A novel method for detection of camellia oil adulteration based on time-resolved emission fluorescence.

In this study, time-resolved emission fluorescence (TRES) combined with chemometrics was developed and employed for adulteration analysis of camellia oil. TRES was first decomposed by parallel factors analysis (PARAFAC). Next, an artificial neural network (ANN) model was built for the adulteration analysis. A linear range of 5-50%, a limit of detection (LOD) of 3% and root mean square error of prediction (RMSEP) values lower than 3% were achieved. Compared with the steady-state measurement, easy access to the information from fluorophores of low concentration was shown to be an intrinsic advantage of the time-resolved measurement; this advantageous characteristic was helpful for optimizing adulteration analysis. It was demonstrated that TRES combined with chemometrics was a simple, rapid and non-intrusive method for adulteration analysis of vegetable oil.