Version 1
: Received: 10 April 2024 / Approved: 11 April 2024 / Online: 11 April 2024 (11:13:59 CEST)
How to cite:
Ratnayake, W.; Bellgard, S.E.; Wang, H.; Murthy, V. Electronic Nose and GC-MS Analysis to Detect Mango Twig Tip Dieback in Mango (Mangifera indica) and Panama Disease (TR4) in Banana (Musa acuminata). Preprints2024, 2024040794. https://doi.org/10.20944/preprints202404.0794.v1
Ratnayake, W.; Bellgard, S.E.; Wang, H.; Murthy, V. Electronic Nose and GC-MS Analysis to Detect Mango Twig Tip Dieback in Mango (Mangifera indica) and Panama Disease (TR4) in Banana (Musa acuminata). Preprints 2024, 2024040794. https://doi.org/10.20944/preprints202404.0794.v1
Ratnayake, W.; Bellgard, S.E.; Wang, H.; Murthy, V. Electronic Nose and GC-MS Analysis to Detect Mango Twig Tip Dieback in Mango (Mangifera indica) and Panama Disease (TR4) in Banana (Musa acuminata). Preprints2024, 2024040794. https://doi.org/10.20944/preprints202404.0794.v1
APA Style
Ratnayake, W., Bellgard, S.E., Wang, H., & Murthy, V. (2024). Electronic Nose and GC-MS Analysis to Detect Mango Twig Tip Dieback in Mango (<em>Mangifera indica</em>) and Panama Disease (TR4) in Banana (<em>Musa acuminata</em>). Preprints. https://doi.org/10.20944/preprints202404.0794.v1
Chicago/Turabian Style
Ratnayake, W., Hao Wang and Vinuthaa Murthy. 2024 "Electronic Nose and GC-MS Analysis to Detect Mango Twig Tip Dieback in Mango (<em>Mangifera indica</em>) and Panama Disease (TR4) in Banana (<em>Musa acuminata</em>)" Preprints. https://doi.org/10.20944/preprints202404.0794.v1
Abstract
Volatile organic compounds (VOCs) released from plants have been correlated with disease-status. Analysis of VOCs using GC-MS is time-consuming, laboratory-based, and requires specialist training. Electronic nose devices (E-nose) provide a portable alternative. Three different E-nose devices were compared to assess how accurately they could detect Mango Twig Tip Dieback and Panama disease in banana. The devices were initially trained on known volatiles, then pure cultures of Pantoea sp., Staphylococcus sp., and Fusarium odoratissimum, and finally, on infected and healthy mango leaves and field-collected, infected banana pseudo-stems. The experiments were repeated three times with six replicates for each host-pathogen pair. The variation between healthy and infected host materials was evaluated by principal component and linear discriminant analysis, cross-validation and chemometric data analysis. GC-MS analysis was conducted contemporaneously and identified an 80% similarity between healthy and infected plant material. The portable C 320 was 100% successful in discriminating known volatiles but had a low capability in differentiating healthy and infected plant substrates. The advanced devices (PEN 3 / MSEM 160) successfully detected healthy and diseased samples with a high variance. The results suggest that E-nose devices are more sensitive and accurate in detecting changes of VOCs between healthy and infected plants compared to headspace GC-MS.
Keywords
electronic nose; headspace GC-MS analysis; Linear Discriminant Analysis (LDA); Principal Component Analysis (PCA); Volatile Organic Compounds (VOC); Chemometric Data Analysis (CDA); Panama Disease (TR4); Mango Twig Tip Dieback (MTTD)
Subject
Biology and Life Sciences, Agricultural Science and Agronomy
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
