@article { , title = {Real‐time quality authentication of honey using atmospheric pressure chemical ionisation mass spectrometry ( APCI ‐ MS )}, abstract = {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.}, doi = {10.1111/ijfs.14210}, eissn = {1365-2621}, issn = {0950-5423}, issue = {11}, journal = {International Journal of Food Science \& Technology}, pages = {2983-2997}, publicationstatus = {Published}, publisher = {Wiley}, url = {https://nottingham-repository.worktribe.com/output/2135289}, volume = {54}, keyword = {Food Science, Industrial and Manufacturing Engineering}, year = {2019}, author = {ElMasry, Gamal and Morsy, Noha and Al?Rejaie, Salim and Ayed, Charfedinne and Linforth, Robert and Fisk, Ian} }