Roger B Boulton | PubMed Health

PubMed2004

Chemical and sensory characterization of DOC red wines from Marche (Italy) related to vintage and grape cultivars.

Monomeric phenols, color and copigmentation parameters, pigments with different chemical structure, tannin, glucose, fructose, glycerol, ethanol, and organic acids were determined in DOC red wines from Marche (Italy), obtained during three different vintages ranging from 1996 to 2000. The intensity of the bitter and astringent tastes of the wines was determined with panel tastings. Lacrima di Morro and Vernaccia di Serrapetrona (obtained from local cultivars) were different from Rosso Piceno, Rosso Piceno Superiore, and Rosso Conero (produced from different percentages of Sangiovese and Montepulciano). Vernaccia, a red, sweet, "spumante" wine, was an outlier. Lacrima showed a low tannin content, a high content of small pigments and phenols, and a high ratio of copigmented color, which persisted after 3 years of aging. The chemical determinations accounted for a high percentage of variability of measured panel astringency, copigmented color, and measured wine absorbance at 520 nm. It was not possible to create a predictive model for bitterness.

Fruit↗

PubMed2003

Study of aged cognac using solid-phase microextraction and partial least-squares regression.

Headspace solid-phase microextraction (SPME) and GC-MS were used to analyze 17 commercial French Cognac brandies (9 young and 8 well-aged, ranging in age from 3 to 55 years). Sixty-four volatiles were chosen on the basis of chromatographic separation and/or known odor importance. Chromatographic peaks were manually integrated and the peak area data analyzed using partial least-squares (PLS) regression to study relationships between volatile composition (X variables) and age (Y variable). When only those compounds with the highest significance were included and from these selected the variables (a total of 33) with the highest correlation loadings on the first two principal components, principal component 1 explained 82% of the variance of the measured compounds and 85% of the variance in age. These were considered the most important volatiles to distinguish products of different ages because young and old samples were separated along principal component 1. Norisoprenoids, terpenes, and acetate esters had weaker positive and negative loadings and were therefore left out. The PLS model could predict sample age accurately with the optimum 33 volatiles as well as with a smaller subset consisting of ethyl esters and methyl ketones.

Alcoholic Beverages↗