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IVES 9 IVES Conference Series 9 CONVOLUTIONAL NEURAL NETWORK TO PREDICT GENETIC GROUP AND SULFUR TOLERANCE OF BRETTANOMYCES BRUXELLENSIS

CONVOLUTIONAL NEURAL NETWORK TO PREDICT GENETIC GROUP AND SULFUR TOLERANCE OF BRETTANOMYCES BRUXELLENSIS

Abstract

The spoilage yeast Brettanomyces bruxellensis presents many strain dependent characteristics, particularly sulfur tolerance (1). Climate change and the evolution of oenological practices are at the origin of matrices with low levels of sulfur dioxide and higher pH. These parameters favor the development of this yeast and lead to serious financial losses for winemakers (2). Thus, it is essential to discriminate B. bruxellensis isolates at the strain level in order to predict their stress resistance capacities. Few predictive tools are available to reveal intraspecific diversity within B. bruxellensis species; also, they require expertise and can be expensive. In this study, to make analysis even faster, we further investigated the correlation between genetic groups previously described (3) and cell polymorphism using the analysis of optical microscopy images via deep learning. A Convolutional Neural Network (CNN) was trained and allowed the discrimination of B. bruxellensis isolates in 4 of the 6 genetic groups (GG), with an accuracy of 96.6% (4). Future works will have to be done for the no tested genetic groups. But already these results confirm the possibility to develop a tool allowing to determine the tolerance of a contaminant, in a short time, in order to help wine industry professionals to choose the appropriate corrective measure.

 

1. Avramova M, Cibrario A, Peltier E, Coton M, Coton E, Schacherer J, Spano G, Capozzi V, Blaiotta G,  Salin F, Dols-Lafargue M, Grbin P, Curtin C, AlbertinW, Masneuf-Pomarede I (2018) Brettanomyces bruxellensis population survey reveals a diploid-triploid complex structured according to substrate of isolation and geographical distribution. Sci. Rep. 8, 4136. https://doi.org/10.1038/s41598-018-
2. Alston J, Arvik T, Hart J. Lapsley JT (2021) Brettanomics I: The Cost of Brettanomyces in California Wine ProductionJournal of Wine Economics, Volume 16, Number 1, 2021, Pages 4–31 doi:10.1017/jwe.2020.20 
3. Lebleux M*, Abdo H*, Coelho C, Basmaciyan L, Albertin W, Maupeu J, Laurent J, Roullier-Gall C, Alexandre H, Guilloux-Benatier M, Weidmann S, Rousseaux S (2020) New advances on the Brettanomyces bruxellensis biofilm mode of life. Int J Food Microbiol, 318, pp.1084642. DOI : 10.1016/j.ijfoodmicro.2019.108464 
4. Lebleux M, Denimal E, De Oliveira D, Marin A, Desroche N, Alexandre H, Weidmann S, Rousseaux S (2021) Prediction of genetic groups within Brettanomyces bruxellensis through cell morphology using a deep learning tool in press J. Fungi, 7(8):581 https://doi.org/10.3390/jof7080581 

DOI:

Publication date: 09/02/2024

Issue: OENO Macrowine 2023

Type: Poster

Authors

Sandrine Rousseaux1, Manon Lebleux1, Emmanuel Denimal2, Stéphanie Weidmann 1

1. Laboratoire VAlMiS-IUVV, AgroSup Dijon, UMR PAM A 02.102, University Bourgogne Franche-Comté, F-21000 Dijon, France
2. AgroSup Dijon, Direction Scientifique, Appui à la Recherche, 26 Boulevard Docteur Petitjean, F-21000 Dijon, France

Contact the author*

Keywords

Brettanomyces bruxellensis, deep learning, cell morphology, genetic groups

Tags

IVES Conference Series | oeno macrowine 2023 | oeno-macrowine

Citation

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