Study of the interdependency of the data sampling ratio with retention time alignment and principal component analysis for gas chromatography

Fuel property modeling by high-speed gas chromatography coupled with partial least squares data analysis

Partial least squares (PLS) regression is a valuable chemometric tool for property prediction when coupled with gas chromatography (GC). Since the separation run time and stationary phase selection are crucial for effective PLS modeling, we study these GC parameters on the prediction of viscosity, density and hydrogen content for 50 aerospace fuels. Due to the diversity of compounds in the fuels (primarily alkanes, cycloalkanes, and aromatics), we explore both polar and non-polar stationary phase columns. The robustness for the PLS models was evaluated by their normalized root mean square error of cross-validation (NRMSECV). PLS models built for viscosity across 1-min, 3-min, 7-min, and 10-min time window (TW) high-speed GC separations produced nearly the same NRMSECV with the polar column data with an average (standard deviation) of 4.41 % (0.34 %) versus the non-polar column data of 4.69 % (0.15 %). In contrast, while the NRMSECV of density modeling with the polar column data varied more than the viscosity models, averaging 7.54 % (0.67 %), the non-polar column data produced a significantly higher average NRMSECV of 10.06 % (0.35 %). Similarly, for hydrogen content, the NRMSECV with the polar column data averaged 9.50 % (0.87 %), which was significantly lower than the NRMSECV with the non-polar column data averaging 12.10 % (0.88 %). We also investigated the impact of smoothing the GC data on the corresponding PLS models. By applying varying degrees of smoothing, we can effectively obtain similar chromatographic peak patterns in a shorter TW. For example, a 10-min smoothed chromatogram appears like the 1-min separation with no smoothing but resulted in nearly the same NRMSECV. Overall, the fast separation with a 1-min TW produced robust PLS models for viscosity with either stationary phase column, whereas for density and hydrogen content the polar stationary phase column produced superior PLS models, thus with proper stationary phase selection, a fast separation run time could be readily applied with optimal PLS property modeling results.

Implementation of multivariate techniques for the selection of volatile compounds as indicators of sensory quality of raw beef

This study was performed in order to select volatile compounds to predict the off-odour and overall assessment of raw beef's freshness Maronesa breed, using multivariate analysis. M. longissimus dorsi packed in vacuum and MAP (70 % O2/20 % CO2/10 % N2) stored at 4 ºC were examined for off-odour perception as well as the overall assessment of freshness at 10 and 21 days post mortem. The results achieved in this study demonstrated that the selected volatile compounds could be considered as volatile indicators of beef spoilage, enclosing information for discrimination of Maronesa beef samples in sensory classes of odour corresponding to unspoiled and spoiled levels. Fifty-four volatile compounds were detected. A significant increase of aldehydes, ketones and alcohols were observed during storage in MAP. 2 and 3-methylbutanal, 2 and 3-methylbutanol, 1-pentanol, 1-hexanol, 2,3-octanedione, 3,5-octanedione, octanal and nonanal were suggested as indicators of beef spoilage. 3-methylpentane was considered as a marker in the first stages of spoilage in beef, decreasing during storage. Data were examined using PCR and PLSR models for different optimal subsets of volatile compounds. The simplicity and usefulness of the technique in using 0/1 data in preserving high levels of accuracy was also prevalent. The powerful analytical methodologies for reducing variables and the choice of optimal subsets could be advantageous in both basic research and the routine quality control of chilled beef.

Multiplexed analysis of cage and cage free chicken egg fatty acids using stable isotope labeling and mass spectrometry

Binary stable isotope labeling couple with LC-ESI-MS has been used as a powerful non-targeted approach for the relative quantification of lipids, amino acids, and many other important metabolite classes. A multiplexed approach using three or more isotopic labeling reagents greatly reduces analytical run-time while maintaining excellent sensitivity and reproducibility. Three isotopic cholamine labeling reagents have been developed to take advantage of the pre-ionized character of cholamine, for ESI, and the ease by which stable isotopes can be incorporated into the cholamine structure. These three cholamine labeling reagents have been used to relatively quantify three fatty acid samples simultaneously. The quantification resulted in the observation of 12 fatty acids that had an average absolute error of 0.9% and an average coefficient of variation of 6.1%. Caged versus cage-free isotope labeling experiments showed that cage-free eggs have an increased level of omega-3 fatty acids as compared to caged eggs. This multiplexed fatty acid analysis provides an inexpensive and expedited tool for broad-based lipid profiling that will further aid discoveries in the mechanisms of fatty acid action in cells.