Tipificação de méis do estado de Sergipe através do perfil químico dos compostos voláteis obtidos por headspace dinâmico seguido por cromatografia em fase gasosa acoplada a espectrometria de massas (CG/EM) - Brito, Givanilton (2012)
Among the products of the hive, honey is considered the principal, standing out as natural food and for having multiple pharmacological applications. Honey can be produced by honey bees (Apis mellifera, L.) from the nectar, fruit, plant secretions and excretions of aphids or other sweetened solutions.Their nutritive power, pharmacologic and commercial value depends on its botanical origin, which can be obtained through classical methods as sensory evaluation, physicochemical analyses or melissopalynology. Although, these methods require much experience of the analyst and are costly.In view of the current difficulties in conducting these analyses, methods based on the study of volatile constituents have emerged as an alternative in the search for the source of compound markers of floral honeys. For the identification of these compounds, techniques such as solid in solid phase (SPME) and dynamic headspace (HSD) followed by analysis on gas chromatography coupled to mass spectrometer (GC-MS) are suggested. In this work, different honeyproducing regions in the State of Sergipe were studied, as well as samples of honey originated from other states of Brazil, purchased in local supermarkets. Analyses of volatile components were obtained by dynamic headspace using Porapak Q® and Peat in natura as adsorbent materials. For both, parameters such as amount of sample, salt addition, time and temperature of extractionhave been optimized. Optimization, made possible the identification of 112 different compounds belonging to classes of aliphatic alcohols, aliphatic aldehydes, benzene derivatives, monoterpene hydrocarbons, oxygenated hydrocarbons, norisoprenoids, sesquiterpenes, oxygenated sesquiterpenes, carboxylic acids and others. Among these, a group of senior compounds were studied by principal components analysis and hierarchical cluster analysis. With these analyses was likely to identify the components with biggest weights in the samples and cluster them into five groups with a similarity of 48% based on Euclidean distance. Among the weighty compounds are furfuraldehyde, benzaldehyde, cis-linalool oxide (furanoid), trans-linalool oxide (furanoid), linalool, hotrienol, 4-ketoisoforone, aldehyde lilac (isomer I), cis-linalool oxide (pyranoid) and -terpineol.