Introduction: Tea tree oil (TTO) is an important commercial oil which has found application in the flavour, fragrance and cosmetic industries. The quality is determined by the relative concentration of its major constituents: 1,8-cineole, terpinen-4-ol, α-terpineol, α-terpinene, terpinolene, γ-terpinene and limonene. Gas chromatography coupled to mass spectrometry (GC-MS) is traditionally used for qualitative and quantitative analyses but is expensive and time consuming.
Objective: To evaluate the use of vibrational spectroscopy in tandem with chemometric data analysis as a fast and low-cost alternative method for the quality control of TTO.
Methods: Spectral data were acquired in both the mid-infrared (MIR) and near infrared (NIR) wavelength regions and reference data obtained using GC-MS with flame ionisation detection (FID). Principal component analysis (PCA) was used to investigate the data by observing clustering and identifying outliers. Partial least squares (PLS) multivariate calibration models were constructed for the quantification of the seven major constituents.
Results: High correlation coefficients (R(2) ) of ≥ 0.75 were obtained for the seven major compounds and 1,8-cineole showed the best correlation coefficients for both MIR and NIR data (R(2) = 0.97 and 0.95, respectively). Low values were obtained for the root mean square error of estimation (RMSEE) and root mean square error of prediction (RMSEP) values thereby confirming accuracy.
Conclusion: The accurate prediction of the external dataset after introduction into the models confirmed that both MIR and NIR spectroscopy are valuable methods for quantification of the major compounds of TTO when compared with the reference data obtained using GC-MS. Copyright © 2013 John Wiley & Sons, Ltd.
Keywords: Chemometric data analysis; Melaleuca alternifolia; mid-infrared spectroscopy; near-infrared spectroscopy; tea tree oil.
Copyright © 2013 John Wiley & Sons, Ltd.