Phenolic compound, organic acid, mineral, and carbohydrate profiles of pine and blossom honeys

Pine and blossom honey have different levels of consumer appeal. To avoid fraud and adulteration, it is important to distinguish between the two types of honey. The identification of honey as blossom and pine remains difficult. In the current study, palynological parameters, phenolics, organic acids, sugars, and minerals were investigated in blossom and pine honey samples to establish an accurate botanical differentiation. Initially, honey samples were tested using palynological analysis. Afterward, the phenolic compounds and organic acid profiles were investigated by ultra-high-performance liquid chromatography (UHPLC) coupled with tandem mass spectrometry (MS/MS). In pine honey samples, protocatechuic, gallic, caffeic, ferulic, and syringic acids were the most prevalent phenolic compounds, and taxifolin was also found as a unique flavonoid. Lower levels of organic acids such as benzoic and glutaric acid were also detected in pine honeys. According to elemental analysis using an inductively coupled plasma-mass spectrometer (ICP-MS), elevated levels of potassium, magnesium, and iron were found. Through carbohydrate analysis by high-performance liquid chromatography (HPLC), di- and trisaccharides were apparently established in pine honey. The results of the analyses were statistically interpreted using data mining algorithms. Gallic, trans-cinnamic, protocatechuic, and 4-hydroxybenzoic acids from phenolic compounds, as well as oxalic and benzoic acids from organic acids, were discovered to be significant chemicals to identify between pine and floral honey by data mining analysis. [GRAPHICS]

Automated summary

To avoid fraud and adulteration, it is important to distinguish between pine and blossom honey. In this study, various analytical approaches including palynological analysis, UHPLC-MS/MS, ICP-MS, and HPLC were used to differentiate between the two types of honey. Significant chemicals such as phenolic compounds and organic acids were identified using data mining analysis.