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New Plasmonic Sensor Targets Aflatoxin M1 Contamination in Milk

*Important notice: This news reports on an unedited version of an accepted paper and is awaiting final editing. Therefore, the paper should not be regarded as conclusive or treated as established information.

Aflatoxin M1 (AFM1), a toxic fungal contaminant in dairy products, poses a significant food safety concern requiring rapid, sensitive detection. In a study published in the journal Scientific Reports, researchers developed a plasmonic graphene oxide-based aptasensor that uses surface plasmon resonance (SPR) combined with a graphene oxide-chitosan (GOCS) sensing layer to detect AFM1 at extremely low concentrations.

Barista pouring foamed milk into a cup of coffee
Study: Plasmonic graphene oxide aptasensor empowered for ultra-sensitive and rapid detection of aflatoxin M1 in dairy product. Image Credit: worradirek/Shutterstock.com

The sensor achieved a detection limit of 0.005 ng/mL, providing a faster alternative to conventional laboratory methods, which often require expensive equipment and lengthy sample preparation. This approach could support routine food safety monitoring and helps prevent contaminated dairy products from reaching consumers.

Understanding Fungal Contamination in Dairy

Aflatoxins are toxic compounds produced by fungi such as Aspergillus flavus and Aspergillus parasiticus. When dairy cattle consume feed contaminated with aflatoxin B1 (AFB1), it is converted into AFM1, which is excreted into milk. It is documented that AFM1 remains stable during pasteurization, posing a long-term carcinogenic risk to consumers. Agencies, including the European Union and the United States, have established strict limits for AFM1 in milk.

Synthesis and Fabrication of the GOCS Composite

The sensor was fabricated by combining graphene oxide and chitosan into a GOCS composite. Initially, 9 mg of graphene oxide was dispersed in 9 mL of double-distilled water and sonicated for one hour. Separately, 1 mL of 0.01 M acetic acid was used to dissolve 1 mg of chitosan.

The two solutions were mixed at a 9:1 volume ratio and sonicated for an additional two hours, forming chemical bonds between the graphene oxide and chitosan.

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A 50 μL aliquot of the GOCS suspension was spin-coated onto a gold-coated substrate at 4000 rpm and dried for 24 hours. The surface was functionalized by incubating it in a 5 μM thiolated AFM1 aptamer solution at 26 °C for 24 hours. The negatively charged DNA backbone bound to the positively charged amino groups of chitosan through electrostatic interactions, while hydrogen bonds stabilized the sensing layer.

The completed sensor was characterized using field emission scanning electron microscopy (FE-SEM) and X-ray diffraction (XRD) to examine its structure and crystallinity. Finite-difference time-domain (FDTD) simulations were employed to optimize the sensing layer and model the surface plasmon response at an excitation wavelength of 670 nm.

Performance Evaluation and Sensitivity Analysis

The GOCS sensing layer significantly improved the performance of the SPR sensor. Structural analysis revealed a uniform coating with a thickness of 40–60 nm, while elemental analysis confirmed the incorporation of chitosan into the graphene oxide matrix. The modified surface increased the evanescent-field penetration depth from about 200 nm for a bare gold chip to 680 nm, enhancing the sensor's ability to detect molecular binding events.

The sensor detected AFM1 at concentrations as low as 0.0025 ng/mL, achieving a limit of detection (LOD) of 0.005 ng/mL and a limit of quantification (LOQ) of 0.05 ng/mL. Across a linear working range of 0.005–50 ng/mL, the sensor produced a strong calibration curve with a correlation coefficient (R2) of 0.9697.

In contrast, the unmodified gold chip could not reliably detect AFM1 concentrations below 5 ng/mL. The sensor functioned optimally at neutral pH 7 and demonstrated high selectivity, producing a stronger response to AFM1 than to other mycotoxins, including AFB1, ochratoxin A (OTA), and zearalenone (ZEA).

Reproducibility tests over 15 days yielded a relative standard deviation of 4.27%, indicating stable long-term performance. The sensing surface could be regenerated by treating the gold chip with aqua regia for 30 minutes, followed by mechanical polishing.

Practical Applications in Dairy Safety Monitoring

To evaluate the sensor under real-world conditions, researchers tested commercial samples of milk, curd, and buttermilk purchased from local markets. Before analysis, the samples were centrifuged to remove fat, filtered, and diluted to minimize background interference.

The prepared samples were spiked with known concentrations of AFM1 and analyzed using the plasmonic aptasensor. Recovery rates ranged from 95.2% to 106.32%, closely matching results obtained with conventional high-performance liquid chromatography (HPLC).

The sensor can accurately detect AFM1 in dairy products while meeting the regulatory limits established by the European Union and the United States. Its sensitivity, rapid response, and testing procedure make it promising for routine food safety monitoring in dairy processing.

Future Directions for Advanced Biosensing

This study demonstrates the potential of a GOCS plasmonic aptasensor as a portable and sensitive tool for food safety testing. The combination of graphene oxide, chitosan, and SPR enabled the sensor to achieve high sensitivity. The successful detection of AFM1 in commercial dairy samples suggests that this technology could support routine on-site food safety testing.

Future work could expand the platform to detect multiple food contaminants using different aptamers. The reusable gold-sensing chip, which can be regenerated by aqua regia treatment and refunctionalized for repeated use, helps reduce operating costs and enhances the sensor's practicality for large-scale food monitoring.

Journal References

Mardanqom, H.N., et al. (2026). Plasmonic graphene oxide aptasensor empowered for ultra-sensitive and rapid detection of aflatoxin M1 in dairy product. Scientific Reports. https://www.nature.com/articles/s41598-026-60164-y.

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Muhammad Osama

Written by

Muhammad Osama

Muhammad Osama is a full-time data analytics consultant and freelance technical writer based in Delhi, India. He specializes in transforming complex technical concepts into accessible content. He has a Bachelor of Technology in Mechanical Engineering with specialization in AI & Robotics from Galgotias University, India, and he has extensive experience in technical content writing, data science and analytics, and artificial intelligence.

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