Identification of the floral source of New Zealand honeys
Petchell, L. E. (2009). Identification of the floral source of New Zealand honeys (Thesis, Master of Science (MSc)). The University of Waikato, Hamilton, New Zealand. Retrieved from http://hdl.handle.net/10289/8755
Permanent Research Commons link: http://hdl.handle.net/10289/8755
Depending on the nectar source, honey is either unifloral (derived mostly from one plant type), or polyfloral (derived from multiple plant types). Unifloral honey has characteristic sensory properties, and is therefore of greater commercial value. Currently, identification of floral source involves pollen counting, a specialised and labour intensive process. The current research was aimed at developing an alternative, rapid, chemistry-based method of floral identification. The aroma of honey depends on volatile compounds present; these may be derived from the plant from which nectar was taken. Therefore by identifying volatiles in honey it could be possible to identify floral source. Solid-phase microextraction (SPME) is a technique that is useful for the headspace analysis of volatile compounds; when coupled with GC-MS it provides a powerful tool for fingerprinting volatiles in honey. GC-MS chromatograms of ten New Zealand unifloral honey types were obtained after headspace SPME extraction. Statistical analysis of the GC-MS chromatographic data was used to discriminate between floral types. Probability plots were used to identify compounds indicative of floral source; this method discriminated between honey types with 90% success. Hierarchical cluster analysis and principal component analysis were used to study the structure of the data. Learning algorithms in Weka (machine-learning software) were used to build models of data to classify honey types. The logistic model tree algorithm classified 89.8% of samples correctly. Such a model has the potential to be used to classify future honey samples, once further samples have been tested to validate the model.
The University of Waikato
- Masters Degree Theses