Volatile-based diagnosis for pathogenic wood rot fungi affecting Singapore urban trees

• Main Author: Tan, Jhing Yein
• Other Authors: Hong Yan
• Format: Thesis-Master by Research
• Language: English
• Published: Nanyang Technological University 2024
• Subjects: Agricultural Sciences
• Online Access: https://hdl.handle.net/10356/173768
• Institution: Nanyang Technological University

The accurate and timely detection of fungal pathogens is crucial for effective wood rot disease management and prevention in Singapore urban trees. In this study, six species of wood rot pathogens – Fulvifomes siamensis, Rigidoporus microporus, Pyrrhoderma noxium, Lasiodiplodia theobromae, Ganoderma orbiforme and Ganoderma australe were studied. It was hypothesised that these species have distinct volatile profiles, and volatile-based detections tools - electronic-nose (e-nose) and solid-phase microextraction gas chromatography-mass spectrometry (SPME-GC-MS), can help identify these unique volatile profiles. This can be applied in the diagnosis of wood rot fungi in the field. This thesis showed that the six different species had unique volatile profiles via SPME-GC MS characterisation. A consistent, high-abundance volatile was identified for F. siamensis samples — 1,2,4,5-tetrachloro-3,6-dimethoxybenzene. The e-nose in its basic protocol is insufficient for disease diagnosis application. A new protocol was demonstrated with the introduction of nitrogen gas for purging sensor volatile residue. The purge enhanced the sensitivity of the e-nose sensors, allowing species-specific volatile profile differentiation. Additionally, the new protocol can be extended to distinguishing in vitro infection by F. siamensis and R. microporus fungi as early as two weeks post-treatment. This thesis also established a model that could assist in the identification of F. siamensis basidiocarps in real time field diagnosis and validated with molecular identification by DNA barcoding. Overall, volatile-based detection shows promising applicability in the field of wood rot disease detection, and can achieve early-stage, fungus species specific diagnosis for wood decay.