April 7, 2017

Botanical discrimination and classification of honey samples applying GC-MS fingerprinting of headspace volatile compounds

A method for validating discrimination and classification of honey samples performing GC/MS fingerprinting of headspace volatile compounds has been created. A number of varied honey samples from different plants and geographical regions around Greece were tested using orthogonal partial least squares-discriminant analysis (OPLS-DA, soft independent modelling of class analogy (SIMCA) and OPLS-hierarchical cluster analysis (OPLS-HCA). The results revealed an excellent separation between the honey’s tested depending on their botanical origin, and the misclassification was as low as 1.3%. Fragments which determined the separations were assigned to phenolic, terpenoid, and aliphatic compounds that are present in the headspace of unifloral honey. Furthermore, a variable classification for citrus and thyme honey’s relating to geographical origins could also be achieved. Thus results suggest that the developed methodology is both robust and reliable.

http://www.umf.org.nz/wp-content/myimages/2017/02/Botanical-discrimination-and-classification-of-honey-samples-applying-GC-MS-fingerprinting-of-headspace-volatile-compounds.pdf

 

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