Researchers have developed a new nano-biosensor using zinc oxide nanoparticles (ZnO-NPs) and curcumin that significantly improves the sensitivity and efficiency of detecting aflatoxin B1 (AFB1), a dangerous foodborne toxin.
Study: Bio-nanoparticles sensor couple with smartphone digital image colorimetry and dispersive liquid–liquid microextraction for aflatoxin B1 detection. Image Credit: Mike_O/Shutterstock.com
Published in Scientific Reports, the study outlines how this sensor, combined with digital image colorimetry and dispersive liquid–liquid microextraction (DLLME), offers a more streamlined and accurate method for identifying AFB1 in food samples.
Given the serious health risks associated with AFB1 exposure, improving detection methods is critical for food safety. Traditional approaches have often fallen short in terms of sensitivity and speed, prompting the need for more effective alternatives. This research presents a promising solution by merging nanotechnology with simplified analytical techniques.
Background
AFB1 is a potent mycotoxin produced by certain molds and poses serious health risks, including liver damage and increased cancer risk. Reliable detection in food products is critical—but existing methods often struggle to deliver the needed sensitivity, speed, or practicality.
The researchers begin by reviewing past efforts to improve AFB1 detection, particularly those focused on optimizing sample volume, solvent systems, and extraction techniques. While liquid-phase microextraction has been widely explored, many earlier methods fall short of meeting current standards for specificity and reliability.
The study also highlights how recent advances in nanomaterial-based sensors are helping to close this gap. In particular, integrating these materials with digital image analysis holds promise for boosting detection performance while simplifying workflows—especially in settings where rapid, on-site testing is valuable.
Study Design and Methods
The study followed a structured methodology aimed at optimizing both the extraction process and the biosensor system. It began with refining the DLLME protocol, testing various solvents to form a stable cloudy phase—crucial for effective extraction.
At the same time, the ZnO-NPs and curcumin components of the biosensor were mixed in different ratios (1:1, 1:2, and 2:1) to determine the most effective configuration. The researchers then used UV–Vis spectrometry to measure the absorbance of each solution and identify the optimal detection wavelength.
Further optimization focused on variables such as nanoparticle and curcumin concentrations, pH, and reaction time. During this phase, AFB1 standards were introduced at varying concentrations to evaluate the sensor’s sensitivity. Once optimal conditions were established, the researchers used High-Performance Liquid Chromatography (HPLC) with a fluorescence detector to confirm and quantify AFB1 in the samples.
To validate the method, they conducted statistical analyses, including ANOVA and calibration curve plotting, to assess precision and accuracy. The performance of the new biosensor was also compared with that of traditional detection methods.
Results and Discussion
The results showed that the ZnO-NPs/curcumin sensor provided clear improvements in both sensitivity and detection limits compared to conventional techniques. The optimal sample volume was determined to be 20 mL, aligning with previous findings that suggested larger volumes could dilute the analyte and reduce extraction efficiency.
The biosensor achieved strong accuracy and reproducibility. Percent recovery rates were high, and precision was consistent across both intra-day and inter-day trials—supporting its potential for routine food testing applications.
In the discussion, the authors also highlighted the broader significance of combining digital image colorimetry with microextraction techniques. This integrated approach presented a simplified yet highly effective alternative to more complex analytical workflows. While the study focused on AFB1, the researchers emphasized that the same principles could be adapted to detect other harmful substances in food.
They also acknowledged current limitations, such as the need for further validation across different food matrices, and suggested future directions that included field deployment and adaptation for other contaminants.
Conclusion
The study demonstrated the successful development of a ZnO-NPs/curcumin nano-biosensor for detecting aflatoxin B1 with enhanced sensitivity and efficiency. By integrating advanced sensor materials with streamlined extraction and detection techniques, the researchers introduced a method with real-world potential for improving food safety monitoring.
The findings laid a solid foundation for future applications—not just in AFB1 detection but also in broader efforts to monitor other foodborne contaminants using smart, adaptable technologies.
Journal Reference
Alikord M., Shariatifar N., et al. (2025). Bio-nanoparticles sensor couple with smartphone digital image colorimetry and dispersive liquid–liquid microextraction for aflatoxin B1 detection. Scientific Reports 15, 9485. DOI: 10.1038/s41598-025-92944-3, https://www.nature.com/articles/s41598-025-92944-3