Analysis of volatiles in Pinotage wines by stir bar sorptive extraction and chemometric profiling

Use of Multivariate Statistics in the Processing of Data on Wine Volatile Compounds Obtained by HS-SPME-GC-MS

This review takes a snapshot of the main multivariate statistical techniques and methods used to process data on the concentrations of wine volatile molecules extracted by means of solid phase micro-extraction and analyzed using GC-MS. Hypothesis test, exploratory analysis, regression models, and unsupervised and supervised pattern recognition methods are illustrated and discussed. Several applications in the wine volatolomic sector are described to highlight different interactions among the various matrix components and volatiles. In addition, the use of Artificial Intelligence-based methods is discussed as an innovative class of methods for validating wine varietal authenticity and geographical traceability.

Analysis of phenolic and cyclic compounds in plants using derivatization techniques in combination with GC-MS-based metabolite profiling

Metabolite profiling has been established as a modern technology platform for the description of complex chemical matrices and compound identification in biological samples. Gas chromatography coupled with mass spectrometry (GC-MS) in particular is a fast and accurate method widely applied in diagnostics, functional genomics and for screening purposes. Following solvent extraction and derivatization, hundreds of metabolites from different chemical groups can be characterized in one analytical run. Besides sugars, acids, and polyols, diverse phenolic and other cyclic metabolites can be efficiently detected by metabolite profiling. The review describes own results from plant research to exemplify the applicability of GC-MS profiling and concurrent detection and identification of phenolics and other cyclic structures.

Chemometric classification of casework arson samples based on gasoline content

Detection and identification of ignitable liquids (ILs) in arson debris is a critical part of arson investigations. The challenge of this task is due to the complex and unpredictable chemical nature of arson debris, which also contains pyrolysis products from the fire. ILs, most commonly gasoline, are complex chemical mixtures containing hundreds of compounds that will be consumed or otherwise weathered by the fire to varying extents depending on factors such as temperature, air flow, the surface on which IL was placed, etc. While methods such as ASTM E-1618 are effective, data interpretation can be a costly bottleneck in the analytical process for some laboratories. In this study, we address this issue through the application of chemometric tools. Prior to the application of chemometric tools such as PLS-DA and SIMCA, issues of chromatographic alignment and variable selection need to be addressed. Here we use an alignment strategy based on a ladder consisting of perdeuterated n-alkanes. Variable selection and model optimization was automated using a hybrid backward elimination (BE) and forward selection (FS) approach guided by the cluster resolution (CR) metric. In this work, we demonstrate the automated construction, optimization, and application of chemometric tools to casework arson data. The resulting PLS-DA and SIMCA classification models, trained with 165 training set samples, have provided classification of 55 validation set samples based on gasoline content with 100% specificity and sensitivity.

Investigating the coffee flavour in South African Pinotage wine using novel offline olfactometry and comprehensive gas chromatography with time of flight mass spectrometry

Pinotage wine from several South African wine cellars has been produced with a novel coffee flavour. We have investigated this innovative coffee effect using in house developed solventless sampling and fractionating olfactometric techniques, which are unique in their ability to study synergistic aroma effects as opposed to traditional gas chromatography olfactometry (GC-O) which is designed to, ideally, evaluate single eluting compounds in a chromatographic sequence. Sections of the chromatogram, multiple or single peaks, were recaptured on multichannel open tubular silicone rubber (polydimethylsiloxane (PDMS)) traps at the end of a GC column. The recaptured fractions were released in a controlled manner for offline olfactory evaluation, and for qualitative analysis using comprehensive gas chromatography coupled to time of flight mass spectrometry (GC×GC-TOFMS) for compound separation and identification, thus permitting correlation of odour with specific compounds. A combination of furfural and 2-furanmethanol was responsible for a roast coffee bean-like odour in coffee style Pinotage wines. This coffee perception is the result of a synergistic effect in which no individual compound was responsible for the characteristic aroma.

Effect of oak extract application to Verdejo grapevines on grape and wine aroma

Volatile compounds from a commercial aqueous oak extract application to white Verdejo grapevines at veraison have been studied. Treated grapes under two types of formulation (25% and 100%) have been analyzed at the optimum maturation time, and winemaking was then subsequently carried out. The volatile compounds were analyzed by stir bar sorptive extraction-gas chromatography-mass spectrometry. The results suggest that after the grapevine treatments, grapes store the volatiles in the form of nonvolatile precursors, and some of the volatiles are released during the winemaking process, especially six months after the alcoholic fermentation. The sensory analysis shows that wines maintain the typical aroma properties of Verdejo wines at the end of fermentation; but after six months, the wine color is greener and more astringent, and, in terms of aroma, it has wooden notes as if the wine has been aged in oak barrels.