Yeast Morphology Assessment through Automated Image Analysis during Fermentation

Abstract

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.

A Fully Defined Synthetic Medium Mimicking Sugar Cane Molasses

“…The total CO2 loss at the end of fermentations remained between 12-14 mmol.gwetbiomass -1 in molasses (TRS concentration 19.4%) at 30 °C, agreeing with reported values for PE-2 [11]. [44]. (D) Ethanol yield (as percentage of theoretical maximum 0.511 g of ethanol.g of TRS -1 , %) calculated based on [11] and [35].…”

“…(C) Biomass variation (%) represented as percentage of the cycle initial wet yeast mass. (D) Viability (as percentage of living cells, %) values by the methylene blue staining technique [44]. (E) Ethanol yield (as percentage of theoretical maximum 0.511 g of ethanol.g of TRS -1 , %) calculated based on [11] and [35] (*) mean value for a duplicate.…”

“…The copyright holder for this this version posted January 27, 2023. ; https://doi.org/10.1101/2023.01.27.525923 doi: bioRxiv preprint 13 [44]. (D) Ethanol yield (as percentage of theoretical maximum 0.511 g of ethanol.g of TRS -1 , %) calculated based on [11] and [35].…”

“…The final results obtained were different, in which a higher CO2 production per gram of cell was observed for the composition without nitrogen (15.0 ± 0.45 mmol.g -1 ) and decreased production for the 4SMol composition (12.27 ± 0.45 g.g -1 ), in comparison to the reference 2SMol composition (13.18 ± 0.23 g.g -1 ). technique [44]. (E) Ethanol yield (as percentage of theoretical maximum 0.511 g of ethanol.g of TRS -1 , %) calculated based on [11] and [35] (*) mean value for a duplicate.…”

The effect of CO2 concentration on yeast fermentation: rates, metabolic products, and yeast stress indicators

“…Fermentation rate and FAN consumption data were used for this analysis. Additionally, the yeast cross-sectional areas (CSAs) and vacuolar CSAs of yeast in suspension at each data point were measured using the automated image analysis method described by Guadalupe-Daqui et al ( 2021 ). Between 200 and 1300 yeast cells were assessed per sampling time and were assessed using the ImageJ software.…”