Clustering honey samples with unsupervised machine learning methods using FTIR data

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dc.authoridAVCU, Fatih Mehmet/0000-0002-1973-7745
dc.authorwosidAVCU, Fatih Mehmet/ABG-8390-2020
dc.contributor.authorAvcu, Fatih M.
dc.date.accessioned2024-08-04T20:55:08Z
dc.date.available2024-08-04T20:55:08Z
dc.date.issued2024
dc.departmentİnönü Üniversitesien_US
dc.description.abstractThis study utilizes Fourier transform infrared (FTIR) data from honey samples to cluster and categorize them based on their spectral characteristics. The aim is to group similar samples together, revealing patterns and aiding in classification. The process begins by determining the number of clusters using the elbow method, resulting in five distinct clusters. Principal Component Analysis (PCA) is then applied to reduce the dataset's dimensionality by capturing its significant variances. Hierarchical Cluster Analysis (HCA) further refines the sample clusters. 20% of the data, representing identified clusters, is randomly selected for testing, while the remainder serves as training data for a deep learning algorithm employing a multilayer perceptron (MLP). Following training, the test data are evaluated, revealing an impressive 96.15% accuracy. Accuracy measures the machine learning model's ability to predict class labels for new data accurately. This approach offers reliable honey sample clustering without necessitating extensive preprocessing. Moreover, its swiftness and cost-effectiveness enhance its practicality. Ultimately, by leveraging FTIR spectral data, this method successfully identifies similarities among honey samples, enabling efficient categorization and demonstrating promise in the field of spectral analysis in food science.en_US
dc.identifier.doi10.1590/0001-3765202420230409
dc.identifier.issn0001-3765
dc.identifier.issn1678-2690
dc.identifier.issue1en_US
dc.identifier.pmid38451625en_US
dc.identifier.scopus2-s2.0-85187207155en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.1590/0001-3765202420230409
dc.identifier.urihttps://hdl.handle.net/11616/101866
dc.identifier.volume96en_US
dc.identifier.wosWOS:001182273300001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherAcad Brasileira De Cienciasen_US
dc.relation.ispartofAnais Da Academia Brasileira De Cienciasen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectFouirer transform infrared spectrophotometeren_US
dc.subjecthierarchical clustering analysisen_US
dc.subjectmachine learningen_US
dc.subjectdeep Learningen_US
dc.titleClustering honey samples with unsupervised machine learning methods using FTIR dataen_US
dc.typeArticleen_US

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