 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

Monitoring of Pesticide Residues from Vine to Wine

Those previous years, pesticides are often brought to the forefront by media. Questions arose about their toxicity for growers and consumers. Even if a downward trend is underway, the use of pesticides is required to ensure steady quality and quantity of harvests. A large number of active ingredients are authorized but regarding viticulture, mainly insecticides and fungicides are applied, to control pests and diseases and to increase crop yield. Some phytosanitary products, principally fungicides, applied close to the harvest date may frequently be detected in wines.

Physico-chemical properties of vine pruning residues with potential as enological additive

Grapes are one of the world’s primary fruit crops, and pruning activities generate high amounts of annual wood wastes [1]. These pruning shoots contain valuable phenolic compounds and could have numerous potential applications [1,2]. Consequently, the aim of this work was to evaluate the physico-chemical properties of vine pruning residues with potential as enological additives. For this purpose, grapevine shoots from 12 varieties grown in Chile were collected during the winter of 2021.

IDENTIFYING POTENTIAL CHEMICAL MARKERS RESPONSIBLE FOR THE PERMISSIVENESS OF BORDEAUX RED WINES AGAINST BRETTANOMYCES BRUXELLENSIS USING UNTARGETED METABOLOMICS

All along the red winemaking process, many microorganisms develop in wine, some being beneficial and essential, others being feared spoilers. One of the most feared microbial enemy of wine all around the world is Brettanomyces bruxellensis. Indeed, in red wines, this yeast produces volatile phenols, molecules associated with a flavor described as “horse sweat”, “burnt plastic” or “leather”. To produce significant and detectable concentrations of these undesired molecules, the yeasts should first grow and become numerous enough. Even if the genetic group of the strain present and the cellar temperature may modulate the yeast growth rate¹ and thus the risk of spoilage, the main factor seems to be the wines themselves, some being much more permissive to B. bruxellensis development than others.

Sensory differences of Pinot noir wines from willamette valley subregions

Wines from different regions or AVAs have been found to have sensory differences, as these areas are typically located quite far apart and have dramatically different climates, soils and other terroir factors.

Vineyard altitude as a climate change adaptation strategy and its effect on Riesling during grapes and wine composition during ripening

Climate is one of the main drivers of spatial and temporal variability in grapevine physiology and therefore a key determinant of grape composition and final wine value. The world has warmed 1.1 °C since pre-industrial times, and the latest IPCC report indicates an additional 0.5 to 1.3 °C of warming by mid-century with continental locations warming at a greater rate than the oceans.