Kinetics observations of bacterial cellulose thickness formation using image processing approach during the fermentation process

Bacterial cellulose is an exopolysaccharide that has a higher level of purity compared to cellulose from plants. Bacterial Cellulose (BC) is widely used for various uses so that it requires certain initial conditions, one of which is thickness. During the fermentation process, cellulose will be secr...

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Main Authors: Nugroho, D. A., Sutiarso, Liik, Rahayu, Endang Sutriswati, Masithoh, Rudiati Evi
Format: Article PeerReviewed
Language:English
Published: Rynnye Lyan Resources 2022
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Online Access:https://repository.ugm.ac.id/282748/1/_26__fr-2021-093_nugroho.pdf
https://repository.ugm.ac.id/282748/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126432360&doi=10.26656%2ffr.2017.6%281%29.093&partnerID=40&md5=355764cdf7076162dae5540564097938
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spelling id-ugm-repo.2827482024-01-02T03:09:24Z https://repository.ugm.ac.id/282748/ Kinetics observations of bacterial cellulose thickness formation using image processing approach during the fermentation process Nugroho, D. A. Sutiarso, Liik Rahayu, Endang Sutriswati Masithoh, Rudiati Evi Microbial Ecology Food technology Bacterial cellulose is an exopolysaccharide that has a higher level of purity compared to cellulose from plants. Bacterial Cellulose (BC) is widely used for various uses so that it requires certain initial conditions, one of which is thickness. During the fermentation process, cellulose will be secreted into the medium to form BC sheets and visually visible over the time period. The aim of this research was to study the relationship between variables that influence during the fermentation and to fit the kinetic model of the BC thickness formation using image processing approach during the fermentation process. A USB camera was placed in front of the fermenter to capture the formation of BC thickness. Python programming language was used to process the image and calculated the thickness of the BC sheet from the beginning to the end of the fermentation process. Several supporting parameters were observed by placing the turbidity, pH, and medium temperature sensors. Observations were made in real time with a range of data collection every 15 mins during fermentation. The highest correlation value was obtained from the relationship between time and thickness. The fermentation process is divided into 2 clusters, a change in cluster occurs at the 61st hour. The model that describes the relationship between time and thickness was the Gompertz model. © 2022 The Authors. Rynnye Lyan Resources 2022-02 Article PeerReviewed application/pdf en https://repository.ugm.ac.id/282748/1/_26__fr-2021-093_nugroho.pdf Nugroho, D. A. and Sutiarso, Liik and Rahayu, Endang Sutriswati and Masithoh, Rudiati Evi (2022) Kinetics observations of bacterial cellulose thickness formation using image processing approach during the fermentation process. Food Research, 6 (1). pp. 210-214. ISSN 25502166 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126432360&doi=10.26656%2ffr.2017.6%281%29.093&partnerID=40&md5=355764cdf7076162dae5540564097938 10.26656/fr.2017.6(1).093
institution Universitas Gadjah Mada
building UGM Library
continent Asia
country Indonesia
Indonesia
content_provider UGM Library
collection Repository Civitas UGM
language English
topic Microbial Ecology
Food technology
spellingShingle Microbial Ecology
Food technology
Nugroho, D. A.
Sutiarso, Liik
Rahayu, Endang Sutriswati
Masithoh, Rudiati Evi
Kinetics observations of bacterial cellulose thickness formation using image processing approach during the fermentation process
description Bacterial cellulose is an exopolysaccharide that has a higher level of purity compared to cellulose from plants. Bacterial Cellulose (BC) is widely used for various uses so that it requires certain initial conditions, one of which is thickness. During the fermentation process, cellulose will be secreted into the medium to form BC sheets and visually visible over the time period. The aim of this research was to study the relationship between variables that influence during the fermentation and to fit the kinetic model of the BC thickness formation using image processing approach during the fermentation process. A USB camera was placed in front of the fermenter to capture the formation of BC thickness. Python programming language was used to process the image and calculated the thickness of the BC sheet from the beginning to the end of the fermentation process. Several supporting parameters were observed by placing the turbidity, pH, and medium temperature sensors. Observations were made in real time with a range of data collection every 15 mins during fermentation. The highest correlation value was obtained from the relationship between time and thickness. The fermentation process is divided into 2 clusters, a change in cluster occurs at the 61st hour. The model that describes the relationship between time and thickness was the Gompertz model. © 2022 The Authors.
format Article
PeerReviewed
author Nugroho, D. A.
Sutiarso, Liik
Rahayu, Endang Sutriswati
Masithoh, Rudiati Evi
author_facet Nugroho, D. A.
Sutiarso, Liik
Rahayu, Endang Sutriswati
Masithoh, Rudiati Evi
author_sort Nugroho, D. A.
title Kinetics observations of bacterial cellulose thickness formation using image processing approach during the fermentation process
title_short Kinetics observations of bacterial cellulose thickness formation using image processing approach during the fermentation process
title_full Kinetics observations of bacterial cellulose thickness formation using image processing approach during the fermentation process
title_fullStr Kinetics observations of bacterial cellulose thickness formation using image processing approach during the fermentation process
title_full_unstemmed Kinetics observations of bacterial cellulose thickness formation using image processing approach during the fermentation process
title_sort kinetics observations of bacterial cellulose thickness formation using image processing approach during the fermentation process
publisher Rynnye Lyan Resources
publishDate 2022
url https://repository.ugm.ac.id/282748/1/_26__fr-2021-093_nugroho.pdf
https://repository.ugm.ac.id/282748/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126432360&doi=10.26656%2ffr.2017.6%281%29.093&partnerID=40&md5=355764cdf7076162dae5540564097938
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