posted on 2021-03-04, 21:29authored byWenyao Zhu, Frank Benkwitz, Paul A. Kilmartin
The
analytical scope of static headspace–gas chromatography–ion
mobility spectrometry (SHS–GC–IMS) was applied to wine
aroma analysis for the first time. The method parameters were first
fine-tuned to achieve optimal analytical results, before the method
stability was demonstrated, in terms of repeatability and reproducibility.
Succinct qualitative identification of compounds was also realized,
with the identification of several volatiles that have seldom been
described previously in Sauvignon Blanc wine, such as methyl acetate,
ethyl formate, and amyl acetate. Using the SHS–GC–IMS
data in an untargeted approach, computer modeling of large datasets
was applied to link aroma chemistry via prediction models to wine
sensory quality gradings. Six machine learning models were compared,
and artificial neural network (ANN) returned the most promising performance
with a prediction accuracy of 95.4%. Despite its inherent complexity,
the ANN model offered intriguing insights on the influential volatiles
that correlated well with higher and lower sensory gradings. These
findings could, in the future, guide winemakers in establishing wine
quality, particularly during blending operations prior to bottling.