Rapid and accurate classification of Aspergillus ochraceous contamination in Robusta green coffee bean through near-infrared spectral analysis using machine learning

• Published in: Food control Vol. 145; p. 109446
• Main Authors: Ruttanadech, Nuttapong, Phetpan, Kittisak, Srisang, Naruebodee, Srisang, Siriwan, Chungcharoen, Thatchapol, Limmun‬, Warunee, Youryon, Pannipa, Kongtragoul, Pornprapa
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.03.2023
• Subjects: Classification; Coffee; Fungal contamination; Machine learning; Near-infrared; Coffee; Machine learning; Classification; Near-infrared; Fungal contamination
• ISSN: 0956-7135; 1873-7129
• DOI: 10.1016/j.foodcont.2022.109446

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. •Rapid classification of Aspergillus spp. contamination in coffee beans was studied.•Different machine learning algorithms were employed for performance comparison.•NIR-based technique has allowed early detection of the fungal-infected coffee beans.