Rapid Detection and Deep Characterization of Adulterants in Fennel Essential Oil Using Electronic Nose With...

As a classic typical of medicine and food homology, fennel essential oil (FEO) is at risk of fraud due to its high commercial value and soaring consumer demand. This study developed an integrated analysis strategy using electronic nose (E-nose), gas chromatography-mass spectrometry (GC-MS), and chemometrics to achieve rapid identification and quantitative analysis of FEO and its adulterants (anise essential oil [AEO], dill essential oil [DEO], caraway essential oil [CAEO], and cumin essential oil [CUEO]). E-nose combined with principal component analysis (PCA) successfully enabled rapid differentiation of FEO from four common adulterants. Using GC-MS-based untargeted metabolomics coupled with orthogonal partial least squares discriminant analysis (OPLS-DA), 17 differential metabolites were screened, of which 15 were further validated as key markers via gas chromatography-triple quadrupole mass spectrometry (GC-QQQ-MS)-targeted quantitative analysis. On this basis, partial least squares discriminant analysis (PLS-DA) and partial least squares regression (PLS-R) models were established to achieve adulterant type identification and adulteration ratio prediction. The results demonstrated that both established models exhibited good fitting and predictive capabilities. The PLS-DA model achieved 100% classification accuracy for samples with adulteration ratios exceeding 30%, whereas the prediction errors of the PLS-R models for all four adulteration systems were below 10%. This integrated strategy demonstrates the potential to detect FEO adulteration, thereby offering a valuable reference for the quality control of FEO in future studies.