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Interest in bioethanol production has experienced a resurgence in the last few years. Poor temperature control in industrial fermentation tanks exposes the yeast cells used for this production to intermittent heat stress which impairs fermentation efficiency. Therefore, there is a need for yeast strains with improved tolerance, able to recover from such temperature variations. Accordingly, this paper reports the development of methods for the characterization of Saccharomyces cerevisiae growth recovery after a sublethal heat stress. Single-cell measurements were carried out in order to detect cell-to-cell variability. Alcoholic batch fermentations were performed on a defined medium in a 2 l instrumented bioreactor. A rapid temperature shift from 33 to 43 °C was applied when ethanol concentration reached 50 g l⁻¹. Samples were collected at different times after the temperature shift. Single cell growth capability, lag-time and initial growth rate were determined by monitoring the growth of a statistically significant number of cells after agar medium plating. The rapid temperature shift resulted in an immediate arrest of growth and triggered a progressive loss of cultivability from 100 to 0.0001% within 8 h. Heat-injured cells were able to recover their growth capability on agar medium after a lag phase. Lag-time was longer and more widely distributed as the time of heat exposure increased. Thus, lag-time distribution gives an insight into strain sensitivity to heat-stress, and could be helpful for the selection of yeast strains of technological interest.

A dedicated microscopy imaging system including automated positioning, focusing, image acquisition, and image analysis was developed to characterize a yeast population with regard to cell morphology. This method was used to monitor a stress-model alcoholic fermentation with Saccharomyces cerevisiae. Combination of dark field and epifluorescence microscopy after propidium iodide staining for membrane integrity showed that cell death went along with important changes in cell morphology, with a cell shrinking, the onset of inhomogeneities in the cytoplasm, and a detachment of the plasma membrane from the cell wall. These modifications were significant enough to enable a trained human operator to make the difference between dead and viable cells. Accordingly, a multivariate data analysis using an artificial neural network was achieved to build a predictive model to infer viability at single-cell level automatically from microscopy images without any staining. Applying this method to in situ microscope images could help to detect abnormal situations during a fermentation course and to prevent cell death by applying adapted corrective actions.