Optimisation of co-culture fermentation of lactobacillus casei and propionibacterium jensenii in rice bran extract

Co-culture fermentation is a fermentation process involving two defined microorganisms growing together in the same culture. A co-culture of lactic acid-producing bacteria (LAB) and propionic acid-producing bacteria (PAB) is beneficial in producing direct-fed microbial (DFM) products. The synergisti...

Full description

Saved in:

Bibliographic Details
Main Author:	Mohamed Esivan, Siti Marsilawati
Format:	Thesis
Language:	English
Published:	2022
Subjects:	TP Chemical technology
Online Access:	http://eprints.utm.my/id/eprint/101589/1/SitiMarsilawatiMohamedEsivanPSChe2022.pdf
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-utm-ep.101589
record_format	uketd_dc
spelling	my-utm-ep.1015892023-06-26T06:50:01Z Optimisation of co-culture fermentation of lactobacillus casei and propionibacterium jensenii in rice bran extract 2022 Mohamed Esivan, Siti Marsilawati TP Chemical technology Co-culture fermentation is a fermentation process involving two defined microorganisms growing together in the same culture. A co-culture of lactic acid-producing bacteria (LAB) and propionic acid-producing bacteria (PAB) is beneficial in producing direct-fed microbial (DFM) products. The synergistic activity between LAB and PAB in co-culture fermentation can improve the survival of LAB and the growth of PAB. On this basis, the objectives of this study are two-fold. Firstly, the optimisation of co-culture fermentation involving Lactobacillus casei and Propionibacterium jensenii in the agricultural waste extract. Secondly, the development of an artificial neural network (ANN) predictive model for predicting the cell biomass concentration and the co-culture-specific growth rate. In the preliminary phase, two different substrates, namely rice bran and banana peel, were used in this study. This step was conducted to select the suitable carbon source for L. casei to grow and produce lactic acid for P. jensenii consumption. From the observation, rice bran was found more suitable as a carbon source and fermentation medium. Next, the co-culture optimisation of L. casei and P. jensenii fermentation was conducted using the one-factor-at-a-time approach. The fermentations were optimised for rice bran at concentration of 5% to 25% w/v; incubation temperature (30? to 42?); inoculation ratio (1:1 to 1:10 % v/v) and the initial pH (5.0 to 7.0). The optimum fermentation condition was obtained at 20% w/v rice bran concentration, incubated at 35? with an inoculation ratio of 1:4 % v/v and initial pH of 6.5. The optimum growth (2.74 g dry cell weight/L) was recorded after 96 hours of incubation. The highest viable cell counts for L. casei and P. jensenii were 9.10 log CFU/mL and 9.42 log CFU/mL, respectively. The optimum specific growth rate, µ obtained, was 0.41 h-1. The growth of L. casei and P. jensenii was compared to its monoculture fermentation, and it was found that the co-culture did not affect the growth of L. casei but helped maintain its survival. Moreover, P. jensenii gained benefits in the co-culture system, as its growth improved compared to during its monoculture. The ANN predictive model was developed using the multilayer perceptron and trained using the Levenberg-Marquardt training algorithm. Five input parameters, incubation time (h), the concentration of total reducing sugar (g/L), pH culture, incubation temperature (?) and inoculation ratio (% v/v), were used to train the network for the prediction of cell biomass concentration (g/L) and the co-culture specific growth rate, µ (h-1). The model has a low mean square error and high regression coefficient (R2) for the training and testing set, indicating the model is fit to predict the cell biomass produced and its specific growth rate during the co-culture of L. casei and P. jensenii. The structure obtained for ANN predictive model consist of five inputs, eight hidden nodes and two outputs, 5-8-2. The optimum predicted cell biomass concentration and the specific growth rate, µ, were 2.24 g dry cell weight/L and 0.51 h-1, respectively. In conclusion, this work provides a strategy to produce multispecies DFM through co-culture fermentation using rice bran and presented the first predictive ANN model to predict the cell biomass concentration and the co-culture-specific growth rate of L. casei and P. jensenii. 2022 Thesis http://eprints.utm.my/id/eprint/101589/ http://eprints.utm.my/id/eprint/101589/1/SitiMarsilawatiMohamedEsivanPSChe2022.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:150678 phd doctoral Universiti Teknologi Malaysia Faculty of Engineering - School of Chemical & Energy Engineering
