One-hundred and eighty-nine commercial red wines from four vintages (1996-1999), four varieties (Pinot noir, Merlot, Cabernet franc, Cabernet Sauvignon) and 13 vineyard locations within the Okanagan Valley of British Columbia were analysed for total phenolics, anthocyanins, flavonols, tartaric esters, free SO2, pH and titratable acidity, as well as copigmented-, monomeric-, polymeric- and total- anthocyanins (absorbance values). Color was evaluated using color density, hue, Hunter-color (L, a, b) and chroma values. Statistics (means, standard deviations) and discriminant analysis were used to explore the response patterns in the compositional analyses among the vintages, varieties and vineyard locations. Color density was highly correlated to the monomeric- and polymeric- anthocyanins for all varieties. Discriminant analysis revealed that some wine vintages could be differentiated using the flavonols, anthocyanins, copigmented anthocyanins, hue and L values. Phenolic concentrations were lower in 1996 and 1997 vintages compared to 1998 and 1999. Discriminant analysis showed that the varieties Pinot noir, Cabernet franc and Merlot/Cabernet Sauvignon could be differentiated using the monomeric-, polymeric- and total- anthocyanins, as well as color density, hue and L values. Cabernet Sauvignon wines formed a subset within the Merlot grouping. Discriminant of wines from the vineyard locations revealed that there was a considerable overlap among the regions, but that the groupings were generally consistent with geographic location. Sensory analysis was used to determine the intensity of astringency and astringent aftertaste in a subset of 35 wines from 1998. Multiple linear regression was used to relate the sensory and compositional analyses. A two-variable model predicted astringency (R=0.77) from total phenolics and copigmented anthocyanins; whereas, a one-variable model was developed to predict astringent aftertaste (R=0.74) from total phenolics. Sensory data collected on an additional 25 red wines were used to validate the appropriateness of the models.
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