Chemosensory Profile of South Tyrolean Pinot Blanc Wines: A Multivariate Regression Approach

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.

Fusion of 2DGC-MS, HPLC-MS and Sensory Data to Assist Decision-Making in the Marketing of International Monovarietal Chardonnay and Sauvignon Blanc Wines

“…The x.127 specie at 10.7 min was characterised by fragment ions m / z 285, m / z 177, and m / z 125; the loss of a water molecule (18 Da) produced the fragment ion m / z 285, whereas m / z 177, and m / z 125 were reported to be the fragment ions generated from the cleavage of the C ring of a dihydroflavonol (i.e., a flavanonol). As reported by Shen et al and in previous papers by our reserch group [ 6 , 29 , 54 ], this compound is probably identified with taxifolin.…”

“…The fiber was preconditioned at 240 °C for 6 min before the analysis. The gas chromatographic method was adapted from previously published reports [ 6 , 29 ]. The separation was performed by comprehensive GC × GC on an instrument coupled with a Pegasus BT 4D time-of-flight mass spectrometer and equipped with a Flux TM flow modulator (Leco Italy, Milano).…”

“…Partial Least Square Regression (PLS-R) has been used to correlate the descriptor variables with the overall quality judgment to understand which descriptors most affected the quality of the product [ 6 ]. The Q 2 cumulative index represents the goodness of fit in the prediction quality of the model and was calculated using the following (Formula (1)) where “h” is the number of components used in the regression model, “ j ” is the observation and involves the predicted residual error sum of square (PRESS) statistics and the Sum of Squares Errors (SSE).…”

“…Multiple interacting factors make a complete standardization of the approaches to define sensory quality quite difficult. Recent attempts have included the integration of sensory and chemical profiles, with the aim of explaining trends in sensory evaluation based on wine composition, applying chemometric approaches [ 6 ]. Of course, a limitation in the samples available for such a study could pose an issue in terms of the representativeness of the overall significance of the results.…”

Use of Multivariate Statistics in the Processing of Data on Wine Volatile Compounds Obtained by HS-SPME-GC-MS

“…Vilanova et al [ 51 ] investigated the correlation between the volatile composition and sensory properties in Spanish Albariño wines, through PCA. Following this, the multivariate regression approach based on the use of PLS and PCA was used by Poggesi et al [ 52 ] to study the correlation between sensory data and volatile compounds, in Pinot Blanc, in order to use chemical fingerprints to obtain a prediction of the sensory profile of the wine. PCA analysis was also used to evaluate the impact of different yeast strains on the wine quality and on the progress of the fermentation process.…”