Headspace solid-phase microextraction–gas chromatography–mass spectrometry for profiling free volatile compounds in Cabernet Sauvignon grapes and wines

The complex aroma of wine is derived from many sources, with grape-derived components being responsible for the varietal character. The ability to monitor grape aroma compounds would allow for better understanding of how vineyard practices and winemaking processes influence the final volatile composition of the wine. Here, we describe a procedure using GC–MS combined with headspace solid-phase microextraction (HS-SPME) for profiling the free volatile compounds in Cabernet Sauvignon grapes. Different sample preparation (SPME fiber type, extraction time, extraction temperature and dilution solvent) and GC–MS conditions were evaluated to optimize the method. For the final method, grape skins were homogenized with water and 8 ml of sample were placed in a 20 ml headspace vial with addition of NaCl; a polydimethylsiloxane SPME fiber was used for extraction at 40 °C for 30 min with continuous stirring. Using this method, 27 flavor compounds were monitored and used to profile the free volatile components in Cabernet Sauvignon grapes at different maturity levels. Ten compounds from the grapes, including 2-phenylethanol and β-damascenone, were also identified in the corresponding wines. Using this procedure it is possible to follow selected volatiles through the winemaking process.     

A new automatic phase mask filter for high-resolution brain venography at 3 T: theoretical background and experimental validation

To improve vessel contrast in high-resolution susceptibility-based brain venography, an automatic phase contrast enhancing procedure is proposed, based on a new phase mask filter suitable for maximizing contrast of venous MR signals. The effectiveness of the new approach was assessed both on digital phantoms and on acquired MR human brain images, and then compared with venographic results of phase masking methods in recent literature. The digital phantom consisted of a simulated MR dataset with given signal-to-noise ratios (SNRs), while real human data were collected by scanning healthy volunteers with a 3.0-T MR system and a 3D gradient echo pulse sequence. The new phase mask (NM) was more effective than the conventional mask (CM) both on the digital phantoms and on the acquired MR images. A quantitative comparison based on phantom venograms indicates how this phase enhancement can lead to a significant increase in the contrast-to-noise ratio (CNR) for all considered phase values as well as for all vessel sizes of clinical interest. Likewise, the in vivo brain venograms reveal a better depiction of the smallest venous vessels and the enhancement of many details undetectable in conventional venograms.     

Random effects clustering in multilevel modeling: choosing a proper partition

A novel criterion for estimating a latent partition of the observed groups based on the output of a hierarchical model is presented. It is based on a loss function combining the Gini income inequality ratio and the predictability index of Goodman and Kruskal in order to achieve maximum heterogeneity of random effects across groups and maximum homogeneity of predicted probabilities inside estimated clusters. The index is compared with alternative approaches in a simulation study and applied in a case study concerning the role of hospital level variables in deciding for a cesarean section.

     

Interactive visualization in multiclass learning: integrating the SASSC algorithm with KLIMT

The paper deals with multiclass learning from the perspective of analytically interpreting the results of the analysis as well as that of navigating into them by using interactive visualization tools. It is showed that by combining the Sequential Automatic Search of Subset of Classifiers (SASSC) algorithm with the interactive visualization of classification trees provided by the Klassification—Interactive Methods for Trees (KLIMT) software it is possible to highlight important information deriving from the knowledge extraction process without neglecting the prediction accuracy of the classification method. Empirical evidence from two benchmark datasets demonstrates the advantages deriving from the joint use of SASSC and KLIMT.