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Growth Assays to Assess Polyglutamine Toxicity in Yeast
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Using Colour Images for Online Yeast Growth Estimation.

Elias August1, Besmira Sabani2, Nurdzane Memeti3

  • 1Institute of Chemistry and Biotechnology, Zurich University of Applied Sciences, 8820 Wädenswil, Switzerland. elias_august@hotmail.com.

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Smartphone images can estimate biomass concentration in shake flasks using machine vision. This automated, non-invasive method offers affordable online monitoring for laboratory digitalization.

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Area of Science:

  • Biotechnology
  • Laboratory Automation
  • Machine Vision

Background:

  • Laboratory processes increasingly require automation and digitalization.
  • Online measurement techniques are essential for real-time monitoring of bioprocesses.
  • Non-invasive methods are desirable for undisturbed cultivation.

Purpose of the Study:

  • To investigate the use of smartphone images for non-invasive biomass concentration estimation.
  • To develop an automated machine vision approach for robustly identifying regions of interest in shake flask images.
  • To assess the feasibility of affordable, simple online measurement techniques for laboratory cultivation.

Main Methods:

  • 18 experiments were conducted with over 340 measurements.
  • K-means clustering was employed for automatic selection of the relevant image region.
  • Biomass concentration estimation was based on color changes within the selected region.

Main Results:

  • High-fidelity model predictions of optical density were achieved.
  • The method demonstrated the potential for accurate biomass estimation using image analysis.
  • Automated region selection proved robust for process automation.

Conclusions:

  • Smartphone-based image analysis is a viable and affordable method for non-invasive biomass monitoring.
  • Machine vision and K-means clustering enable automated, robust online measurements.
  • This approach supports the digitalization of laboratory cultivation processes.