Essential oils (EOs) are natural products formed by plant volatile compounds. EOs are frequently used in the cosmetic and food industries as well as for domestic purposes, because of their physiochemical, biological and sensory properties. The functional groups (FG), corresponding to various chemical structures present in EOs, are responsible for their biological activities. Therefore, simple, rapid, and economical techniques suitable to characterize the EOs features by measuring their contents, are of great interest. Near-infrared spectroscopy (NIRS) highlights because of its rapidity, and being no-contaminant, as a potential solution. Multivariate correlation methods are commonly used to build NIRS calibrations. These methods were designed for the real space, that is for values comprised between - ∞ and + ∞. However, EOs components are co-dependent data restricted to a simplex space. These are the so-called compositional data (CoDa), needing specific methods to be correlated with a set of spectral explicative variables. In this study, compositional visible and near-infrared (VISNIRS) models have been assessed to quantify the FG of the analyzed EOs. For this purpose, the FG were organized according to their greater frequency in 1) alcohol; 2) ether; 3) ester; 4) aldehydes; 5) ketones, and the hydrocarbon fraction representing the remainder EOs mass, to characterize them. The approach of this study, based on compositional models from VISNIRS spectra, has provided a satisfactory predictive performance for the quantitative estimation of the main FG of the EOs. The proposed approach can be an alternative to traditional chemical methods to characterize EOs.
Keywords: CoDa; Compositional data; Essential oil; NIRS.
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