The aim of this study was to use gas chromatography-mass spectrometry (GC-MS) and APCI-MS techniques to detect adulteration in honey. The key volatile compounds in the headspace of the adulterated honeys were marked by GC-MS and their representative fragment ions were utilized in scanning honey samples using the real-time APCI-MS system. The PLS models validated using independent datasets resulted in coefficient of determination (R_p^2) of 0.97 and 0.96 and root mean square error in prediction (RMSEP) of 2.62 and 2.45 for the GC-MS and APCI-MS datasets, respectively. The most efficient volatiles from GC-MS analysis and their corresponding fragment ions m/z from APCI-MS data analysis were then identified and used to develop new PLS models to predict the level of adulteration. The best PLS model gave R_p^2 of 0.95 and RMEP of 2.60% in the independent validation set indicating that the model was very accurate in predicting the level of adulteration
