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  • 标题:Rapid Antibody Selection Using Surface Plasmon Resonance for High-Speed and Sensitive Hazelnut Lateral Flow Prototypes
  • 本地全文:下载
  • 作者:Georgina M.S. Ross ; Maria G.E.G. Bremer ; Jan H. Wichers
  • 期刊名称:Biosensors
  • 电子版ISSN:2079-6374
  • 出版年度:2018
  • 卷号:8
  • 期号:4
  • 页码:130-146
  • DOI:10.3390/bios8040130
  • 出版社:MDPI Publishing
  • 摘要:Lateral Flow Immunoassays (LFIAs) allow for rapid, low-cost, screening of many biomolecules such as food allergens. Despite being classified as rapid tests, many LFIAs take 10–20 min to complete. For a really high-speed LFIA, it is necessary to assess antibody association kinetics. By using a label-free optical technique such as Surface Plasmon Resonance (SPR), it is possible to screen crude monoclonal antibody (mAb) preparations for their association rates against a target. Herein, we describe an SPR-based method for screening and selecting crude anti-hazelnut antibodies based on their relative association rates, cross reactivity and sandwich pairing capabilities, for subsequent application in a rapid ligand binding assay. Thanks to the SPR selection process, only the fast mAb (F-50-6B12) and the slow (S-50-5H9) mAb needed purification for labelling with carbon nanoparticles to exploit high-speed LFIA prototypes. The kinetics observed in SPR were reflected in LFIA, with the test line appearing within 30 s, almost two times faster when F-50-6B12 was used, compared with S-50-5H9. Additionally, the LFIAs have demonstrated their future applicability to real life samples by detecting hazelnut in the sub-ppm range in a cookie matrix. Finally, these LFIAs not only provide a qualitative result when read visually, but also generate semi-quantitative data when exploiting freely downloadable smartphone apps.
  • 关键词:surface plasmon resonance; high-speed lateral flow immunoassay; food allergen; carbon nanoparticles; antibody selection; smartphone detection surface plasmon resonance ; high-speed lateral flow immunoassay ; food allergen ; carbon nanoparticles ; antibody selection ; smartphone detection
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