Rapid and accurate classification of Aspergillus ochraceous contamination in Robusta green coffee bean through near-infrared spectral analysis using machine learning
| dc.contributor.author | Ruttanadech, Nuttapong | |
| dc.contributor.author | Phetpan, Kittisak | |
| dc.contributor.author | Srisang, Naruebodee | |
| dc.contributor.author | Srisang, Siriwan | |
| dc.contributor.author | Chungcharoen, Thatchapol | |
| dc.contributor.author | Limmun, Warunee | |
| dc.contributor.author | Youryon, Pannipa | |
| dc.contributor.author | Kongtragoul, Pornprapa | |
| dc.date.accessioned | 2026-08-06T10:41:14Z | |
| dc.date.available | 2026-08-06T10:41:14Z | |
| dc.date.issued | 2023-03-01 | |
| dc.description.abstract | Near-infrared (NIR) spectral-based classification of Aspergillus ochraceous contamination in the Robusta green coffee bean was investigated. Six different learning algorithms, including linear discriminant analysis (LDA), support vector machine (SVM), k-nearest neighbors (KNN), decision tree (Tree), Naive Bayes (NB), and quadratic discriminant analysis (QDA), were applied for the investigating purpose. Four classes of fungal contamination on coffee beans, non-fungal contaminated beans on day 1 and day 3 (NCB-D1 and NCB-D3) and fungal contaminated beans on day 1 and day 3 (CB-D1 and CB-D3), were set for the classification intention. Based on the 6 learning algorithms, the Tree approach was optimal, displaying a training accuracy of 97.5%. As proven by the testing dataset, the classification accuracy of the Tree was also at 97.5%. With this number, the Tree could correctly classify 100% between the contaminated and non-contaminated coffee beans. These findings exhibit the potential of the NIR spectroscopy accompanied by machine learning for the early detection of fungal contamination in green coffee beans. | |
| dc.identifier.citation | Food Control, 145, 2023 | |
| dc.identifier.doi | 10.1016/j.foodcont.2022.109446 | |
| dc.identifier.issn | 09567135 | |
| dc.identifier.other | 2-s2.0-85140339851 | |
| dc.identifier.uri | https://dspace.kmitl.ac.th/handle/123456789/14343 | |
| dc.source | Food Control | |
| dc.subject | Classification | |
| dc.subject | Coffee | |
| dc.subject | Fungal contamination | |
| dc.subject | Machine learning | |
| dc.subject | Near-infrared | |
| dc.title | Rapid and accurate classification of Aspergillus ochraceous contamination in Robusta green coffee bean through near-infrared spectral analysis using machine learning | |
| dc.type | Article |