Next Article in Journal
High-Temperature Semi-Dry and Sweet Low Alcohol Wine-Making Using Immobilized Kefir Culture
Next Article in Special Issue
Determination of Foam Stability in Lager Beers Using Digital Image Analysis of Images Obtained Using RGB and 3D Cameras
Previous Article in Journal
Unravelling the Impact of Grape Washing, SO2, and Multi-Starter Inoculation in Lab-Scale Vinification Trials of Withered Black Grapes
Previous Article in Special Issue
Predicting Alcohol Concentration during Beer Fermentation Using Ultrasonic Measurements and Machine Learning
Article

Yeast Morphology Assessment through Automated Image Analysis during Fermentation

Food Science and Human Nutrition Department, University of Florida, Gainesville, FL 32611, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Claudia Gonzalez Viejo
Fermentation 2021, 7(2), 44; https://doi.org/10.3390/fermentation7020044
Received: 5 March 2021 / Accepted: 20 March 2021 / Published: 24 March 2021
(This article belongs to the Special Issue Implementation of Digital Technologies on Beverage Fermentation)
The kinetics and success of an industrial fermentation are dependent upon the health of the microorganism(s) responsible. Saccharomyces 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. View Full-Text
Keywords: yeast morphology; automated image analysis; heat stress; vacuoles; cell size; computer vision yeast morphology; automated image analysis; heat stress; vacuoles; cell size; computer vision
Show Figures

Figure 1

MDPI and ACS Style

Guadalupe-Daqui, M.; Chen, M.; Thompson-Witrick, K.A.; MacIntosh, A.J. Yeast Morphology Assessment through Automated Image Analysis during Fermentation. Fermentation 2021, 7, 44. https://doi.org/10.3390/fermentation7020044

AMA Style

Guadalupe-Daqui M, Chen M, Thompson-Witrick KA, MacIntosh AJ. Yeast Morphology Assessment through Automated Image Analysis during Fermentation. Fermentation. 2021; 7(2):44. https://doi.org/10.3390/fermentation7020044

Chicago/Turabian Style

Guadalupe-Daqui, Mario, Mandi Chen, Katherine A. Thompson-Witrick, and Andrew J. MacIntosh 2021. "Yeast Morphology Assessment through Automated Image Analysis during Fermentation" Fermentation 7, no. 2: 44. https://doi.org/10.3390/fermentation7020044

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop