Yeast Morphology Assessment through Automated Image Analysis during Fermentation

• Main Authors: Mario Guadalupe-Daqui, Mandi Chen, Katherine A. Thompson-Witrick, Andrew J. MacIntosh
• Format: Article
• Language: English
• Published: MDPI AG 2021-03-01
• Series: Fermentation
• Subjects: yeast morphology; automated image analysis; heat stress; vacuoles; cell size; computer vision
• Online Access: https://www.mdpi.com/2311-5637/7/2/44

The kinetics and success of an industrial fermentation are dependent upon the health of the microorganism(s) responsible. <i>Saccharomyces</i> sp. are the most commonly used organisms in food and beverage production; consequently, many metrics of yeast health and stress have been previously correlated with morphological changes to fermentations kinetics. Many researchers and industries use machine vision to count yeast and assess health through dyes and image analysis. This study assessed known physical differences through automated image analysis taken throughout ongoing high stress fermentations at various temperatures (30 °C and 35 °C). Measured parameters included sugar consumption rate, number of yeast cells in suspension, yeast cross-sectional area, and vacuole cross-sectional area. The cell morphological properties were analyzed automatically using ImageJ software and validated using manual assessment. It was found that there were significant changes in cell area and ratio of vacuole to cell area over the fermentation. These changes were temperature dependent. The changes in morphology have implications for rates of cellular reactions and efficiency within industrial fermentation processes. The use of automated image analysis to quantify these parameters is possible using currently available systems and will provide additional tools to enhance our understanding of the fermentation process.