 IVES 9 IVES Conference Series 9 Fluorescence spectroscopy with xgboost discriminant analysis for intraregional wine authentication

Fluorescence spectroscopy with xgboost discriminant analysis for intraregional wine authentication

Abstract

AIM: This study aimed to use simultaneous measurements of absorbance, transmittance, and fluorescence excitation-emission matrix (A-TEEM) combined with chemometrics as a rapid method to authenticate wines from three vintages within a single geographical indication (GI) according to their subregional variations.

METHODS: The A-TEEM technique (Gilmore, Akaji, & Csatorday, 2017) has been applied to analyse experimental Shiraz wines (n = 186) from six subregions of Barossa Valley, South Australia, from 2018, 2019 and 2020 vintages. Absorbance spectra and EEM fingerprints of the wines were recorded and the data were fused for multivariate statistical modelling with extreme gradient boost discriminant analysis (XGBDA) as reported by Ranaweera, Gilmore, Capone, Bastian, and Jeffery (2021) to classify wine according to their subregions. The cross-validated (k =10, Venetian blinds) confusion matrix score probabilities of classes were used to assess the accuracy of the classification models. A similar procedure was also carried out to discriminate subregions for a single vintage year. Basic chemical parameters (alcohol %v/v, pH, titratable acidity, and volatile acidity) were modelled with the partial least squares regression (PLSR) using A-TEEM data and reference chemical data.

RESULTS: Results have shown an unprecedented 100% correct classification of wines according to subregion across the three vintages and 98% accuracy for subregion in a single vintage year. Other model performance parameters of confusion matrix, including sensitivity, specificity, precision, and F1 score, were also showing the highest values (1.0) for each of the subregions. PLSR modelling revealed that A-TEEM data can also be used for a rapid assessment of basic wine chemical parameters. Notably, the results confirmed a distinct resolution among subregions despite their relatively close proximity within a single GI, indicating the effect of terroir on intraregional variation.

CONCLUSIONS

The sensitivity of A-TEEM allied with multivariate statistical analysis of fluorescence data facilitated the accurate classification of Shiraz wines according to the subregion of origin and production year. As a robust analytical method, A-TEEM can help identify the drivers of regional expression of wine and can potentially be developed for use within the supply chain to guarantee the provenance indicated on the label and to provide an assurance of quality. Overall, A-TEEM with XGBDA modelling continues to be shown as an accurate wine authentication tool that could even be applied at a subregional level.

DOI:

Publication date: September 7, 2021

Issue: Macrowine 2021

Type: Article

Authors

Ruchira Ranaweera

Department of Wine Science, The University of Adelaide, South Australia, Australia,Adam GILMORE, Horiba Instruments Inc., Piscataway, New Jersey, USA Dimitra CAPONE, The Australian Research Council Training Centre for Innovative Wine Production, The University of Adelaide Susan BASTIAN, The Australian Research Council Training Centre for Innovative Wine Production, The University of Adelaide David JEFFERY, The Australian Research Council Training Centre for Innovative Wine Production, The University of Adelaide

Contact the author

Keywords

geographical indication, authenticity, subregion, excitation-emission matrix, chemometrics, terroir

Citation

Observatoire Grenache en Vallée du Rhône: incidence du terroir sur certains précurseurs d’arômes et substances volatiles

As observed in other grape varieties, Red Grenache juice contains low level of volatiles. The main flavor compounds are ” Iock up “as flavorless glycoconjugates which could generate at the wine pH volatile flavorants and constitute the varietal aroma of this cultivar.

L’étude “terroirs d’Anjou”: un exemple de caractérisation intégrée des terroirs viticoles, utilisable à l’échelle parcellaire

Natural factors of the production (“terroir” and vintage) are known as an important element for identifying wines by their genuine typicité and their authenticity. The program “Terroirs d’Anjou” (1994-1999) aims at bringing the necessary scientific basis for a rational and reasoned exploitation of the terroir.

Eléments importants d’une méthodologie de caractérisation des facteurs naturels du terroir, en relation avec la réponse de la vigne à travers le vin

The French viticultural appellation areas are the result of an empirical, historical and evolutionary selection which, generally, has consecrated a match between natural factors, grape varieties and viti-vinicultural practices. The notion of terroir is the main basis of the Appellation d’Origine Contrôlée in viticulture. It is based on the one hand on privileged natural factors and on the other hand on the know-how of the winegrowers; the whole allowing the production of a wine endowed with an authenticity and a sensory typicity. Wine-growing practices evolve according to progress in viticulture and oenology, while the natural factors of the terroir are much more stable, with the exception of the vintage. They therefore represent a fundamental pillar of the identity of an appellation vineyard.

Wine industry, digital transformation, and sustainability: a systematic literature

This paper aims to (i) identify the state of the art regarding digital transformation in the transition to sustainability in the wine industry, (ii) analyze the adoption of digital technologies at different stages of the winemaking process and their contribution to the triple bottom line of sustainability, and (iii) present a research agenda that facilitates the development of the field, providing contributions to both literature and managerial practice.

Where the sky is no limit — The transformation of wine marketing through text-to-video generation AI model

The introduction of ai-driven tools in digital content creation represents a significant shift in the landscape of marketing, particularly for industries reliant on rich visual storytelling such as the wine sector. The development of ai models like openai’s sora, runway’s gen-2 or google’s lumiere, which can generate realistic video content from textual descriptions, offers promising new avenues for enhancing brand narrative and consumer engagement. This research explores the potential of text-to-video (t2v) ai models to revolutionize wine marketing by creating dynamic, engaging content that captures the essence of vineyards and their products without the need for traditional video production processes.