Polystyrene microsphere-mediated optical sensing strategy for ultrasensitive determination of aflatoxin M1 in milk

A metal-organic framework signaling probe-mediated immunosensor for the economical and rapid determination of enrofloxacin in milk

Ensuring the safety of animal-derived foods requires the reliable and swift identification of enrofloxacin residues to monitor the presence of antibiotics. In this regard, we synthesized, tuned, and investigated the optical properties of a bimetallic metal-organic framework (Ce/Zr-UiO 66). The investigation was facilitated by employing a polydopamine-coated pipette tip with high adsorption efficiency, serving as an immunoreactive carrier. Subsequently, an immunofunctionalized variant of Ce/Zr-UiO 66, referred to as Ce/Zr-UiO 66@ Bovine serum albumin-enrofloxacin, was developed as an optical probe for the rapid and sensitive identification of enrofloxacin across a variety of samples. The method can accurately detect enrofloxacin at concentrations as low as 0.12 ng/mL, with a determination time of under 15 min; furthermore, it demonstrates exceptional efficacy when applied to food, environmental, and clinical samples. The implementation of this methodology offers a valuable means for cost-effective, rapid, and on-site enrofloxacin determination.

Machine-learning-driven optical immunosensor based on microspheres-encoded signal transduction for the rapid and multiplexed detection of antibiotics in milk

Antibiotic residues are the most common contaminants in milk and other related dairy products. Simultaneous, convenient, and stable detection of antibiotic residues in foods is vital to secure public health. Herein, we proposed an optical immunosensor with easily-functionalized polystyrene nanoparticles differing in size and quantity, and bearing multiplex signal probes for the simultaneous detection of multiple antibiotics through a simple one-step signal conversion reaction. After the integration of the machine-learning-based transcoding analysis, this sensor is suitable for multiplexed detection of antibiotics in a broad linear range from pg/mL to ng/mL within 30 min, with an overall accuracy of >99 %. Compared to the conventional standard chemiluminescence immunoassays, this immunosensor is suitable for the accurate quantification of multiple antibiotics in milk, with improved accuracy, reduced costs, and simplified procedure. This ensures its applications in food safety monitoring when simultaneous detection of multiple hazardous substances in food matrices is needed.

Magnetic nanoparticle-based immunosensors and aptasensors for mycotoxin detection in foodstuffs: An update

Mycotoxin contamination of food crops is a global challenge due to their unpredictable occurrence and severe adverse health effects on humans. Therefore, it is of great importance to develop effective tools to prevent the accumulation of mycotoxins through the food chain. The use of magnetic nanoparticle (MNP)-assisted biosensors for detecting mycotoxin in complex foodstuffs has garnered great interest due to the significantly enhanced sensitivity and accuracy. Within such a context, this review includes the fundamentals and recent advances (2020-2023) in the area of mycotoxin monitoring in food matrices using MNP-based aptasensors and immunosensors. In this review, we start by providing a comprehensive introduction to the design of immunosensors (natural antibody or nanobody, random or site-oriented immobilization) and aptasensors (techniques for aptamer selection, characterization, and truncation). Meanwhile, special attention is paid to the multifunctionalities of MNPs (recoverable adsorbent, versatile carrier, and signal indicator) in preparing mycotoxin-specific biosensors. Further, the contribution of MNPs to the multiplexing determination of various mycotoxins is summarized. Finally, challenges and future perspectives for the practical applications of MNP-assisted biosensors are also discussed. The progress and updates of MNP-based biosensors shown in this review are expected to offer readers valuable insights about the design of MNP-based tools for the effective detection of mycotoxins in practical applications.