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<p>Volatile organic compounds (VOCs) can serve as sensitive indicators of plant health and pathogen infection. In this study, gas chromatography–mass spectrometry combined with multivariate chemometric analysis was applied to identify VOC patterns specific to potato wart disease caused by the pathogen <i>Synchytrium endobioticum</i>. Healthy and artificially infected potato tubers were analyzed under controlled conditions, and the resulting chromatographic data were processed using a Python-based workflow integrating data merging, preprocessing, principal component analysis, and linear discriminant analysis. The chemometric models successfully distinguished infected from healthy tubers. Seven compounds, 1-methoxy-3-methylbutane, 3-methyl-1-butanol, 2-methyl-1-butanol, 2,3-butanediol, prenyl ethyl ether, styrene, and solavetivone, were identified as indicative for infection. In addition, a mass-specific evaluation demonstrated that discrimination is possible using selected ion fragments alone, providing a basis for simplified on-site applications. This study presents the first characterization of a volatile fingerprint for <i>S. endobioticum</i> infection and establishes a robust, time-efficient workflow for non-invasive detection of quarantine pathogens in potato crops.</p>

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Diagnostic volatile organic compounds for potato wart disease: a GC-MS based chemometric approach

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月16日

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  • Sarah Vermeeren,
  • Markus Witzler,
  • Phillip Raschke,
  • Carsten Engelhard,
  • Peter Kaul

摘要

Volatile organic compounds (VOCs) can serve as sensitive indicators of plant health and pathogen infection. In this study, gas chromatography–mass spectrometry combined with multivariate chemometric analysis was applied to identify VOC patterns specific to potato wart disease caused by the pathogen Synchytrium endobioticum. Healthy and artificially infected potato tubers were analyzed under controlled conditions, and the resulting chromatographic data were processed using a Python-based workflow integrating data merging, preprocessing, principal component analysis, and linear discriminant analysis. The chemometric models successfully distinguished infected from healthy tubers. Seven compounds, 1-methoxy-3-methylbutane, 3-methyl-1-butanol, 2-methyl-1-butanol, 2,3-butanediol, prenyl ethyl ether, styrene, and solavetivone, were identified as indicative for infection. In addition, a mass-specific evaluation demonstrated that discrimination is possible using selected ion fragments alone, providing a basis for simplified on-site applications. This study presents the first characterization of a volatile fingerprint for S. endobioticum infection and establishes a robust, time-efficient workflow for non-invasive detection of quarantine pathogens in potato crops.