 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

Predictive Breeding: Impact of véraison (onset of ripening) on wine quality

Grapevine breeding focuses on high wine quality and climate-adapted grapevine varieties with fungal disease resistances to be cultivated in a pesticide-reduced and sustainable viticulture.

Adaptation to soil and climate through the choice of plant material

Choosing the rootstock, the scion variety and the training system best suited to the local soil and climate are the key elements for an economically sustainable production of wine. The choice of the rootstock/scion variety best adapted to the characteristics of the soil is essential but, by changing climatic conditions, ongoing climate change disrupts the fine-tuned local equilibrium. Higher temperatures induce shifts in developmental stages, with on the one hand increasing fears of spring frost damages and, on the other hand, ripening during the warmest periods in summer. Expected higher water demand and longer and more frequent drought events are also major concerns. The genetic control of the phenotypes, by genomic information but also by the epigenetic control of gene expression, offers a lot of opportunities for adapting the plant material to the future. For complex traits, genomic selection is also a promising method for predicting phenotypes. However, ecophysiological modelling is necessary to better anticipate the phenotypes in unexplored climatic conditions Genetic approaches applied on parameters of ecophysiological models rather than raw observed data are more than ever the basis for finding, or building, the ideal varieties of the future.

CHARACTERIZATION AND ANTIBACTERIAL ACTIVITY OF A POLYPHENOLIC EXTRACT OBTAINED BY GREEN SUPERCRITICAL CO₂ EXTRACTION FROM RED GRAPE POMACE

Upgrading wine industry solid wastes is considered as one of the main strategies to support the circular economy. Red grape pomaces constitute a rich source of polyphenols, which have been shown to possess antioxidant properties and to provide benefits for human and animal health. The objective of this work was to obtain and characterise polyphenolic extracts from red grape pomaces via green supercritical CO₂ extraction using ethanol as a co-solvent, and to evaluate their antibacterial activity against susceptible and multidrug-resistant Escherichia coli strains of animal intestinal origin.

Characterization of berry softening and sugar accumulation dynamics in a slow-ripening genotype and its response to abscisic acid treatments

In the current viticultural context, global warming leads to advanced and possibly accelerated ripening which can alter the balance among desirable grape quality traits sought for winemaking. Evaluation of genetic material that displays delayed and/or slower ripening could uncover a potential “slow ripening” trait for incorporation into commercial varieties through breeding. In this study, we evaluated a white-fruited selection discovered in the Grape Breeding and Genetics program at E. & J. Gallo Winery that displayed an unusual ripening pattern compared to standard varieties. Vines of the slow-ripening selection did not differ in their visual appearance, water status or gas exchange characteristics compared to vines of its normal-ripening sibling.

Which heat test can realistically estimate white wine haze risk?

Different heat tests are used to predict the dose of bentonite necessary to prevent wine haze after bottling. The most used tests are 60-120 min. at 80°C. Nevertheless, there is a lack of information about the relationship between these tests and the turbidities observed in the bottles after the storage/transport of the wines in realistic conditions, when temperatures reach 35-42°C during 3-12 days.