Distinguishing Turkish pine honey from multi-floral honey through MALDI-MS-based N-glycomics and machine learning

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dc.authoridKARAV, SERCAN/0000-0003-4056-1673
dc.contributor.authorMasri, Saad
dc.contributor.authorAksoy, Sena
dc.contributor.authorDuman, Hatice
dc.contributor.authorKarav, Sercan
dc.contributor.authorKayili, Haci Mehmet
dc.contributor.authorSalih, Bekir
dc.date.accessioned2025-01-27T20:20:27Z
dc.date.available2025-01-27T20:20:27Z
dc.date.issued2024
dc.departmentÇanakkale Onsekiz Mart Üniversitesi
dc.description.abstractHoney, a multifaceted blend of sugars, amino acids, vitamins, proteins, and minerals, exhibits compositional variability dependent upon the floral source. While previous studies have attempted to categorize honey, the use of glycomic profiles for honey classification remains an unexplored avenue. This investigation seeks to establish a methodology for distinguishing honey types, specifically multi-floral and pine honey, employing mass spectrometry-based glycomic analysis in tandem with machine learning. In this search, seven samples of pine honey and eight samples of multi-floral honey were obtained from diverse regions of Turkey. Subsequently, the proteins within these honey samples were extracted, and glycans were enzymatically released. The released glycans were labeled with 2-aminobenzoic acid (2-AA) and subjected to analysis via matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS). The glycan profiles of pine and multi-floral honey were determined through these analytical procedures, revealing a total of 76 distinct N-glycan structures. Among these, 13 N-glycan profiles consistently established at high levels across experimental replicates and were incorporated in subsequent analyses. Following the quantification of individual glycan abundances, statistically significant differences in glycan profiles were determined. Notably, N-glycans Hex5HexNAc2, Hex4HexNAc3, and Hex5HexNAc3 displayed considerable differences. Using the 13 N-glycan profiles, an accuracy rate of 93.5% was obtained from machine learning analysis, which increased to 100% when incorporating the identified significantly changed glycans. The most productive models were identified as subspace and fine k-nearest neighbors (KNN). The findings underscore the potential of mass spectrometry-based glycomics in conjunction with machine learning as a robust tool for precise honey type classification and its prospective utility in quality control and honey product authentication.
dc.description.sponsorshipMinistry of Development-Republic of Turkiye [2016 K121230]; Turkish Academy of Science (TUBA); Scientific and Technological Research Council of Turkiye (TUBIdot;TAK)
dc.description.sponsorshipOpen access funding provided by the Scientific and Technological Research Council of Turkiye (TUB & Idot;TAK)Partial funding for the research was received from the Ministry of Development-Republic of Turkiye, with Project Number 2016 K121230. Bekir Salih extends his gratitude to the Turkish Academy of Science (TUBA) for their financial suppor
dc.identifier.doi10.1007/s11694-024-02597-5
dc.identifier.endpage5682
dc.identifier.issn2193-4126
dc.identifier.issn2193-4134
dc.identifier.issue7
dc.identifier.scopus2-s2.0-85193595479
dc.identifier.scopusqualityQ1
dc.identifier.startpage5673
dc.identifier.urihttps://doi.org/10.1007/s11694-024-02597-5
dc.identifier.urihttps://hdl.handle.net/20.500.12428/21712
dc.identifier.volume18
dc.identifier.wosWOS:001228754400001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofJournal of Food Measurement and Characterization
dc.relation.publicationcategoryinfo:eu-repo/semantics/openAccess
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WoS_20250125
dc.subjectHoney classification
dc.subjectPine honey
dc.subjectMulti-floral honey
dc.subjectMachine learning
dc.subjectGlycomics
dc.titleDistinguishing Turkish pine honey from multi-floral honey through MALDI-MS-based N-glycomics and machine learning
dc.typeArticle

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