Future Perspectives:

A wide range of nanoparticle-based biosensors has been developed to detect viral threats within food systems. However, transitioning these sensors from lab-based proofs-of-concept to practical, reliable tools suitable for farms and factories remains a significant challenge due to biological, technical, and logistical barriers^33,44.

Biological Challenges: Many foodborne viruses, such as norovirus, cannot be cultured in the lab, limiting sensor validation and research. Additionally, RNA viruses often mutate rapidly, which can undermine the effectiveness of sensors designed to target specific viral components^{34, 49}.

Technical and Matrix Challenges: Sensors often perform well in controlled buffer solutions but struggle in real-world food samples due to the "matrix effect." Components like fats, proteins, and enzymes can interfere with detection, reducing sensitivity and causing false results^35,50.

Manufacturing and Economic Viability: Many high-performance sensors rely on complex nanomaterials and fabrication methods that are difficult to scale. Developing scalable, cost-effective manufacturing techniques with consistent quality is a critical engineering challenge. While nanoparticle-based sensors can be more affordable than conventional methods like PCR or ELISA, the cost must be low enough for widespread use along the food supply chain.

Emerging Solutions and Future Directions: Promising approaches include sensors that can distinguish infectious from non-infectious viral particles, such as those targeting intact viral capsids.³⁶.

Artificial intelligence (AI) and machine learning (ML) offer exciting prospects for enhancing biosensor capabilities. AI can improve signal analysis, reduce noise, and adapt to complex food matrices, enabling highly sensitive and rapid virus detection comparable to laboratory methods. These AI-powered systems can also provide predictive analytics, helping to prevent outbreaks by identifying contamination trends early^37,38,39.

REFERENCES:

1. Riann Martin Sarza, Xiaodi Su, Nanoparticle-based biosensors for virus detection in food systems: from farm to fork, Nanoscale, 2025, 1- 24.

2. J. Fanzo, A. L. Bellows, M. L. Spiker, A. L. Thorne-Lyman and M. W. Bloem, Am. J. Clin. Nutr, 2021, 113, 7–16.

3. Bosch, E. Gkogka, F. S. Le Guyader, F. Loisy-Hamon, A. Lee, L. van Lieshout, B. Marthi, M. Myrmel, A. Sansom, A. C. Schultz, A. Winkler, S. Zuber and T. Phister, Int. J. Food Microbiol., 2018, 285, 110–128, DOI: 10.1016/j.ijfoodmicro.2018.06.001.

4. N. Nasheri, J. Harlow, A. Chen, N. Corneau and S. Bidawid, Food Environ. Virol., 2021, 13, 107–116.

5. G. Di Cola, A. C. Fantilli, M. B. Pisano and V. E. Ré, Int. J. Food Microbiol., 2021, 338, 108986, DOI: 10.1016/j. ijfoodmicro.2020.108986.

6. S. Mukherjee, P. Strakova, L. Richtera, V. Adam and A. Ashrafi, Advanced Biosensors for Virus Detection: Smart Diagnostics to Combat SARS-CoV-2, Elsevier, 2022, pp. 391–405.

7. C. Hennechart-Collette, O. Dehan, M. Laurentie, A. Fraisse, S. Martin-Latil and S. Perelle, Int. J. Food Microbiol., 2021, 337, ijfoodmicro.2020.108931. 108931, DOI: 10.1016/j.

8. T. Chanchaidechachai, H. Saatkamp, C. Inchaisri and H. Hogeveen, Front. Vet. Sci., 2022, 9, 904630, DOI: 10.3389/fvets.2022.904630.

9. V. G. Panferov, I. V. Safenkova, A. V. Zherdev and B. B. Dzantiev, Microchim. Acta, 2018, 185, 506, DOI: 10.1007/s00604-018-3052-7.

10. Razmi, A. Golestanipour, M. Nikkhah, A. Bagheri, M. Shamsbakhsh and S. Malekzadeh-Shafaroudi, J. Virol. Methods, 2019, 267, 1–7.

11. Z. Wang, W. Yu, R. Xie, S. Yang and A. Chen, Anal. Bioanal. Chem., 2021, 413, 4665–4672, DOI: 10.1007/ s00216-021-03408-2.

