Deep learning enabled rapid classification of yeast species in food by imaging of yeast microcolonies

Diverse species of yeasts are commonly associated with food and food production environments. The contamination of food products by spoilage yeasts poses significant challenges, leading to quality degradation and food loss. Similarly, the introduction of undesirable strains during fermentation can cause considerable challenges with the quality and progress of the fermentation process. Conventional detection methods require the isolation of visible yeast colonies for genetic or biochemical characterization, which takes 5-7 days and demands significant labor. This study presents a deep learning-based yeast classification approach that combines conventional cultivation methods, white light optical microscopy of microcolony, and deep learning techniques for rapidly detecting and classifying yeasts. Utilizing deep convolutional neural networks, the model accurately discriminates 7 different yeasts within 6 h, achieving a mPrecision of 96.0 % and a mRecall of 96.3 %. Synthetic image dataset generated by generative adversarial networks (GAN) model further improved the model performance for Debaryomyces hansenii and Wickerhamomyces anomalus, yeast species with lower initial classification performance. With the addition of synthetic images in the training process, Precision for W. anomalus and Recall for D. hansenii increased by 7.7 % and 5.6 %, respectively. The yeast classification model was validated in the presence of microscopic food debris using tomato and tomato juice as representative examples of fresh produce and processed juice. The model maintained high classification accuracy in the presence of food debris (mPrecision and mRecall >93.9 %). Overall, this methodology significantly accelerates the detection and classification of yeast species using conventional cultivation and simple white light microscopy in combination with deep learning. The simplicity, including low cost of the experimental approaches and the robustness of the deep learning model make it a highly applicable approach for routine yeast monitoring and yeast spoilage control in the food industry.