Ability of near infrared spectroscopy to detect anthracnose disease early in mango after harvest
| dc.contributor.author | Seehanam, Pimjai | |
| dc.contributor.author | Sonthiya, Katthareeya | |
| dc.contributor.author | Maniwara, Phonkrit | |
| dc.contributor.author | Theanjumpol, Parichat | |
| dc.contributor.author | Ruangwong, Onuma | |
| dc.contributor.author | Nakano, Kazuhiro | |
| dc.contributor.author | Ohashi, Shintaroh | |
| dc.contributor.author | Kramchote, Somsak | |
| dc.contributor.author | Suwor, Patcharaporn | |
| dc.date.accessioned | 2026-08-06T10:46:46Z | |
| dc.date.available | 2026-08-06T10:46:46Z | |
| dc.date.issued | 2024-08-01 | |
| dc.description.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 × 10<sup>6</sup> 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. | |
| dc.identifier.citation | Horticulture Environment and Biotechnology, 65(4), 581-591, 2024 | |
| dc.identifier.doi | 10.1007/s13580-023-00590-3 | |
| dc.identifier.issn | 22113452 | |
| dc.identifier.other | 2-s2.0-85182688094 | |
| dc.identifier.uri | https://dspace.kmitl.ac.th/handle/123456789/15821 | |
| dc.source | Horticulture Environment and Biotechnology | |
| dc.subject | Artificial neural network | |
| dc.subject | Early detection | |
| dc.subject | Nondestructive analysis | |
| dc.subject | Partial least squares | |
| dc.subject | Rapid detection | |
| dc.title | Ability of near infrared spectroscopy to detect anthracnose disease early in mango after harvest | |
| dc.type | Article |