12. X. Wang, Y. Luo, K. Huang and N. Cheng, Adv. Agrochem, 2022, 3–6, DOI: 10.1016/j.aac.2022.08.002.

13. T. Yang, Z. Luo, T. Bewal, L. Li, Y. Xu, S. M. Jafari and X. Lin, Food Chem., 2022, 394, 133534, DOI: 10.1016/j. foodchem.2022.133534.

14. X. Zhu, T. Y. Kim, S. M. Kim, K. Luo and M. C. Lim, J. Agric. Food Chem., 2023, 71, 15942–15953, DOI: 10.1021/ acs.jafc.3c05166.

15. S. Chakraborty, N. Kumar, K. Dhama, A. K. Verma, R. Tiwari, A. Kumar, S. Kapoor and V. Singh, Advances in Animal and Veterinary Sciences, 2013, 2(2S), 1–18.

16. Y. Revilla, D. Pérez- Núñez and J. A. Richt, Advances in Virus Research, Academic Press Inc., 2018, vol. 100, pp. 41–74.

17. E. Spackman, in Animal Influenza Virus, ed. E. Spackman, Humana Press, 3^rd edn, 2020, p. 83.

18. P. Signoret, in Encyclopedia of Virology, ed. B.W. J. Mahy and M. H. V. Van Regenmortel, 3^rd edn, 2008.

19. G. Lu, Z. Wang, F. Xu, Y. B. Pan, M. P. Grisham and L. Xu, Microorganisms, 2021, microorganisms9091984. 9, 1984, DOI: 10.3390/

20. V. Morton, J. Jean, J. Farber and K. Mattison, Appl.Environ. Microbiol., 2009, 75, 4641–4643.

21. K. Nemes, S. Persson and M. Simonsson, Viruses, 2023, 15, 1725, DOI: 10.3390/v15081725.

22. M. Koopmans and E. Duizer, Int. J. Food Microbiol., 2004, 90,23–41.

23. E. H. Teshale and D. J. Hu, World J. Hepatol., 2011, 3, 285–291.

24. B. S. Coulson, Curr. Opin. Virol., 2015, 15, 90–96, DOI: 10.1016/j.coviro.2015.08.012.

25. R. A. Farahat, S. H. Khan, A. A. Rabaan and J. A. Al-Tawfiq, New Microbes New Infect., 2023, 53, 101122, DOI: 10.1016/ j.nmni.2023.101122.

26. Blagodatski, K. Trutneva, O. Glazova, O. Mityaeva, L. Shevkova, E. Kegeles, N. Onyanov, K. Fede, A. Maznina, E. Khavina, S. J. Yeo, H. Park and P. Volchkov, Pathogens, 2021, 10, 630, DOI: 10.3390/pathogens10050630.

27. V. Ayerdurai, M. Cieplak and W. Kutner, TrAC, Trends Anal. Chem., 2023, 158, 116830, DOI: 10.1016/j.trac.2022.116830.

28. P. Ray, R. Chakraborty, O. Banik, E. Banoth and P. Kumar, ACS Omega, 2023, 8, 3606–3629, DOI: 10.1021/ acsomega.2c05983.

29. Z. Yilmaz-Sercinoglu, C. İlke Kuru and F. Ulucan-Karnak, Sensing Tools and Techniques for COVID-19: Developments and Challenges in Analysis and Detection of Coronavirus, Elsevier, 2022, pp. 57–82.

30. Y. Jeong, J. Kim, J. Lee, S. Seo, S. Roh, G. Lee, B. G. Choi, N. H. Bae, J. Jung, T. Kang, K. G. Lee and E. K. Lim, Mater. Horiz, 2025, 12, 451–457, DOI: 10.1039/d4mh01062k.

31. S. M. Sheta and S. M. El-Sheikh, Anal. Biochem., 2022, 648, 114680, DOI: 10.1016/j.ab.2022.114680.

32. O. Prakash, D. Verma and P. C. Singh, J. Mater. Chem. B, 2024, 12, 10198–10214, DOI: 10.1039/d4tb01556h.

Received on 08.12.2025 Revised on 19.01.2026

Accepted on 27.02.2026 Published on 25.04.2026

Available online from April 28, 2026

Research J. Science and Tech. 2026; 18(2):199-204.

DOI: 10.52711/2349-2988.2026.00028

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Creative Commons License.