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IVES 9 IVES Conference Series 9 OIV 9 OIV 2024 9 Orals - Oenology, methods of analysis 9 Multi-mineral wine profiling and Artificial Intelligence: Implementing the signatures of each wine to train algorithms to meet the new challenges facing the wine industry

Multi-mineral wine profiling and Artificial Intelligence: Implementing the signatures of each wine to train algorithms to meet the new challenges facing the wine industry

Abstract

Although present in minimal quantities, minerals are essential to the composition of every wine. Their presence results from complex interactions between various factors such as soil, vines, climate, topography, and viticultural practices, all strongly influenced by terroir. Each stage of the winemaking process further shapes the unique mineral and flavor profile of each wine. This mineral composition, stable over time, can serve as a foundation for developing new artificial intelligence (AI) algorithms. To this end, we have established a comprehensive database of over 20,000 wines, each characterized by a “Mineral Wine Profile” (MWP), a concentration profile of forty mineral elements obtained through inductively coupled plasma mass spectrometry (ICP-MS). These profiles constitute robust and extensive datasets, ideal for integration into AI-guided oenology. Beyond the specific signature of a wine and the development of authentication services, combining these signatures with additional data allows for the creation of new AI-driven tools to address emerging challenges in the wine industry. We will present the first proofs of concept that demonstrate the potential of this AI and mineral analysis approach for the creation of targeted wines.

Profilo multiminerale dei vini: un importante passo avanti nei modelli di IA dell’industria vinicola

Profilazione multiminerale dei vini e intelligenza artificiale: implementare le firme di ogni vino per addestrare algoritmi in grado di affrontare le nuove sfide dell’industria vinicola.  Sebbene la loro quantità sia minima, i minerali sono elementi essenziali nella composizione di ogni vino. La loro presenza è il risultato di complesse interazioni tra vari fattori come il suolo, i vitigni, il clima, la topografia e le pratiche viticole, tutti influenzati dal territorio. Ogni fase del processo di vinificazione contribuisce inoltre a plasmare il profilo minerale e gustativo unico di ogni vino, conferendo a ciascuna cuvée il suo carattere distintivo. Questa composizione minerale, stabile nel tempo, è di fondamentale importanza per una comprensione approfondita dei vini e può servire come base per lo sviluppo di nuovi algoritmi di intelligenza artificiale.  In quest’ottica, abbiamo intrapreso un’indagine sul mineral wine profile (mwp), che consiste in un profilo di concentrazione di 41 elementi minerali ottenuto semplicemente con un’analisi semi-quantitativa mediante spettrometria di massa accoppiata induttivamente (icp-ms). Oltre alla firma specifica di una cuvée e allo sviluppo di servizi di autenticazione, l’associazione di queste firme con altre informazioni (qualitative, pedologiche, enologiche, viticole, ambientali, ecc.) Consentirà, grazie all’intelligenza artificiale, di creare nuovi strumenti in grado di rispondere alle sfide emergenti dell’industria del vino.   I minerali possono essere sia causa che conseguenza dei processi studiati; le loro origini sono complesse, coinvolgono molteplici reazioni chimiche in ogni fase e i loro equilibri sono relativi tra ogni elemento. Nonostante la loro grande complessità e sensibilità, questi profili possono essere posizionati al centro dell’enologia guidata dall’intelligenza artificiale. Abbiamo già analizzato e sviluppato il più grande database di minerali, che attualmente contiene più di 20.000 mwp.  Presenteremo i primi proof of concept che illustrano la forza di questo approccio e discuteremo le prospettive future.

Profil multi-minéral des vins : avancée majeure dans les modèles d’IA viti-vinicoles

Profil multi-minéral des vins et intelligence artificielle : implémentation des signatures de chaque vin pour l’entraînement d’algorithmes au service des nouveaux enjeux de la filière viti-vinicole.  Bien que leur quantité soit minime, les minéraux sont des éléments essentiels de la composition de chaque vin. Leur présence découle d’interactions complexes entre divers facteurs tels que le sol, la vigne, le climat, la topographie et les pratiques viticoles, tous influencés par le terroir. Chaque étape du processus de vinification contribue également à façonner le profil minéral et gustatif unique de chaque vin, conférant ainsi à chaque cuvée son caractère distinctif. Cette composition minérale, stable dans le temps, revêt une importance fondamentale pour une compréhension approfondie des vins, pouvant servir de base au développement de nouveaux algorithmes d’intelligence artificielle.  Dans cette perspective, nous avons entrepris le recensement du mineral wine profile (mwp), qui consiste en un profil de concentration de 41 éléments minéraux obtenus simplement par analyse semi-quantitative en spectrométrie de masse à couplage inductif (icp-ms). Au-delà de la signature spécifique d’une cuvée et des développements de services d’authentification, l’association de ces signatures à d’autres informations (qualitatives, sols, œnologiques, viticoles, environnementales, etc.) Permettra, grâce à l’intelligence artificielle, de créer de nouveaux outils répondant aux défis émergents de la filière viti-vinicole.  Les minéraux peuvent être à la fois la cause et/ou la conséquence des processus étudiés ; leurs origines sont complexes, impliquant de multiples réactions chimiques à chaque étape, et leurs équilibres sont relatifs entre chaque élément. Malgré leur grande complexité et sensibilité, ces profils peuvent être positionnés au cœur de l’œnologie guidée par l’intelligence artificielle. Nous avons déjà analysé et développé la plus grande base de données minérales, contenant actuellement plus de 20 000 mwp.  Nous présenterons les premières preuves de concept illustrant la force de cette approche et discuterons des perspectives à venir.

DOI:

Publication date: November 18, 2024

Issue: OIV 2024

Type: Article

Authors

Augustin Tillement1,2,3, Coraline Duroux2, Leticia Sarlo1,2, Théodore Tillement2, Laurent David3, and Olivier Tillement1,2

1 Institut Lumière-Matière, UMR 5306, Université Claude Bernard Lyon 1-CNRS, Université de Lyon, Villeurbanne Cedex 69100, France
2 M&Wine, 305 rue des Fours, 69270 Fontaines Saint Martin, France
3 Institut National des Sciences Appliquées, CNRS UMR 5223, Ingénierie des Matériaux Polymères, Université Claude Bernard Lyon 1, Université Jean Monnet, 15 bd Latarjet, 69622 Villeurbanne, France

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Full papers OIV 2024 | IVES Conference Series | OIV | OIV 2024

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