本文讨论了微生物的生物标志物、不同的识别方法以及纸基生物传感器的应用。此外,总结了未来的发展方向,为纸基生物传感器的进展及其在食物链安全保障中的进一步应用提供参考。
Jie Lia, Keren Chenb, Yuan Sub, Longjiao Zhub, Hongxing Zhanga, Wentao Xub,*, Xiangyang Lia,*
a Beijing Laboratory of Food Quality and Safety, Beijing Key Laboratory of Agricultural Product Detection and Control of Spoilage Organisms and Pesticide Residue, Faculty of Food Science and Engineering, Beijing University of Agriculture, Beijing 102206, China
b Department of Nutrition and Health, Institute of Nutrition and Health, China Agricultural University, Beijing 100191, China
*Corresponding authors.
Abstract
Microbially contaminated food can cause serious health hazards and economic losses, therefore sensitive, rapid, and highly specific visual detection is called for. Traditional detection of microorganisms is complex and time-consuming, which cannot meet current testing demands. The emergence of paper-based biosensors provided an effective method for efficient and visual detection of microorganisms, due to its high speed, all-in-one device, low cost, and convenience. This review focused on 5 biomarkers, namely nucleic acids, proteins, lipopolysaccharides, metabolites, and the whole microorganism of microorganisms. Besides, the recognition methods were summed up in 5 forms, including immunological recognition, aptamer recognition, nucleic acid amplification-mediated recognition, DNAzyme recognition and clustered regularly interspaced short palindromic repeats mediated recognition. In addition, we summarized the applications of paper-based biosensors in the detection of microorganisms thoroughly. Through the exploration of different biomarkers, identification methods, and applications, we hope to provide a reference for the development of paper-based biosensors and their application in safeguarding the food chain.
LI J, CHEN K R, SU Y, et al. Paper-based biosensors based on multiple recognition modes for visual detection of microbially contaminated food[J]. Journal of Future Foods, 2024, 4(1): 61-70. DOI:10.1016/j.jfutfo.2023.05.007.
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