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Review
. 2022 Mar 22;11(7):910.
doi: 10.3390/foods11070910.

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

Maria Tufariello  1 , Sandra Pati  2 , Lorenzo Palombi  3 , Francesco Grieco  1 , Ilario Losito  4
Affiliations
Review

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

Maria Tufariello et al. Foods. .

Abstract

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.

Keywords: HS-SPME-GC-MS; artificial intelligence; multivariate statistical analysis; volatile compounds; wine.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Number of published articles between 1998 and 2022 related to HS-SPME/wine/volatile.
Figure 2
Figure 2
Schematic diagram of different objectives and multivariate statistical analysis techniques used for HS-SPME/GC-MS data.
See this image and copyright information in PMC

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