4.6 Article

Construction of Raman spectroscopic fingerprints for the detection of Fusarium wilt of banana in Taiwan

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PLOS ONE
卷 15, 期 3, 页码 -

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PUBLIC LIBRARY SCIENCE
DOI: 10.1371/journal.pone.0230330

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资金

  1. Bureau of Animal and Plant Health Inspection and Quarantine, Council of Agriculture, Executive Yuan, Taiwan, R.O.C. [104AS10.10.1-BQ-B1(8)]
  2. Ministry of Science and Technology, Taiwan, R.O.C. [103-2633-B020-002, 105-2311-B-020-001, 1062311-B-020-001, 107-2311-B-020-001]
  3. National Pingtung University of Science and Technology, Taiwan, R.O.C

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Banana (Musa sp.) is cultivated worldwide and is one of the most popular fruits. The soilborne fungal disease Fusarium wilt of banana (FWB), commonly known as Panama disease, is caused by Fusarium oxysporum f. sp. cubense (Foc) and is a highly lethal vascular fungal disease in banana plants. Raman spectroscopy, an emerging laser-based technology based on Raman scattering, has been used for the qualitative characterization of biological tissues such as foodborne pathogens, cancer cells, and melamine. In this study, we describe a Raman spectroscopic technique that could potentially be used as a method for diagnosing FWB. To that end, the Raman fingerprints of Foc (including mycelia and conidia) and Foc-infected banana pseudostems with varying levels of symptoms were determined. Our results showed that eight, eleven, and eleven characteristic surface-enhanced Raman spectroscopy peaks were observed in the mycelia, microconidia, and macroconidia of Foc, respectively. In addition, we constructed the Raman spectroscopic fingerprints of banana pseudostem samples with varying levels of symptoms in order to be able to differentiate Foc-infected bananas from healthy bananas. The rate at which FWB was detected in asymptomatic Foc-infected samples by using the spectral method was 76.2%, which was comparable to the rates previously reported for other FWB detection methods based on real-time PCR assays, suggesting that the spectral method described herein could potentially serve as an alternative tool for detecting FWB in fields. As such, we hope that the developed spectral method will open up new possibilities for the on-site diagnosis of FWB.

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