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Assessment of Sauvignon Blanc Aroma and Quality Gradings Based on Static Headspace-Gas Chromatography-Ion Mobility Spectrometry (SHS-GC-IMS): Merging Analytical Chemistry with Machine Learning

preprint
revised on 14.12.2020, 22:49 and posted on 16.12.2020, 11:28 by Wenyao Zhu, Frank Benkwitz, Paul Kilmartin
In this paper, we report on the application of the static headspace-gas chromatography-ion mobility spectrometry (SHS-GC-IMS) instrument in the field of wine aroma analysis and its potential in constructing a prediction model for the quality gradings of wines. The easy-to-operate, cost effective SHS-GC-IMS instrument was innovatively used for a non-targeted search for volatile compounds in Sauvignon Blanc wine, with the identification of volatiles seldom before reported. The wine aroma profile acquired by the instrument was organically and innovatively combined with advanced classification models, inspired by the computer science community, to produce high classification accuracy in terms of wine quality gradings. Useful insights were also extracted by using advanced interpretation methods on complex models to learn the important volatiles correlated with wine quality grading.

Funding

Callaghan Innovation CONB1801

History

Email Address of Submitting Author

wzhu221@aucklanduni.ac.nz

Institution

The University of Auckland

Country

New Zealand

ORCID For Submitting Author

0000-0001-7302-5178

Declaration of Conflict of Interest

The authors declare no conflict in competing financial interest

Version Notes

15/12/2020: Revised the manuscript and supporting material in accordance with JAFC requirements; previous version still made available.

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