Characterization of sulfur and nitrogen compounds in Brazilian petroleum derivatives using ionic liquid capillary columns in comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometric detection

Strategies for the identification and sensory evaluation of volatile constituents in wine

Wine aroma, which stems from complex perceptual and cognitive processes, is initially driven by a multitude of naturally occurring volatile constituents. Its interpretation depends on the characterization of relevant volatile constituents. With large numbers of volatile constituents already identified, the search for unknown volatiles in wine has become increasingly challenging. However, the opportunities to discover unknown volatile compounds contributing to the wine volatilome are still of great interest, as demonstrated by the recent identification of highly odorous trace (µg/L) to ultra-trace (ng/L) volatile compounds in wine. This review provides an overview of both existing strategies and future directions on identifying unknown volatile constituents in wine. Chemical identification, including sample extraction, fractionation, gas chromatography, olfactometry, and mass spectrometry, is comprehensively covered. In addition, this review also focuses on aspects related to sensory-guided wine selection, authentic reference standards, artifacts and interferences, and the evaluation of the sensory significance of discovered wine volatiles. Powerful key volatile odorants present at ultra-trace levels, for which these analytical approaches have been successfully applied, are discussed. Research areas where novel wine volatiles are likely to be identified are pointed out. The importance of perceptual interaction phenomena is emphasized. Finally, future avenues for the exploration of yet unknown wine volatiles by coupling analytical approaches and sensory evaluation are suggested.

Unique ion filter-A data reduction tool for chemometric analysis of raw comprehensive two-dimensional gas chromatography-mass spectrometry data

Comprehensive gas chromatography with time of flight mass spectrometry is a powerful tool in the analysis of complex samples. Chemometric analysis of raw chromatographic data is more useful in one- and two-dimensional separations relative to peak tables. The data volume from such experiments generally necessitates the use of data reduction tools. Such tools often sacrifice some of the multivariate information in the mass to charge ratio dimension. The unique ion filter reduces the over-redundancy in two-dimensional gas chromatography-mass spectrometry data by limiting the data to a few unique/pseudo-unique ions, sub-peaks/slices in the first dimension, and spectra in the second dimension. We explore the performance of this algorithm through careful inspection of two-dimensional gas chromatography-mass spectrometry data before and after application of the filter. A reduction (99%) in the number of variables in a two-dimensional gas chromatography-mass spectrometry chromatogram passed on to subsequent analysis was observed. Feature selection times for model optimization reduced from 229 (±13) to 6.8 (±0.5) min when the filter was applied. An estimate of two unique/pseudo-unique ions, one sub-peak in the first dimension and five spectra in the second dimension were considered to provide a true representation of each chromatogram and provided enough information to achieve 100% model prediction accuracy.