Understanding food safety hazard risks is essential to avoid potential negative heath impacts in the food supply chain in a post-COVID-19 pandemic era. Development of strategies for virus direction in foods plays an important role in food safety and verification. Early warning, tracing, and detection should be implemented as an integrated system in order to mitigate thecoronavirus disease 2019 (COVID-19) outbreak, in which the detection of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is critical as it not only concerns screening of populations but also monitoring of possible contaminated sources such as the food supply chain. In this review, we point out the consequences in different aspects of our daily life in the post-COVID-19 pandemic from the perspective of the food supply chain and the food industry. We summarize the possible transmission routes of COVID-19 in the food supply chain before exploring the development of corresponding detection tools of SARS-CoV-2. Accordingly, we compare different detection methods for the virus in foods, including different pretreatments of food matrices in the virus detection. Finally, the future perspectives are proposed.
We report a low-cost and convenient microchannel resistance (MCR) biosensing platform that uses current signal to report biorecognition. The biorecognition behavior between targets and biometric molecules (antigens, antibodies, or oligonucleotides) immobilized on magnetic beads and polystyrene (PS) microspheres induces a quantitative change in the unreacted PS microspheres. After magnetic separation, the unreacted PS microsphere solution is passed through the microchannel, leading to an obvious blocking effect, resulting in an increase in resistance, which can in turn be measured by monitoring the electric current. Thus, the biorecognition is directly converted into a detectable current signal without any bulky instruments or additional chemical reactions. The MCR biosensing platform is cost-effective and user-friendly with high accuracy. It can be an appropriate analysis technique for point-of-care testing in resource-poor settings.
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