Estimation of the accuracy of two diagnostic methods for the detection of Plum pox virus in nursery blocks by latent class models

Authors

  • E. Vidal,

    1. Laboratorio de Virología e Inmunología, Centro de Protección Vegetal y Biotecnología, Instituto Valenciano de Investigaciones Agrarias (IVIA), Ctra. Moncada-Náquera km 5, 46113 Moncada, Valencia
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  • A. Moreno,

    1. Departamento de Protección Vegetal, Instituto de Ciencias Agrarias, ICA-CSIC, c/ Serrano 115 dpdo, 28006 Madrid, Spain
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  • E. Bertolini,

    1. Laboratorio de Virología e Inmunología, Centro de Protección Vegetal y Biotecnología, Instituto Valenciano de Investigaciones Agrarias (IVIA), Ctra. Moncada-Náquera km 5, 46113 Moncada, Valencia
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  • M. Cambra

    Corresponding author
    1. Laboratorio de Virología e Inmunología, Centro de Protección Vegetal y Biotecnología, Instituto Valenciano de Investigaciones Agrarias (IVIA), Ctra. Moncada-Náquera km 5, 46113 Moncada, Valencia
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E-mail: mcambra@ivia.es

Abstract

The control of Plum pox virus (PPV), the most important viral disease that affects stone fruit trees, requires the use of reliable detection methods. The effectiveness of spot real-time reverse transcriptase polymerase chain reaction (RT-PCR) for the detection of PPV in samples collected from nursery blocks was compared with a validated PPV detection technique, the double antibody sandwich indirect enzyme-linked immunosorbent assay (DASI-ELISA) using the PPV-specific monoclonal antibody 5B-IVIA/AMR. In total, 5047 nursery plants were analysed by both techniques. The agreement between the techniques was almost perfect (Cohen’s kappa index of 0·88 ± 0·01). The diagnostic parameters (sensitivity, specificity and likelihood ratios) of both techniques were simultaneously evaluated in 2473 nursery plants by latent class models using maximum likelihood functions and a Bayesian approach. The sensitivity and specificity of both techniques did not vary according to the latent model applied. Spot real-time RT-PCR was more sensitive while DASI-ELISA was more specific for PPV detection. In addition, the findings demonstrate that latent class models are a flexible and potent statistical method to estimate the accuracy of diagnostic tests for plant pathology.

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