Cell morphology observations for discriminating between <i>Brettanomyces bruxellensis</i> strains among genetic groups

Abstract

It is essential to discriminate between B. bruxellensis isolates at the strain level, because stress resistance capacities are strain dependent and also related to the genetic groups (GG). In this work, we investigated further the correlation between genetic groups and cell polymorphism by analysing optical microscopy images via deep learning. A Convolutional Neural Network (CNN) was trained to discriminate between 74 different B. bruxellensis isolates belonging to 4 of the 6 genetic groups described. Compared to the microsatellite analysis, the CNN enabled the prediction of the genetic groups of B. bruxellensis isolates with 96.6 % accuracy in a faster and cheaper way and with the same genetic group affiliations. Based on these very promising results, further research is needed to validate this technique for all genetic groups.