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

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.

• Publication year

  2022

• Venue

  Foods

• Publication date

  2022-03-22

• Fields of study

  Medicine, Chemistry, Environmental Science

• Identifiers
  DOI 10.3390/foods11070910PMID 35406997PMCID 8997410
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

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