Using Colour Images for Online Yeast Growth Estimation

• Published in: Sensors (Basel, Switzerland) Vol. 19; no. 4; p. 894
• Main Authors: August, Elias, Sabani, Besmira, Memeti, Nurdzane
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI 21.02.2019; MDPI AG
• Subjects: Algorithms; automatisation; Biomass; Bioreactors; Cluster Analysis; computer vision; non-invasive online measurements; optical density measurements; pattern recognition; Saccharomyces cerevisiae - growth & development; software sensor; automatisation; non-invasive online measurements; optical density measurements; software sensor; computer vision; pattern recognition
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s19040894

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.