Biomedical subjects

Margarita Aznar

Publications and source records attributed to Margarita Aznar.

5 recordsLinked to original sources

PubMed2003

Fast fractionation of complex organic extracts by normal-phase chromatography on a solid-phase extraction polymeric sorbent. Optimization of a method to fractionate wine flavor extracts.

Some properties of LiChrolut-EN resins as normal-phase sorbent have been studied. Retention factors using pentane as solvent range from less than 2 (ethyl esters) to more than 56 (fatty acids and vanillin). All retention factors were smaller than 2 with dichloromethane. The efficiency of the bed was between 4 and 13 plates per cm. A method for the fast fractionation of wine flavor extracts has been further developed. Wine (75 ml) is extracted on a 0.5 g LiChrolut-EN bed. Volatile compounds are recovered in 5 ml of dichloromethane and the extract is further concentrated to 0.1 ml. Recoveries of the extraction procedure are above 85% for all compounds less polar than isoamyl alcohol. This extract is fractionated on a bed (5.0 cm height, 0.6 cm internal diameter) packed with 0.55 g of LiChrolut-EN resins. A first fraction is collected by the elution with 4 ml of pentane. A second one with 6 ml of a mixture pentane/dichloromethane (9:1) and a final fraction with 4 ml of dichloromethane. The first fraction is enriched in ethyl esters and some other non-polar compounds. The second fraction concentrates the alcohols and some volatile phenols, while the third is enriched in fatty acids, vanillin derivatives and some lactones. The recovery in the fractionation is complete. The profile obtained in the fractionation is very stable, and becomes distorted only when the column is loaded with an extract containing 80 mg of major volatiles (coming from more than 150 ml of wine). The fractionation of extracts from different wines showed that the performance of the process does not depend on the composition of the extract. Twenty-seven out of 32 studied compounds eluted reproducibly mainly in one fraction. The results suggest that the method can be applied as an aid for qualitative or quantitative analysis to any kind of organic extract as an alternative to liquid chromatography on silica-gel.

Chromatography, Liquid↗

PubMed2003

Quantitative gas chromatography-olfactometry. Analytical characteristics of a panel of judges using a simple quantitative scale as gas chromatography detector.

Some questions related to the ability of gas chromatography-olfactometry (GC-O) to provide quantitative measurements of the concentration of a given odorant in an extract are explored and discussed. A panel of eight individuals has been used to evaluate the intensity of 15 odorants present at different concentrations. The use of very simple scales, such as a 0-3 scale, makes it possible to build calibration graphs based on the different stimulus-response models (Fechner, Stevens, Hill) and, with a proper calibration, up to nine different concentration levels can be discriminated by an eight-judge panel. The signal shows a good long-term stability, and its precision varies between 3.7 and 8% of the whole scale, with 5.7% as average. Sensitivity is extremely dependent on the compound: in the best case a concentration change of 20% can be detected, while in the worst, concentrations must differ more than one order of magnitude. In average, concentrations must differ between 2 and 4.7 times (including calibration error) or between 1.2 and 2.3 (excluding calibration error). The performance of the different judges, the effect of the close elution of two odors and the benefits derived from the use of more complex scales (7-points) are briefly discussed from the perspective of the analytical performance of GC-O methods.

Calibration↗

PubMed2003

Prediction of aged red wine aroma properties from aroma chemical composition. Partial least squares regression models.

Partial least squares regression (PLSR) models able to predict some of the wine aroma nuances from its chemical composition have been developed. The aromatic sensory characteristics of 57 Spanish aged red wines were determined by 51 experts from the wine industry. The individual descriptions given by the experts were recorded, and the frequency with which a sensory term was used to define a given wine was taken as a measurement of its intensity. The aromatic chemical composition of the wines was determined by already published gas chromatography (GC)-flame ionization detector and GC-mass spectrometry methods. In the whole, 69 odorants were analyzed. Both matrixes, the sensory and chemical data, were simplified by grouping and rearranging correlated sensory terms or chemical compounds and by the exclusion of secondary aroma terms or of weak aroma chemicals. Finally, models were developed for 18 sensory terms and 27 chemicals or groups of chemicals. Satisfactory models, explaining more than 45% of the original variance, could be found for nine of the most important sensory terms (wood-vanillin-cinnamon, animal-leather-phenolic, toasted-coffee, old wood-reduction, vegetal-pepper, raisin-flowery, sweet-candy-cacao, fruity, and berry fruit). For this set of terms, the correlation coefficients between the measured and predicted Y (determined by cross-validation) ranged from 0.62 to 0.81. Models confirmed the existence of complex multivariate relationships between chemicals and odors. In general, pleasant descriptors were positively correlated to chemicals with pleasant aroma, such as vanillin, beta damascenone, or (E)-beta-methyl-gamma-octalactone, and negatively correlated to compounds showing less favorable odor properties, such as 4-ethyl and vinyl phenols, 3-(methylthio)-1-propanol, or phenylacetaldehyde.

Chromatography, Gas↗

PubMed2002

Determination of minor and trace volatile compounds in wine by solid-phase extraction and gas chromatography with mass spectrometric detection.

A new method for the quantitative determination of important wine odorants has been developed. The wine (50 ml) is extracted in a 200 mg solid-phase extraction (SPE) cartridge filled with Lichrolut-EN resins from Merck. The elution is carried out with 1.3 ml of dichloromethane. These extracts are directly analyzed by GC-Ion Trap-MS without further concentration. Twenty-seven important wine odorants, such as volatile phenols, vanillin derivatives, aliphatic lactones, nor-isoprenoids, minor esters and terpenols, can be quantitatively determined in a single gas chromatography-mass spectrometry (GC-MS) run. The recoveries in the SPE isolation are in good agreement with those expected from the calculation of breakthrough volumes from solid-liquid distribution coefficients and are higher than 90%, except for guaiacol, vanillin, 2,6-dimethoxyphenol and 4-vinylphenol. In most cases, precision is below 10%. Method linearity is satisfactory, with r2 higher than 0.99 in all cases. The analysis of spiked samples has shown that there is good agreement between the real mass of compound added to the wine and that determined by analysis. In all cases detection limits are below the odor detection threshold of the compounds, and the calibrated interval covers the natural range of occurrence of the compounds in wine.

Gas Chromatography-Mass Spectrometry↗

PubMed2002

Aroma extract dilution analysis. Precision and optimal experimental design.

The odor thresholds of 12 different compounds have been determined in an AEDA experiment using a panel composed of 8 individuals. Only in one case is the distribution of thresholds among judges significantly different from the log-normal. The cause of that departure from normality seems to be a cross adaptation rather than anosmia. The standard deviations (SD) range from 2(0.7) to 2(4.1), with 2(1.8) as average. If the AEDA is carried out at a dilution rate, R, and dilution R(P) (where P = 0, 1, 2.) is the last one at which a compound was detected by a judge, the flavor dilution (FD) factor that should be provided for that judge is R(P+0.5). In the case where several judges carry out the AEDA, the FD for a given compound should be the geometric mean of the FD provided by all the judges. The SD of the distribution of FDs is related to that of the distribution of odor thresholds, but is strongly influenced by the dilution rate, R, being higher with higher R values. The relationship between both SDs can be used to determine the expected precision (measured as the geometric length of the 95% confidence interval) of the result of an AEDA experiment as a function of the dilution rate, the number of judges, and the SD of the distribution of thresholds. Different simulations have shown that in most cases, a dilution rate of 10 is the best option, and that lower dilution rates are advantageous only if the analyzed solution contains compounds with a very narrow distribution of thresholds.

Chromatography, Gas↗