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Predictive classifier of anthracnose disease in 'Namdokmai Sithong' mango fruit using reflectance spectroscopy
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DOI：10.1079/ejhs.2026.0008.png)
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Abstract
En 中文
Anthracnose disease, caused by the fungal pathogen Colletotrichum spp., constitutes a major postharvest challenge to the quality and commercial viability of 'Namdokmai Sithong' mangoes. Traditional methods for disease detection, which often rely on subjective visual inspection, are only effective once symptoms are externally visible. This study was conducted to develop a rapid, objective and non-destructive method for the early classification of this latent disease using near-infrared spectroscopy (NIRS). Mango fruits at commercial maturity (100-105 days after flowering) with export-grade quality from a certified orchard were used to simulate natural infection. Each fruit was half-dipped horizontally for 1 min in a 1.4 & times; 105 spores/ml suspension of Colletotrichum spp. Near-infrared reflectance spectra (800-2500 nm) were subsequently acquired from 52 inoculated and 48 uninoculated mango fruits at 24 h over a 4-day period. An artificial neural network (ANN) classifier with k-fold cross-validation (k = 5) achieved a precise classification result through the first-derivative (1D) spectra at the early stage of inoculation (24 h). A key finding was the absence of any false positive predictions from the 1D-ANN model during the crucial initial incubation period (24, 48 and 72 h), which is critical for preventing the unnecessary rejection of healthy fruit. While the overall accuracy of most ANN models exhibited a slight decline at 96 h, the 1D-ANN classifier maintained a perfect accuracy of 100% without generating any false negatives. This study conclusively demonstrates the feasibility of using reflectance spectroscopy for the early and accurate detection of anthracnose disease, offering a valuable and reliable tool for effective postharvest disease management and mitigating significant financial losses within the Thai mango industry.
Keywords:
non-destructive detection
near-infrared spectroscopy
artificial neural network
k-fold cross-validation
early classifier
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