Literature DB >> 18943759

Development of conductive polymer analysis for the rapid detection and identification of phytopathogenic microbes.

A D Wilson, D G Lester, C S Oberle.   

Abstract

ABSTRACT Conductive polymer analysis, a type of electronic aroma detection technology, was evaluated for its efficacy in the detection, identification, and discrimination of plant-pathogenic microorganisms on standardized media and in diseased plant tissues. The method is based on the acquisition of a diagnostic electronic fingerprint derived from multisensor responses to distinct mixtures of volatile metabolites released into sampled headspace. Protocols were established to apply this technology specifically to plant disease diagnosis. This involved development of standardized cultural methods, new instrument architecture for sampling, sample preparation, prerun procedures, run parameters and schedules, recognition files and libraries, data manipulations, and validation protocols for interpretations of results. The collective output from a 32-sensor array produced unique electronic aroma signature patterns diagnostic of individual microbial species in culture and specific pathogen-host combinations associated with diseased plants. The level of discrimination applied in identifications of unknowns was regulated by confidence level and sensitivity settings during construction of application-specific reference libraries for each category of microbe or microbe-host combination identified. Applications of this technology were demonstrated for the diagnosis of specific disease systems, including bacterial and fungal diseases and decays of trees; for host identifications; and for determinations of levels of infection and relatedness between microbial species. Other potential applications to plant pathology are discussed with some advantages and limitations for each type of diagnostic application.

Entities:  

Year:  2004        PMID: 18943759     DOI: 10.1094/PHYTO.2004.94.5.419

Source DB:  PubMed          Journal:  Phytopathology        ISSN: 0031-949X            Impact factor:   4.025


  13 in total

Review 1.  Advances in electronic-nose technologies developed for biomedical applications.

Authors:  Alphus D Wilson; Manuela Baietto
Journal:  Sensors (Basel)       Date:  2011-01-19       Impact factor: 3.576

Review 2.  Potential Applications and Limitations of Electronic Nose Devices for Plant Disease Diagnosis.

Authors:  Antonio Cellini; Sonia Blasioli; Enrico Biondi; Assunta Bertaccini; Ilaria Braschi; Francesco Spinelli
Journal:  Sensors (Basel)       Date:  2017-11-11       Impact factor: 3.576

Review 3.  Diverse applications of electronic-nose technologies in agriculture and forestry.

Authors:  Alphus D Wilson
Journal:  Sensors (Basel)       Date:  2013-02-08       Impact factor: 3.576

4.  Evaluation of three electronic noses for detecting incipient wood decay.

Authors:  Manuela Baietto; Alphus D Wilson; Daniele Bassi; Francesco Ferrini
Journal:  Sensors (Basel)       Date:  2010-01-29       Impact factor: 3.576

5.  Applications and advances in electronic-nose technologies.

Authors:  Alphus D Wilson; Manuela Baietto
Journal:  Sensors (Basel)       Date:  2009-06-29       Impact factor: 3.576

Review 6.  Electronic-nose applications for fruit identification, ripeness and quality grading.

Authors:  Manuela Baietto; Alphus D Wilson
Journal:  Sensors (Basel)       Date:  2015-01-06       Impact factor: 3.576

7.  Diagnosis of Varroosis Based on Bee Brood Samples Testing with Use of Semiconductor Gas Sensors.

Authors:  Beata Bąk; Jakub Wilk; Piotr Artiemjew; Jerzy Wilde; Maciej Siuda
Journal:  Sensors (Basel)       Date:  2020-07-19       Impact factor: 3.576

8.  Detection of off-flavor in catfish using a conducting polymer electronic-nose technology.

Authors:  Alphus D Wilson; Charisse S Oberle; Daniel F Oberle
Journal:  Sensors (Basel)       Date:  2013-11-25       Impact factor: 3.576

9.  Enhancing Electronic Nose Performance Based on a Novel QPSO-KELM Model.

Authors:  Chao Peng; Jia Yan; Shukai Duan; Lidan Wang; Pengfei Jia; Songlin Zhang
Journal:  Sensors (Basel)       Date:  2016-04-11       Impact factor: 3.576

Review 10.  Plant Pest Detection Using an Artificial Nose System: A Review.

Authors:  Shaoqing Cui; Peter Ling; Heping Zhu; Harold M Keener
Journal:  Sensors (Basel)       Date:  2018-01-28       Impact factor: 3.576

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