Journal
FOODS
Volume 11, Issue 7, Pages -Publisher
MDPI
DOI: 10.3390/foods11070910
Keywords
wine; HS-SPME-GC-MS; volatile compounds; multivariate statistical analysis; artificial intelligence
Categories
Funding
- Apulia Region project: Innovazione nella tradizione: tecnologie innovative per esaltare le qualita dei vini autoctoni spumante della murgia barese-INVISPUBA, Birra: dal campo al boccale-BE2R
- Apulia Region project: Spumantizzazione e frizzantatura per il rilancio della vitivinicoltura dell'areale Centro Nord della regione Puglia-SPUMAPULIA (P:S:R. Puglia) [Misura 16.2]
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This review provides a snapshot of the main multivariate statistical techniques and methods used to process data on wine volatile molecule concentrations. It also discusses their applications and advancements in related fields.
This review takes a snapshot of the main multivariate statistical techniques and methods used to process data on the concentrations of wine volatile molecules extracted by means of solid phase micro-extraction and analyzed using GC-MS. Hypothesis test, exploratory analysis, regression models, and unsupervised and supervised pattern recognition methods are illustrated and discussed. Several applications in the wine volatolomic sector are described to highlight different interactions among the various matrix components and volatiles. In addition, the use of Artificial Intelligence-based methods is discussed as an innovative class of methods for validating wine varietal authenticity and geographical traceability.
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