Macrowine 2021
IVES 9 IVES Conference Series 9 Prediction of sauvignon blanc quality gradings with static headspace−gas chromatography−ion mobility spectrometry (SHS−GC−IMS) and machine learning

Prediction of sauvignon blanc quality gradings with static headspace−gas chromatography−ion mobility spectrometry (SHS−GC−IMS) and machine learning

Abstract

AIM: The main goal of the current study is the development of a cost-effective and easy-to-use method suitable for use in the laboratory of commercial wineries to analyze wine aroma. Additionally, this study attempted to establish a prediction model for wine quality gradings based on their aroma, which could reveal the important aroma compounds that correlate well with different grades of perceived quality

METHODS: Parameters of the SHS−GC−IMS instrument were first optimized to acquire the most desirable chromatographic resolution and signal intensities. Method stability was then exhibited by repeatability and reproducibility. Subsequently, compound identification was conducted. After method development, a total of 143 end-ferment wine samples of three different quality gradings from vintage 2020 were analyzed with the SHS−GC−IMS instrument. Six machine learning methods were employed to process the results and construct a quality prediction model. Techniques that aim to explain the model to extract useful insights were also applied.

RESULTS: The SHS−GC−IMS method was able to detect 23 compounds among 65 peaks, mostly esters and higher alcohols, using the current instrumentation. Several identified compounds, including methyl acetate, ethyl formate, and amyl acetate, have seldomly been reported in Sauvignon Blanc wines before. The method also indicated decent repeatability and reproducibility, both of which were below 10%. The quality prediction model was successfully established using artificial neural network (ANN) based on all peaks regardless of their identity. The model returned a highly satisfactory prediction accuracy of 95.4% using 10-fold cross-validation. SHapley Additive exPlanations (SHAP) values was used to delineate the prediction mechanism of the model. SHAP values revealed that isoamyl acetate, ethyl decanoate, ethyl octanoate and 1-hexanol were positively linked to better quality, whereas hexyl acetate, isoamyl alcohol, and 1-butanol could lower the quality grading.

CONCLUSIONS:

This study has successfully developed a method alternative to GC−MS based instruments for the non-targeted screening of wine volatile compounds. With its simple design featuring a headspace sampling unit, highly simplified sample preparation, and nitrogen being the only gas supply, the instrument has shown outstanding practicality desired by commercial winery laboratories. The powerful prediction model and the insights extracted by SHAP values could serve as a starting point for winemakers to investigate the effects of winemaking operations on the expression of the volatiles shown to correlate with higher gradings, to enhance the quality of wines. The findings of this study have been published as an original research article in the Journal of Agricultural and Food Chemistry: J. Agric. Food Chem. 2021, 69(10), 3255−3265.

DOI:

Publication date: September 22, 2021

Issue: Macrowine 2021

Type: Article

Authors

Wenyao Zhu , Frank BENKWITZ, Paul A. KILMARTIN,

School of Chemical Sciences, University of Auckland, Private Bag 92019, Auckland, New Zealand; Drylands Winery, Constellation Brands NZ, Blenheim 7273, New Zealand.

Contact the author

Keywords

Sauvignon blanc, static headspace−gas chromatography−ion mobility spectrometry (SHS−GC−IMS), quality grading, machine learning, artificial neural network (ANN), model explanation

Citation

Related articles…

Les sols du cru de Bonnezeaux, Thouarcé, Anjou, France

Le cru de Bonnezeaux est une des appellations prestigieuses des vins liquoreux et moelleux des Coteaux du Layon et sa réputation est ancienne. L’INAO a effectué sa délimitation en 1953. Le vignoble est situé au nord de la ville de Thouarcé et au sud du village de Bonnezeaux, le long du versant rive droite du Layon, exposé au sud-ouest. La superficie du vignoble est de 156 ha.

Optimized grape seed protein extraction for wine fining

The extraction of proteins from grape seeds represents a promising strategy to revalorize wine industry by-products. As a natural endogenous fining agent, grape seed protein (GSE) offers an effective solution for wine clarification [1] without requiring label declaration.

Electrochemical approaches in wine analysis 

There is a high demand in the wine industry for analytical methods able to provide useful information to support the decision-making process in the vineyard and in the winery. Ideally these methods should be rapid (e.g.

Projections of vine phenology and grape composition of Tempranillo variety In Rioja DOCa (Spain) under climate change

Aims: Some of the most direct effects of climate variability on grapevines are the changes in the onset and timing of phenological events and in the length of the growing season, which may affect grape quality. The aim of this research was to analyze the projected changes in vine phenology and on grape composition of the Tempranillo variety in Rioja DOCa under different climate change scenarios.

Long-term sensorial and compositional effects of copper fining on the wine containing ‘reductive’ and ‘tropical’ volatile sulfur compounds

The aim of this study was to investigate long-term sensorial and compositional effects of copper addition to the white wine naturally high in varietal thiol levels, with added volatile sulfur compounds