Chemical profiling of floral and chestnut honey using high-performance liquid chromatography-ultraviolet detection

Aloglu A. K., Harrington P. d. B., ŞAHİN S., Demir C., GÜNEŞ M. E.

JOURNAL OF FOOD COMPOSITION AND ANALYSIS, vol.62, pp.205-210, 2017 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 62
  • Publication Date: 2017
  • Doi Number: 10.1016/j.jfca.2017.06.002
  • Journal Name: JOURNAL OF FOOD COMPOSITION AND ANALYSIS
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.205-210
  • Keywords: Chestnut honey, Floral honey, Food analysis, Food composition, HPLC-DAD, Classification, Phenolic compounds, Chemometrics, FuRES, SVMTreeG, PARTIAL LEAST-SQUARES, BUILDING EXPERT-SYSTEMS, ANTIOXIDANT CAPACITIES, PHYSICOCHEMICAL PROPERTIES, DISCRIMINANT-ANALYSIS, ITALIAN HONEYS, CLASSIFICATION, SPECTROMETRY, HPLC, AUTHENTICATION
  • Bursa Uludag University Affiliated: Yes

Abstract

Using 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.