Yeast viability and concentration analysis using lens-free computational microscopy and machine learning

Alborz Feizi; Yibo Zhang; Alon Greenbaum; Alex Guziak; Michelle Luong; Raymond Yan Lok Chan; Brandon Berg; Haydar Ozkan; Wei Luo; Michael Wu; Yichen Wu; Aydogan Ozcan

doi:10.1117/12.2252731

3 March 2017 Yeast viability and concentration analysis using lens-free computational microscopy and machine learning

Alborz Feizi, Yibo Zhang, Alon Greenbaum, Alex Guziak, Michelle Luong, Raymond Yan Lok Chan, Brandon Berg, Haydar Ozkan, Wei Luo, Michael Wu, Yichen Wu, Aydogan Ozcan

Author Affiliations +

Proceedings Volume 10055, Optics and Biophotonics in Low-Resource Settings III; 1005508 (2017) https://doi.org/10.1117/12.2252731
Event: SPIE BiOS, 2017, San Francisco, California, United States

Abstract

Research laboratories and the industry rely on yeast viability and concentration measurements to adjust fermentation parameters such as pH, temperature, and pressure. Beer-brewing processes as well as biofuel production can especially utilize a cost-effective and portable way of obtaining data on cell viability and concentration. However, current methods of analysis are relatively costly and tedious. Here, we demonstrate a rapid, portable, and cost-effective platform for imaging and measuring viability and concentration of yeast cells. Our platform features a lens-free microscope that weighs 70 g and has dimensions of 12 × 4 × 4 cm. A partially-coherent illumination source (a light-emitting-diode), a band-pass optical filter, and a multimode optical fiber are used to illuminate the sample. The yeast sample is directly placed on a complementary metal-oxide semiconductor (CMOS) image sensor chip, which captures an in-line hologram of the sample over a large field-of-view of >20 mm². The hologram is transferred to a touch-screen interface, where a trained Support Vector Machine model classifies yeast cells stained with methylene blue as live or dead and measures cell viability as well as concentration. We tested the accuracy of our platform against manual counting of live and dead cells using fluorescent exclusion staining and a bench-top fluorescence microscope. Our regression analysis showed no significant difference between the two methods within a concentration range of 1.4 × 10⁵ to 1.4 × 10⁶ cells/mL. This compact and cost-effective yeast analysis platform will enable automatic quantification of yeast viability and concentration in field settings and resource-limited environments.

Conference Presentation

Citation Download Citation

Alborz Feizi, Yibo Zhang, Alon Greenbaum, Alex Guziak, Michelle Luong, Raymond Yan Lok Chan, Brandon Berg, Haydar Ozkan, Wei Luo, Michael Wu, Yichen Wu, and Aydogan Ozcan "Yeast viability and concentration analysis using lens-free computational microscopy and machine learning", Proc. SPIE 10055, Optics and Biophotonics in Low-Resource Settings III, 1005508 (3 March 2017); https://doi.org/10.1117/12.2252731

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