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

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dc.citation.issue20
dc.citation.volume26
dc.contributor.authorPoggesi S
dc.contributor.authorDupas de Matos A
dc.contributor.authorLongo E
dc.contributor.authorChiotti D
dc.contributor.authorPedri U
dc.contributor.authorEisenstecken D
dc.contributor.authorRobatscher P
dc.contributor.authorBoselli E
dc.date.available2021-10
dc.date.available2021-10-13
dc.date.issued2021-10-15
dc.description.abstractA 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.
dc.description.publication-statusPublished
dc.identifierhttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000714937400001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=c5bb3b2499afac691c2e3c1a83ef6fef
dc.identifierARTN 6245
dc.identifier.citationMOLECULES, 2021, 26 (20)
dc.identifier.doi10.3390/molecules26206245
dc.identifier.eissn1420-3049
dc.identifier.elements-id449213
dc.identifier.harvestedMassey_Dark
dc.publisherMDPI (Basel, Switzerland)
dc.relation.isPartOfMOLECULES
dc.rightsCC BY
dc.subjectpinot blanc
dc.subjectsensory analysis
dc.subjectvolatile profile
dc.subjectphenolic profile
dc.subjectmultivariate regression
dc.subjectwinemaking
dc.subject.anzsrc0304 Medicinal and Biomolecular Chemistry
dc.subject.anzsrc0305 Organic Chemistry
dc.subject.anzsrc0307 Theoretical and Computational Chemistry
dc.titleChemosensory Profile of South Tyrolean Pinot Blanc Wines: A Multivariate Regression Approach
dc.typeJournal article
pubs.notesNot known
pubs.organisational-group/Massey University
pubs.organisational-group/Massey University/College of Sciences
pubs.organisational-group/Massey University/College of Sciences/School of Food and Advanced Technology
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