4.8 Article

Direct detection of orchid viruses using nanorod-based fiber optic particle plasmon resonance immunosensor

期刊

BIOSENSORS & BIOELECTRONICS
卷 51, 期 -, 页码 371-378

出版社

ELSEVIER ADVANCED TECHNOLOGY
DOI: 10.1016/j.bios.2013.08.009

关键词

Fiber optic sensor; Particle plasmon resonance; Gold nanorod; Orchid virus detection

资金

  1. National Science Council of Taiwan [NSC95-3114-P-194-001-MY3, NSC100-2120-M-194-005-CC1]

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A fiber optic particle plasmon resonance (FOPPR) immunosensor is developed for label-free detection of orchid viruses that use gold nanorods (AuNRs) as the sensing material. The AuNRs are employed to create a near-infrared sensing window to solve the color interference problem of sample matrix for direct sensing of target analyte. This work cannot be achieved using gold nanospheres (AuNSs) because the signal of sample color absorption largely overlaps the signal of molecular recognition events in the visible spectrum, making the signal interpretation much more difficult. The AuNRs are immobilized on the unclad fiber core surface, and functionalized by antibodies which can specifically recognize the corresponding Cymbidium mosaic virus (CymMV) or Odontoglossum ringspot virus (ORSV) for rapid viral infection diagnosis. The refractive index resolution of the AuNR-FOPPR sensor is estimated to be 8 x 10(-6) RIU. The limits of detection (LODs) for CymMV and ORSV in leaf saps are 48 and 42 pg/mL, respectively, which are better than the LODs of 1200 pg/mL for both viruses obtained by enzyme-linked immunosorbent assay (ELISA). Exploiting the AuNR-FOPPR sensing strategy not only solves the color interference problem encountered by using AuNSs, but provides faster analysis, better reproducibility, and lower detection limit than ELISA. The sensor can distinguish between healthy and infected orchids in 10 min, and can further provide the quantitative analysis of infection level. It is potentially applicable to the quality control of orchid cultivation industry, but not limited to this, especially for creating special spectral sensing window for particular samples. (C) 2013 Elsevier B.V. All rights reserved.

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