A rapid method using FTIR-ATR spectroscopy combined with PCA and a classification model was applied to distinguish between natural, synthetic, and adulterated Bergamot essential oil (BEO). Synthetic BEOs are often composed of specific alcohols such as ethanol and dipropylene glycol (DPG), which are used to dilute synthetic metabolites like limonene, linalyl acetate, and linalool. Synthetic BEOs exhibited a distinct peak at 1340 cm−1, linked to C-H bending of alcohols or methyl group deformation in artificial esters like linalyl acetate, a peak that is absent in natural BEOs. Additionally, an absorption band between 3600 and 3100 cm−1 indicated the presence of DPG and synthetic ethanol, a byproduct of synthetic linalyl acetate. These findings were validated by comparison with NMR spectroscopy for metabolite recognition. A logistic regression (LR) model using PCA was applied to 369 samples, achieving an overall accuracy of 0.976 ± 0.016 through five-fold cross-validation (CV).
Application of FTIR and PCA-LR metabolites recognition for bergamot essential oil authentication
Manin, Laura;Oliva, Giuseppe;Bianco, Maria Giovanna;Lagana, Filippo;Fiorillo, Antonino S.;Pullano, Salvatore A.
2026-01-01
Abstract
A rapid method using FTIR-ATR spectroscopy combined with PCA and a classification model was applied to distinguish between natural, synthetic, and adulterated Bergamot essential oil (BEO). Synthetic BEOs are often composed of specific alcohols such as ethanol and dipropylene glycol (DPG), which are used to dilute synthetic metabolites like limonene, linalyl acetate, and linalool. Synthetic BEOs exhibited a distinct peak at 1340 cm−1, linked to C-H bending of alcohols or methyl group deformation in artificial esters like linalyl acetate, a peak that is absent in natural BEOs. Additionally, an absorption band between 3600 and 3100 cm−1 indicated the presence of DPG and synthetic ethanol, a byproduct of synthetic linalyl acetate. These findings were validated by comparison with NMR spectroscopy for metabolite recognition. A logistic regression (LR) model using PCA was applied to 369 samples, achieving an overall accuracy of 0.976 ± 0.016 through five-fold cross-validation (CV).I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.


