Essential‐oil inspired synthetic libraries: synthesis, spectral and gas‐chromatographic properties of new fenchyl esters

Abstract

Herein, the design, synthesis, and gas‐chromatographic and spectral characterization of a library of endo‐ and exo‐fenchyl esters (formate, acetate, propanoate, butanoate, isobutanoate, pentanoate, isopentanoate, 2‐methylbutanoate, hexanoate, heptanoate, octanoate, nonanoate, tiglate, angelate, senecioate and benzoate; 32 compounds in total) were described. 1D‐ and 2D–NMR experiments combined with 1H iterative full spin analysis and GIAO (gauge invariant atomic orbitals) computation of NMR chemical shifts and coupling constants for DFT‐calculated minimum energy conformations enabled a complete 1H–NMR assignment of the higher‐order signals of the fenchyl‐core protons. Library members, derivatives of a well‐known monoterpenol of economical importance, often encountered in essential oils (EOs), might potentially represent new (up to now undiscovered), or be classified as rare, natural products. The data summarized in this work should facilitate future EO analyses, especially those studies dealing with minor constituents. The benefits of the usage of (untargeted) metabolome‐inspired synthetic libraries in the analysis of EOs were discussed.

A synthetic library of allylmethoxyphenyl esters: spectral characterization and gas chromatographic behavior

“…An inspection of the RI trends pointed to ‘discrepancies’ (high RMSE values) coming from the formates. Due to the formal lower contribution of the acyl part to the overall structure of esters, formates are expected to interact somewhat differently with the GC stationary phase compared to higher esters within a homologous series of esters . Thus, in order to further test the linear correlation within these series, formates of all regioisomeric allylmethoxyphenols were excluded.…”

“…Values of RI are known to usually correlate with different types of molecular descriptors (Balaban index, molecular topological index, Wiener index, the total number of carbon atoms, etc. ), and we utilized this in several previous QSPR analyzes . The starting point in the new QSPR analysis was to find appropriate molecular descriptors allowing a fast but reliable RI prediction.…”

“…The have successfully used such an approach to identify different secondary metabolites present in essential oils through the use of QSPR equations based on simple 1D/2D molecular descriptors and verified them with experimental data (high R 2 and low root mean square of the errors, RMSE). 14,[22][23][24] We expected that, if we developed an appropriate QSPR model, we might be able to predict the retention indices for all of the possible regioisomers of allylmethoxyphenols (the applicability domain), that is, also predict RI values for esters not synthesized in this work (derivatives of the five remaining out of the 10 in total allylmethoxyphenyl regioisomers).…”

“…), and we utilized this in several previous QSPR analyzes. 14,23,24 The starting point in the new QSPR analysis was to find appropriate molecular descriptors allowing a fast but reliable RI prediction. All of these molecular descriptors convey a certain part, or a combination of the information regarding the molecules -its polarity and polarizability.…”