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Using Colour Images for Online Yeast Growth Estimation

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 resear...

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Detalles Bibliográficos
Autores principales: August, Elias, Sabani, Besmira, Memeti, Nurdzane
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6412256/
https://www.ncbi.nlm.nih.gov/pubmed/30795509
http://dx.doi.org/10.3390/s19040894
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author August, Elias
Sabani, Besmira
Memeti, Nurdzane
author_facet August, Elias
Sabani, Besmira
Memeti, Nurdzane
author_sort August, Elias
collection PubMed
description 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.
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spelling pubmed-64122562019-04-03 Using Colour Images for Online Yeast Growth Estimation August, Elias Sabani, Besmira Memeti, Nurdzane Sensors (Basel) Article 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. MDPI 2019-02-21 /pmc/articles/PMC6412256/ /pubmed/30795509 http://dx.doi.org/10.3390/s19040894 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
August, Elias
Sabani, Besmira
Memeti, Nurdzane
Using Colour Images for Online Yeast Growth Estimation
title Using Colour Images for Online Yeast Growth Estimation
title_full Using Colour Images for Online Yeast Growth Estimation
title_fullStr Using Colour Images for Online Yeast Growth Estimation
title_full_unstemmed Using Colour Images for Online Yeast Growth Estimation
title_short Using Colour Images for Online Yeast Growth Estimation
title_sort using colour images for online yeast growth estimation
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6412256/
https://www.ncbi.nlm.nih.gov/pubmed/30795509
http://dx.doi.org/10.3390/s19040894
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