Yeast fermentation of sugarcane for ethanol production: Can it be monitored by using in situ microscopy?

Abstract

This paper addresses some key issues related to the automation of fermentation process analysis in the context of industrial-scale ethanol production from sugarcane substrates. As the current methods for the determination of cell density and viability are time consuming and laborious, high resolution in situ microscopy (0.5µm) is proposed as a promising alternative. Laboratory-scale experiments presented here show that this imaging technique allows automatic, on-line, and real-time monitoring of yeast cells suspended in sugarcane molasses used in the ethanol industry. In particular, the feasibility of cell concentration measurements of Saccharomyces cerevisiae SA-1 in industrial sugarcane molasses is demonstrated. Automated concentration measurements exhibit a linear correlation with manual reference values using a Neubauer chamber from 3×10 6 cells/mL up to a saturation level at approximately 2×10 8 cells/mL. Furthermore, it was demonstrated that the microscopic resolution of this technique, combined with its large statistics, allows a morphological assessment of the size, shape and some internal structures of the yeast cells. On average, the accuracy of the algorithm´s yeast cells classification was 0.80. The results obtained suggest that the ISM is a suitable tool to perform in-line sugarcane fermentation monitoring.

Automatic cell segmentation from brightfield microscopy images of pseudohyphal cell-aggregates

“…Unlike dispersed S. cerevisiae single yeast cells, see e.g. [12], the present strain exhibits a varying amount of branched chains of cells with different orientations so that their optical appearance depends on their distance from the objective focal plane (Fig. 1).…”

“…An in situ microscope (ISM) is a light microscope adapted to capture images directly from suspensions. It has been used to monitor cell number, cell size distribution, and morphological characteristics of several specimens, including yeasts [12][13][14][15][16][17][18][19][20], filamentous bacteria [21,22], Chinese hamster ovary (CHO) cells [23], and the viability of mammalian cells [24].…”

Benchmarking real-time monitoring strategies for ethanol production from lignocellulosic biomass

“…Donnelly et al [87] developed a method to predict the viability of cell cultures with the cell volume distribution and used it to calculate the pitch size in industrial fermentations. Belini et al [88] used in-line microscopy combined with image analysis to monitor yeast growth in a lab-scale molasses-toethanol fermentation. By using classification algorithms, they were able to differentiate between yeast cells and other solid compounds present in the fermentation media (e.g., plant fibers, sugar crystals or gas bubbles).…”