Abstract
A multivariate regression approach based on sensory data and chemical compositions has been applied to study the correlation between the sensory and chemical properties of Pinot Blanc wines from South Tyrol. The sensory properties were identified by descriptive analysis and the chemical profile was obtained by HS-SPME-GC/MS and HPLC. The profiles of the most influencing (positively or negatively) chemical components have been presented for each sensory descriptor. Partial Least Square Regression (PLS) and Principal Component Regression (PCR) models have been tested and applied. Visual (clarity, yellow colour), gustatory (sweetness, sourness, saltiness, bitterness, astringency, and warmness) and olfactory (overall intensity, floral, apple, pear, tropical fruit, dried fruit, fresh vegetative, spicy, cleanness, and off-odours) descriptors have been correlated with the volatile and phenolic profiles, respectively. Each olfactory descriptor was correlated via a PCR model to the volatile compounds, whereas a comprehensive PLS2 regression model was built for the correlation between visual/gustatory descriptors and the phenolic fingerprint. “Apple” was the olfactory descriptor best modelled by PCR, with an adjusted R2 of 0.72, with only 20% of the validation samples falling out of the confidence interval (α = 95%). A PLS2 with 6 factors was chosen as the best model for gustatory and visual descriptors related to the phenolic compounds. Finally, the overall quality judgment could be explained by a combination of the calibrated sensory descriptors through a PLS model. This allowed the identification of sensory descriptors such as “olfactory intensity”, “warmness”, “apple”, “saltiness”, “astringency”, “cleanness”, “clarity” and “pear”, which relevantly contributed to the overall quality of Pinot Blanc wines from South Tyrol, obtained with two different winemaking processes and aged in bottle for 18 months.
Highlights
The present study aims at correlating sensory descriptors and chemical compounds of two Pinot Blanc wines, by applying two alternative multivariate approaches, namely Partial Least Square Regression (PLS) and Principal Component Regression (PCR)
In the GC/MS dataset (ii), repeated PLS1 were performed on each sensory descriptor as the y-dependent variable and the aroma compounds as the X-matrix
The accurate assessment of the sensory quality is a prerequisite for identifying deviations from sensory requirements/specifications and for the application of the appropriate corrective actions [20]
Summary
The aromatic profile of wines can derive from grapes (varietal or primary aromas), production operations such as crushing, pressing, or maceration (secondary aromas), alcoholic and malolactic fermentations (fermentation aromas), and aging processes (maturation aromas) [3,4,5]. In addition to the chemical complexity deriving from the release of the compounds most readily extracted from grapes during crushing, maceration processes can induce the further release of aroma-impacting compounds. The maceration process aims at obtaining the maximum intensity of varietal aroma [6] through the extraction of terpenes, esters, aldehydes and alcohols. The maceration can increase the extraction of other molecules, such as phenolic compounds [7,8,9,10]
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