Portable SERS test detects harmful bacteria in beef within minutes

By Dr. Liji Thomas, MDReviewed by Bethan DaviesOct 10 2025

Using silver nanoparticles and portable Raman spectroscopy, researchers have created a rapid, cost-effective test that identifies dangerous bacteria in beef within minutes, a breakthrough that could strengthen food safety standards from slaughterhouse to supermarket.

Bacterial contamination of meat is a major threat to meat consumers. The currently available tests for these bacteria are often cumbersome, and the results are delayed. A recent paper published in Foods presents the use of surface-enhanced Raman scattering (SERS) combined with portable Raman spectroscopy for the rapid testing of beef for bacterial growth or contamination.

Introduction

With rising food production and sales worldwide, foodborne disease has become a threat to public health. Beef, in particular, is nutrient-rich, encouraging bacterial growth.

When beef is cooked thoroughly, it usually kills off most harmful microbes. But with ready-to-eat foods, the risk is much higher since there's no cooking step to eliminate pathogens, making quick and accurate detection especially important. Although there are several testing methods out there, many take too long to deliver results or aren't sensitive enough to catch bacteria at low levels.

Raman scattering, an inelastic scattering of light by molecules, creates a unique pattern of light, often called a spectroscopic fingerprint, that can be used to identify specific molecules. Raman spectroscopy has been widely used to find the concentration and structure of molecules found in living tissues and cells, as well as their interactions. When combined with other processing techniques, it can also help differentiate between various bacterial genera. 

However, its low sensitivity and resolution have been barriers to its widespread adoption for bacterial detection and identification. SERS is a technique that aims to improve these aspects.

SERS can amplify Raman signals by as much as seven orders of magnitude. This is done by first adsorbing the particles on rough metal surfaces or nanostructures, usually gold, silver, or copper.

While SERS-Raman spectroscopy has shown promise in identifying and classifying bacteria, it’s often tested using pure bacterial cultures under controlled conditions. In contrast, food testing, especially with products like beef, presents a much more complex environment that poses additional challenges. 

About the study

The current study explored how SERS, combined with chemometric analysis, can be used to detect four common foodborne pathogens in beef. Stable and easily stored silver nanoparticles (AgNPs) were prepared and used as the nanostructures for bacterial attachment.

By bringing the bacteria into close contact with the AgNPs, the setup created ideal conditions for SERS. Most of the bacteria adhered to the nanoparticles, which formed clusters along the cell surfaces, effectively coating them and acting as enhancement substrates for Raman signal amplification.

The study focused on detecting these four major beef-associated pathogens:

• Escherichia coli (E. coli) O157:H7
• Salmonella typhimurium (S. typhimurium)
• Staphylococcus aureus (S. aureus)
• Listeria monocytogenes (L. monocytogenes)

The researchers identified the optimal conditions for the detection of AgNPs. They also set the limits of detection (LOD) for all four pathogens in beef samples and in culture.

Study findings

Each of the four pathogens produced distinct spectral patterns, with unique Raman shifts serving as identifying markers. For example, E. coli O157:H7 showed higher intensity at 1350 cm^-1, while S. typhimurium produced a dominant signal at 1520 cm^-1. These spectral differences reflect variations in biochemical structures, such as the C–N stretching vibrations of proteins in E. coli, versus biofilm-related signatures in S. typhimurium.  

Interestingly, some peaks previously linked to S. typhimurium biofilm formation (at 1330, 1030, and 875 cm^-1) were not observed, highlighting variability in spectral features across different studies or experimental conditions. 

A key advantage of the method is that it requires no pre-enrichment or separation steps, making it faster and more cost-effective than many traditional approaches.

For S. aureus, signal intensity increased at 1330 cm⁻¹ in a dose-dependent fashion, likely due to the “phenylalanine ring breathing mode”, a vibration commonly detected by Raman spectroscopy. The LOD for this species was lower than previously reported, perhaps due to methodological variations.

The current method is more economical and efficient than earlier techniques; however, it does not require bacterial capture.

Finally, for L. monocytogenes, the signal intensity was higher at 1325 cm⁻¹ in a dose-dependent manner, probably due to the C-H deformation vibration of lipids or proteins in the bacterial cells. Again, this echoes earlier literature but with a more accurate method.

Pathogen recovery rates from beef samples ranged from ~91 % to 110 %, with an average of 99.47 %, demonstrating that the SERS method is effective even in complex food matrices. The technique detected as few as 4–23 CFU/mL of bacteria in beef. 

However, the LOD for E. coli O157:H7 and S. typhimurium was higher than with some gold or gold-silver nanoparticle-based methods. That said, the high Raman scattering efficiency of AgNPs still produced strong results. The presence of fats, proteins, and pigments in beef may interfere with Raman signal intensity, suggesting a need for further refinement to reduce matrix effects. 

It’s worth noting that while AgNP-based SERS can detect and differentiate bacteria, it doesn’t provide information about bacterial activity or virulence, both of which are critical for assessing food safety.

Using linear discriminant analysis (LDA) on the spectral data, the researchers achieved an overall detection accuracy of 92–97%. All four pathogens were correctly identified with 100 % accuracy, except for S. typhimurium, which had a 95 % correct detection rate.

This demonstrates the method’s ability to distinguish between different pathogens and detect mixed bacterial populations, making it more suitable for real-world testing than approaches based solely on pure cultures.

Practical implications

This study presents a rapid, portable method for detecting multiple foodborne pathogens in beef. Future research should expand detection to other bacterial species and strengthen model accuracy in mixed-species samples. 

For farmers, this method offers a way to rapidly detect pathogen contamination of their meat and control its spread to maintain and improve the health of their livestock and the efficiency of their farm. Moreover, such measures promote food safety and build consumer trust.

Meat processing plants are subject to strict food safety and regulatory standards. Therefore, this test could boost production efficiency and obviate testing-related delays while ensuring food safety and maintaining the reputation and competitiveness of the plant.

Similar impacts are expected on beef sales points, including abattoirs and supermarkets, and on families and catering businesses.

Conclusion

This research demonstrates the potential of SERS combined with portable Raman spectroscopy as a powerful tool for the rapid detection of pathogenic bacteria in meat products.

Future studies should aim to optimize detection conditions further, account for the impact of complex food matrices, and prioritize the use of real-world samples. There’s also a need to develop more portable and user-friendly systems. In addition, examining bacterial characteristics, such as viability and virulence, will be important, likely through integration with complementary detection methods.

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