Using Colour Images for Online Yeast Growth Estimation

Sensors
Elias AugustNurdzane Memeti

Abstract

Automatisation and digitalisation of laboratory processes require adequate online measurement techniques. In this paper, we present affordable and simple means for non-invasive measurement of biomass concentrations during cultivation in shake flasks. Specifically, we investigate the following research questions. Can images of shake flasks and their content acquired with smartphone cameras be used to estimate biomass concentrations? Can machine vision be used to robustly determine the region of interest in the images such that the process can be automated? To answer these questions, 18 experiments were performed and more than 340 measurements taken. The relevant region in the images was selected automatically using K-means clustering. Statistical analysis shows high fidelity of the resulting model predictions of optical density values that were based on the information embedded in colour changes of the automatically selected region in the images.

References

Apr 24, 2012·Trends in Biotechnology·Wolf Klöckner, Jochen Büchs
Sep 22, 2012·Lab on a Chip·Li ShenIan Papautsky
Nov 28, 2012·Trends in Biotechnology·Abhinav A Shukla, Uwe Gottschalk
Dec 21, 2012·Lab on a Chip·Ahmet F CoskunAydogan Ozcan
Oct 31, 2013·Molecular Biology and Evolution·Barry G HallMiriam Barlow
Dec 30, 2014·Water Research·Clémence SicardCarlos D M Filipe
Jan 10, 2017·Scientific Reports·Huisung KimEuiwon Bae
Jan 19, 2018·Sensors·Andrew J S McGonigleAlfio V Parisi

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Methods Mentioned

BETA
light scattering
chips

Software Mentioned

R
imread
PopSockets
MATLAB
rgb2lab
CIELAB

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