Aloğlu, Ahmet KemalHarrington, Peter de B2023-02-282023-02-282017-06-02Aloğlu, A. K. vd. (2017). ''Chemical profiling of floral and chestnut honey using high-performance liquid chromatography-ultraviolet detection''. Journal of Food Composition and Analysis, 62, 205-210.0889-15751096-0481https://doi.org/10.1016/j.jfca.2017.06.002https://www.sciencedirect.com/science/article/pii/S0889157517301400http://hdl.handle.net/11452/31255Using the two-way images of phenolic compounds from high-performance liquid chromatography-ultraviolet diode array detection (HPLC-DAD), floral and chestnut honey from Turkey were successfully differentiated. A fuzzy rule-building expert system (FuRES), support vector machine classification tree (SVMTreeG), and super partial least-square discriminant analysis (sPLS-DA) were used to develop classification models. Normalization, retention time alignment, square root transform, and dissimilarity kernel were evaluated as data preprocessing methods. The bootstrapped Latin partition was used with 100 bootstraps and 4 partitions. Classification rates of FuRES and SVMTreeG with a square root transform were 97.6 +/- 0.4% and 97.6 +/- 0.4% for classifying the type of honey, respectively. The measures of precision are 95% confidence intervals. HPLC-DAD was demonstrated as a reliable analytical method for authentication of honey.eninfo:eu-repo/semantics/closedAccessChemistryFood science & technologyChemometricsChestnut honeyClassificationFloral honeyFood analysisFood compositionFuRESHPLC-DADPhenolic compoundsSVMTreeGPartial least-squaresBuilding expert-systemsAntioxidant capacitiesPhysicochemical propertiesDiscriminant-analysisItalian honeysClassificationSpectrometryHplcAuthenticationArticle0004076604000252-s2.0-8502163206620521062Chemistry, appliedyFood science & technologyHoney; Stingless Bees; Botany