Determination of yeast viability during a stress‐model alcoholic fermentation using reagent‐free microscopy image analysis

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. © 2011 American Institute of Chemical Engineers Biotechnol. Prog., 2011