David A. Pollard

Multicomponent analyses of chiral samples by use of regression analysis of UV-visible spectra of cyclodextrin guest-host complexes.

We report the first combined use of analytical spectroscopy, guest–host chemistry, and multivariate regression analysis for determination of enantiometric composition of multicomponent samples of chiral analytes. Sample solutions containing multicomponent analytes of ephedrine, tryptophan, propranolol, and proline of varying enantiomeric composition with beta-cyclodextrin (BCD) or methyl-beta-cyclodextrin (Me-BCD) as chiral host molecules were investigated using ultraviolet (UV)–visible spectroscopy. The interactions of enantiomers of chiral analytes with chiral hosts resulted in the formation of transient diastereomeric inclusion complexes with varying spectral properties. Multivariate analysis using partial-least-square (PLS) regression was used to correlate subtle changes in the UV–visible spectra of the guest–host complexes with the enantiomeric composition of the calibration samples. These PLS regressions were carefully optimized and then used to predict the enantiomeric composition of multicomponent chiral analytes of validation samples. The results of these validation studies demonstrate the predictive ability of the regression models for determination of future enantiomeric composition of samples. The accuracy of the models to correctly predict the enantiomeric composition of samples, evaluated by use of the root mean square percent relative error (RMS%RE) was analyte and chiral host dependent. In general, better prediction of enantiomeric composition of samples and low RMS%RE values were obtained when Me-BCD was used as the chiral host. The analyses procedure reported here is simple, rapid, and inexpensive. In addition, this approach does not require prior separation of chiral analytes, thus reducing analysis time and eliminating the need for expensive chiral columns.

Determination of boiling point of petrochemicals by gas chromatography–mass spectrometry and multivariate regression analysis of structural activity relationship

Accurate understanding of analyte boiling points (BP) is of critical importance in gas chromatographic (GC) separation and crude oil refinery operation in petrochemical industries. This study reported the first combined use of GC separation and partial-least-square (PLS1) multivariate regression analysis of petrochemical structural activity relationship (SAR) for accurate BP determination of two commercially available (D3710 and MA VHP) calibration gas mix samples. The results of the BP determination using PLS1 multivariate regression were further compared with the results of traditional simulated distillation method of BP determination. The developed PLS1 regression was able to correctly predict analytes BP in D3710 and MA VHP calibration gas mix samples, with a root-mean-square-%-relative-error (RMS%RE) of 6.4%, and 10.8% respectively. In contrast, the overall RMS%RE of 32.9% and 40.4%, respectively obtained for BP determination in D3710 and MA VHP using a traditional simulated distillation method were approximately four times larger than the corresponding RMS%RE of BP prediction using MRA, demonstrating the better predictive ability of MRA. The reported method is rapid, robust, and promising, and can be potentially used routinely for fast analysis, pattern recognition, and analyte BP determination in petrochemical industries.

Determination of non-toxic and potentially toxic elements concentration and antioxidant capacity in selected natural and essential oils with high market values

ABSTRACT Natural oils (NOs) and essential oils (EOs) are widely used in the food and beverage, medical, aromatherapy and cosmetic industries, but little is known about their elemental composition or antioxidant ability. Microwave-assisted acid digestion and inductively coupled plasma-optical emission spectroscopy were used to determine the non-toxic elements (Al, Ca, Cu, Fe, K, Mg, Na, Se and Zn) and potentially toxic elements (As, Cr, Cd, Mn, Ni and Pb) concentrations in 13 selected NOs and EOs. The per cent recoveries of laboratory-fortified blanks analysed for quality control were 94–110%. The elemental concentrations varied widely in NO and EO samples, as demonstrated by the large standard deviation obtained for some elements. The average levels of non-toxic elements (Al (14.5 ± 3.7 μg/g); Ca (278 ± 138 μg/g); Cu (7 ± 14 μg/g); Fe (16 ± 5 μg/g); K (36 ± 31 μg/g); Mg (56 ± 27 μg/g); Na (266 ± 277 μg/g); Se (0.7 ± 0.3 μg/g) and Zn (6.1 ± 2.6 μg/g)) were determined in NOs and EOs. Comparatively, low levels of potentially toxic elements (As (0.1 ± 0.2 μg/g); Cd (0.1 ± 0.0 μg/g); Cr (0.2 ± 0.1 μg/g); Mn (0.8 ± 0.1 μg/g); Ni (4.5 ± 2.2 μg/g); and Pb (0.3 ± 0.2 μg/g)) were obtained in the oils. Principal component analysis (PCA) revealed that the first two principal components explained 100% of the variability in the elemental concentrations. Na, Ca, Mg and K were the main contributors to PCA. Non-toxic element pairs were strongly correlated (R2 > 0.9440) indicating a common source in these oils, but toxic element pairs were poorly correlated. Although toxic element concentrations were low, routine monitoring in oils is recommended. The antioxidant ability of NOs and EOs to potentially reduce free radicals, which are often involved in several degenerative diseases, such as ageing, stroke, diabetes and cancers was determined by DPPH (2,2-di(4-tert-octylphenyl)-1-picrylhydrazyl) free radical scavenging assay and ultraviolet-visible spectroscopy. Jasmine, castor and tea tree lemon oils were the best antioxidants. The oils in this study have the potential to replace artificial antioxidants used in foods, cosmetics and other products.