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Carbon Stable-Isotope and Physicochemical Data as a Possible Tool to Differentiate between Honey-Production Environments in Uruguay

The allocation of honey origin is an increasingly important issue worldwide as it is closely related to product quality and consumer preference. In South America, honeys produced in grasslands and eucalyptus or native forests are preferred at the regional level, so their differentiation is essential...

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Detalles Bibliográficos
Autor principal: Berriel, Verónica
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6025253/
https://www.ncbi.nlm.nih.gov/pubmed/29882757
http://dx.doi.org/10.3390/foods7060086
Descripción
Sumario:The allocation of honey origin is an increasingly important issue worldwide as it is closely related to product quality and consumer preference. In South America, honeys produced in grasslands and eucalyptus or native forests are preferred at the regional level, so their differentiation is essential to assure consumers of their authenticity according to their productive system. The objective of this study was to differentiate honeys produced in three environments: one, a monoculture system based on the eucalyptus forest, and two others based in natural environments of grasslands and native forests. To do this, honey’s physicochemical and isotopic variables (pH, free acidity, lactic acid content, moisture, total sugar content, and honey and extracted protein (13)C isotopic composition) were analysed. Discriminant analysis applied to the data revealed that, based on the selected variables, it was impossible to differentiate the three groups of honeys due to the superposition of those produced in grasslands and native forests. For this reason, a group of honeys derived from native and polyfloral environments (grasslands and native forests) was formed and subjected to discriminant analysis (DA), together with the group of honeys derived from a commercial plantation of eucalyptus forest. The model obtained in this case achieved 100% correct allocation both at the training stage and the cross-validation stage.