Search

Honey is a valuable functional food rich in phenolic compounds with a broad spectrum of biological activities. Analysis of the phenolic compounds in honey is a very promising tool for the quality control, the authentication and characterization of botanical origin, and the nutraceutical research. This work describes a novel approach for the rapid analysis of five phenolic acids and 10 flavonoids in honey. Phenolic compounds were rapidly extracted and concentrated from diluted honey by dispersive liquid-liquid microextraction (DLLME) and then analyzed using high performance liquid chromatography with UV absorbance detection (HPLC-UV). Some important parameters, such as the nature and volume of extraction and dispersive solvents, pH and salt effect were carefully investigated and optimized to achieve the best extraction efficiency. Under the optimal conditions, an exhaustive extraction for twelve of the investigated analytes (recoveries >70%), with a precision (RSD<10%) highly acceptable for complex matrices, and detection and quantification limits at ppb levels (1.4-12 and 4.7-40ngg(-1), respectively) were attained. The proposed method, compared with the most widely used method in the analysis of phenolic compounds in honey, provided similar or higher extraction efficiency, except in the case of the most hydrophilic phenolic acids. The capability of DLLME to the extraction of other honey phytochemicals, such as abscisic acid, was also demonstrated. The main advantages of developed method are the simplicity of operation, the rapidity to achieve a very high sample throughput and low cost., (Copyright © 2014 Elsevier B.V. All rights reserved.)

To better understand the biochemistry of the organoleptic properties of honey influencing its commercial value, a predictive model for correlating amino acid profiles to aromatic compounds was built. Because the amino acid composition of different varieties of honey plays a key role as a precursor of specific aroma bouquets, it is necessary to relate the amino acid typesetting to aromatic molecules. A selection of unifloral honeys produced in Calabria, South Italy, were used, and a new methodology based on the use of HILIC-UHPLC-ESI-MS/MS and HS-SPME-GC-MS combined with multivariate processing has been developed. This study, carried out for the first time on honey, shows its excellent potential as a modern analytical tool for a rapid multicomponent analysis of food-quality indicators. Data obtained showed strong positive linear correlations between aldehydes and isoleucine, valine, leucine, and phenylalanine. Furans are correlated with isoleucine, leucine, and phenylalanine; hydrocarbons with serine, glutamic acid, and aspartic acid; and ketones with serine, alanine, glutamine, histidine, asparagine, and lysine. Alcohols were more associated with tyrosine than esters with arginine. Proline, tryptophan, and threonine showed poor correlations with all the classes of aroma compounds. [ABSTRACT FROM AUTHOR]

High-performance anion exchange chromatography with pulsed amperometric detection (HPAEC–PAD) combined with chemometric analysis was developed to describe, for the first time, the sugar profile of sixty-one honeys of different botanical origin produced in southern Italy (Calabria Region). The principal component and linear discriminant analysis used to describe the variability of sugar data were able to discriminate the honeys according to their botanical origin with a correlation index higher than 90%. For the purpose of the robustness of the conclusions of this study, the analytical advantages of the HPAEC–PAD method have been statistically demonstrated compared to the official Italian HPLC–RI method (Refractive Index detection). Finally, as the characterization of the floral and geographical origin of honey became an important issue due to high consumer demand, 13 acacia honeys originating from Europe and China were studied by using the same method. By chemometric method it was possible to discriminate the different geographical origin with an index of 100%. All results proved the possibility to identify the sugar profile obtained by HPAEC–PAD combined with a robust statistical analysis, as a tool of authentication. [ABSTRACT FROM AUTHOR]