Comparison of chemometric assisted targeted and untargeted approaches for the prediction of radical scavenging activity of ylang-ylang essential oils.

Essential oils have been used for centuries for their preservative properties. An example is ylang-ylang Cananga odorata [Lam.] Hook. f. & Thomson essential oil, which exists in four different distillation grades, where the fraction with the longest distillation time has the highest radical scavenging activity (RSA). Gas chromatography mass spectrometry (GC-MS) followed by multivariate statistical analysis is a powerful approach for determination of RSA. Herein the performance of such multivariate statistical analysis using three data sets derived from gas chromatography mass spectrometry (GC-MS) analysis, is compared to that achieved using two direct and fast spectroscopic techniques, for the prediction of RSA using partial least squares (PLS) regression analysis. The three GC-MS data sets were, 'full chemical composition', 'total chromatogram average mass spectra (TCAMS)' and 'segment average mass spectra (SAMS)', whilst two spectroscopic techniques, namely attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy and Raman spectroscopy, provided the spectroscopic data sets for comparison. PLS models created using ATR-FTIR and 'full chemical composition' data sets provided the lowest relative error of prediction (REP) and mean error of prediction (MEP) in validation, whilst in independent test sets, the PLS models created using ATR-FTIR and SAMS data sets delivered the lowest REP and MEP. The three GC-MS derived data sets were further compared for value in determination of compounds contributing to the RSA. PLS regression analysis of the full chemical composition data set revealed that germacrene D and (E,E)-α-farnesene were the major contributors to the RSA, whilst average mass spectrum based data sets, TCAMS and SAMS, also highlighted eugenol as another contributor to the RSA.

Smartphone-based handheld Raman spectrometer and machine learning for essential oil quality evaluation.

We present a method, utilising a smartphone-based miniaturized Raman spectrometer and machine learning for the fast identification and discrimination of adulterated essential oils (EOs). Firstly, the approach was evaluated for discrimination of pure EOs from those adulterated with solvent, namely benzyl alcohol. In the case of ylang-ylang EO, three different types of adulteration were examined, adulteration with solvent, cheaper vegetable oil and a lower price EO. Random Forest and partial least square discrimination analysis (PLS-DA) showed excellent performance in discriminating pure from adulterated EOs, whilst the same time identifying the type of adulteration. Also, utilising partial least squares regression analysis (PLS) all adulterants, namely benzyl alcohol, vegetable oil and lower price EO, were quantified based on spectra recorded using the smartphone Raman spectrometer, with relative error of prediction (REP) being between 2.41-7.59%.

Radical scavenging activity and metabolomic profiling study of ylang-ylang essential oils based on high-performance thin-layer chromatography and multivariate statistical analysis.

Ylang-ylang (YY) essential oil (EO) is distilled from the fresh-mature flowers of the Annonaceae family tropical tree Cananga odorata [Lam.] Hook. f. & Thomson, and is widely used in perfume and cosmetic industries for its fragrant character. Herein, two different metabolomic profiles obtained using high-performance thin-layer chromatography (HPTLC), applying different stains, namely 2,2-diphenyl-1-picrylhydrazyl (DPPH·) and p-anisaldehyde, were used for discrimination of 52 YY samples across geographical origins and distillation grades. The first profile is developed using the DPPH· stain based on the radical scavenging activity (RSA) of YY EOs. Results of the HPTLC-DPPH· assay confirmed that RSA of YY EOs is in proportion to the length of distillation times. Major components contributing to the RSA of YY EOs were tentatively identified as germacrene D and α-farnesene, eugenol and linalool, by gas chromatography-mass spectrometry (GC-MS) and GC-flame ionisation detector (GC-FID). The second profile was developed using the general-purpose p-anisaldehyde stain based on the general chemical composition of YY EOs. Untargeted metabolomic discrimination of YY EOs from different geographical origins was performed based on the HPTLC-p-anisaldehyde profiles, followed by principal component analysis (PCA). A discrimination and prediction model for identification of YY distillation grade was developed using PCA and partial least squares regression (PLS) based on binned HPTLC-ultraviolet (254 nm) profiles, which was successfully applied to distillation grade determination of blended YY Complete EOs.

Comprehensive characterisation of ylang-ylang essential oils according to distillation time, origin, and chemical composition using a multivariate approach applied to average mass spectra and segmented average mass spectral data.

Analyses of the complex essential oil samples using gas chromatography hyphenated with mass spectrometry (GC-MS) generate large three-way data arrays. Processing such large data sets and extracting meaningful information in the metabolic studies of natural products requires application of multivariate statistical techniques (MSTs). From the GC-MS raw data several different input data sets for the MSTs can be created, including total chromatogram average mass spectra (TCAMS), segmented average mass spectra (SAMS) and chemical composition. Herein, we compared the performance of MSTs on average mass spectrum based data sets, TCAMS and SAMS, against chemical composition and attenuated total reflectance - Fourier transformation infrared (ATR-FTIR) spectroscopy in the evaluation of quality of ylang-ylang essential oils, based on their grade, geographical origin and chemical composition, using principal component analysis (PCA), partial least squares regression (PLS) and discriminatory analysis (PLS-DA). PCA based on TCAMS, SAMS and chemical composition showed clear trends amongst the samples based on increase in grade (distillation time). PLS-DA applied to TCAMS, SAMS and ATR-FTIR discriminated between all geographical origins. Predicted relative abundances of the 18 most important compounds, using PLS regression models on TCAMS, SAMS and ATR-FTIR, were successfully applied to ylang-ylang essential oil quality assessment based on comparison with the ISO 3063:2004 standard, where the SAMS data set showed superior performance, compared to other data sets.