Abstract
Determining anthracnose-infested mango can involve laborious and time-consuming assays, resulting in delayed postharvest management and decreased fruit marketability. Near infrared spectroscopy (NIRS) is proposed to detect the fungus in fully matured ‘Namdokmai Sithong’ mango. Inoculation of Colletotrichum gloeosporioides (1 × 106 conidia/mL) was artificially made onto one side of the fruit’s peel at the center of mango fruit while the other side was left intact. Interactance measurements were conducted at both inoculated and intact locations for 104 mango samples every 24 h until anthracnose symptoms visibly appeared. The classification approaches included a partial least squares discriminant analysis (PLS-DA) and a conventional artificial neural network (ANN). Results of our study revealed increased absorbance values corresponding with days after inoculation. Relatively high classification accuracies were obtained from all chemometrics approaches (˃ 89%). In the early hours after inoculation (24 h), the best classification result was obtained from the ANN model (98.1%), confirming that early detection was possible. Applications of PLS-DA and ANN are discussed.
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Acknowledgements
The research leading to these results received funding from the School of Agricultural Technology, King Mongkut’s Institute of Technology Ladkrabang (KMITL), Bangkok, Thailand (grant number 2566-02-04-001) and the Postharvest Technology Innovation Center, Ministry of Higher Education, Science, Research and Innovation (Thailand).
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Conceptualization: Pimjai Seehanam & Phonkrit Maniwara; Methodology: Parichat Theanjumpol & Onuma Ruangwong; Formal analysis and investigation: Katthareeya Sonthiya, Parichat Theanjumpol, Onuma Ruangwong & Patcharaporn Suwor; Writing– original draft preparation: Katthareeya Sonthiya, Kazuhiro Nakano, Shintaroh Ohashi, Somsak Kramchote & Phonkrit Maniwara; Writing– review and editing: Katthareeya Sonthiya, Somsak Kramchote & Phonkrit Maniwara; Funding acquisition: Somsak Kramchote & Patcharaporn Suwor; Resources: Parichat Theanjumpol & Pimjai Seehanam; Supervision: Pimjai Seehanam, Phonkrit Maniwara & Somsak Kramchote.
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Seehanam, P., Sonthiya, K., Maniwara, P. et al. Ability of near infrared spectroscopy to detect anthracnose disease early in mango after harvest. Hortic. Environ. Biotechnol. (2024). https://doi.org/10.1007/s13580-023-00590-3
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DOI: https://doi.org/10.1007/s13580-023-00590-3