Analysis of volatile compounds in New Zealand unifloral honeys by SPME-GC-MS and chemometric-based classification of floral source.

摘要

New Zealand unifloral honeys have a higher commercial value than polyfloral honeys; however identification of floral source can be difficult and time-consuming. In this study, we aimed to establish a rapid and semi-automated method for identifying the floral source of New Zealand honeys. Volatile compounds from ten types of New Zealand unifloral honeys (a total of 234 samples) were analyzed by solid-phase microextraction (SPME) and gas chromatography coupled to mass spectrometry (GC-MS). For 37 compounds, probability plots of log 10[GC-MS peak area] versus cumulative probability enabled visual identification of those that could be possible markers used to discriminate floral source. GC-MS peak areas were also analyzed by hierarchical cluster analysis and principal component analysis. Results showed data falling into groups based on floral source, indicating that supervised pattern recognition could be used to build a model with which to classify honeys based on floral source. A model was built using WEKA (Waikato Environment for Knowledge Analysis) machine-learning software. The logistic model tree algorithm in WEKA produced a model that classified 89.8% of samples correctly. Overall, results show that the methods employed here have the potential to be used as a basis for routine testing and classification of New Zealand unifloral honeys.