Fourier transform infrared spectroscopy (FTIR) and z-Nose were used as screening tools for the identification and classification of honey from different floral sources. Honey samples were scanned using microattenuated total reflectance spectroscopy in the region of 600-4000 cm(-1). Spectral data were analyzed by principal component analysis, canonical variate analysis, and artificial neural network for classification of the different honey samples from a range of floral sources. Classification accuracy near 100% was achieved for clover (South Dakota), buckwheat (Missouri), basswood (New York), wildflower (Pennsylvania), orange blossom (California), carrot (Louisiana), and alfalfa (California) honey. The same honey samples were also analyzed using a surface acoustic wave based z-Nose technology via a chromatogram and a spectral approach, corrected for time shift and baseline shifts. On the basis of the volatile components of honey, the seven different floral honeys previously mentioned were successfully discriminated using the z-Nose approach. Classification models for FTIR and z-Nose were successfully validated (near 100% correct classification) using 20 samples of unknown honey from various floral sources. The developed FTIR and z-Nose methods were able to detect the floral origin of the seven different honey samples within 2-3 min based on the developed calibrations.