Investigating the Relation between Skin Cell Wall Composition and Phenolic Extractability in Cabernet Sauvignon Wines

In this study, phenolic extractability of Cabernet Sauvignon grapes from two California regions (Sonoma County and Central Coast) and its relation with skin cell wall composition was investigated. Phenolic grape composition, wine phenolic content as well as berry and pomace cell wall composition of three sites per region were determined. Grape cell wall material (CWM) composition, and thus pomace CWM composition, was impacted by the growing region. The process of fermentation modified CWM composition, solubilizing some of the compounds such as pectin and polysaccharides making pomace CWM composition from different sites more similar in the case of Sonoma County and more different for the samples grown in the Central Coast. Growing region had a significant impact on grape phenolics, particularly on flavan−3-ols and polymeric phenols, whereas polymeric pigments and anthocyanin contents were more similar among samples. Wines made from Sonoma County grapes showed higher anthocyanin and polymeric phenol content when compared to wines made from Central Coast grapes. Comparing wine to grape phenolic composition suggests a large difference in extractability based on region. Of all the CWM components analyzed, only lignin and the amount of cell wall isolated were found to have a significant impact on phenolic extractability.

Quantitative detection of soluble solids content, pH, and total phenol in Cabernet Sauvignon grapes based on near infrared spectroscopy

Determination of Cabernet Sauvignon grapes quality plays an important role in commercial processing. In this research, a rapid approach based on near infrared spectroscopy was proposed to the determination of soluble solids content (SSC), pH, and total phenol content (TPC) in entire bunches of Cabernet Sauvignon grapes. Standardized normal variate (SNV) and competitive adaptive weighted sampling (CARS), genetic algorithm (GA), and synergy interval partial least squares (si-PLS) were used to optimize the spectral data. With optimal combination input, the prediction accuracy of partial least squares regression (PLSR) and support vector regression (SVR) models was compared. The results showed that these models based on variable optimization method could predict well the SSC, pH, and TPC of Cabernet Sauvignon grapes. The correlation coefficient of prediction for SSC, pH, and TPC had reached more than 0.85. This work provides an alternative to analyze the chemical parameters in whole bunch of Cabernet Sauvignon grape.