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	TP Chemical technology
spellingShingle	TP Chemical technology Mohamed Esivan, Siti Marsilawati Optimisation of co-culture fermentation of lactobacillus casei and propionibacterium jensenii in rice bran extract
description	Co-culture fermentation is a fermentation process involving two defined microorganisms growing together in the same culture. A co-culture of lactic acid-producing bacteria (LAB) and propionic acid-producing bacteria (PAB) is beneficial in producing direct-fed microbial (DFM) products. The synergistic activity between LAB and PAB in co-culture fermentation can improve the survival of LAB and the growth of PAB. On this basis, the objectives of this study are two-fold. Firstly, the optimisation of co-culture fermentation involving Lactobacillus casei and Propionibacterium jensenii in the agricultural waste extract. Secondly, the development of an artificial neural network (ANN) predictive model for predicting the cell biomass concentration and the co-culture-specific growth rate. In the preliminary phase, two different substrates, namely rice bran and banana peel, were used in this study. This step was conducted to select the suitable carbon source for L. casei to grow and produce lactic acid for P. jensenii consumption. From the observation, rice bran was found more suitable as a carbon source and fermentation medium. Next, the co-culture optimisation of L. casei and P. jensenii fermentation was conducted using the one-factor-at-a-time approach. The fermentations were optimised for rice bran at concentration of 5% to 25% w/v; incubation temperature (30? to 42?); inoculation ratio (1:1 to 1:10 % v/v) and the initial pH (5.0 to 7.0). The optimum fermentation condition was obtained at 20% w/v rice bran concentration, incubated at 35? with an inoculation ratio of 1:4 % v/v and initial pH of 6.5. The optimum growth (2.74 g dry cell weight/L) was recorded after 96 hours of incubation. The highest viable cell counts for L. casei and P. jensenii were 9.10 log CFU/mL and 9.42 log CFU/mL, respectively. The optimum specific growth rate, µ obtained, was 0.41 h-1. The growth of L. casei and P. jensenii was compared to its monoculture fermentation, and it was found that the co-culture did not affect the growth of L. casei but helped maintain its survival. Moreover, P. jensenii gained benefits in the co-culture system, as its growth improved compared to during its monoculture. The ANN predictive model was developed using the multilayer perceptron and trained using the Levenberg-Marquardt training algorithm. Five input parameters, incubation time (h), the concentration of total reducing sugar (g/L), pH culture, incubation temperature (?) and inoculation ratio (% v/v), were used to train the network for the prediction of cell biomass concentration (g/L) and the co-culture specific growth rate, µ (h-1). The model has a low mean square error and high regression coefficient (R2) for the training and testing set, indicating the model is fit to predict the cell biomass produced and its specific growth rate during the co-culture of L. casei and P. jensenii. The structure obtained for ANN predictive model consist of five inputs, eight hidden nodes and two outputs, 5-8-2. The optimum predicted cell biomass concentration and the specific growth rate, µ, were 2.24 g dry cell weight/L and 0.51 h-1, respectively. In conclusion, this work provides a strategy to produce multispecies DFM through co-culture fermentation using rice bran and presented the first predictive ANN model to predict the cell biomass concentration and the co-culture-specific growth rate of L. casei and P. jensenii.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Mohamed Esivan, Siti Marsilawati
author_facet	Mohamed Esivan, Siti Marsilawati
author_sort	Mohamed Esivan, Siti Marsilawati
title	Optimisation of co-culture fermentation of lactobacillus casei and propionibacterium jensenii in rice bran extract
title_short	Optimisation of co-culture fermentation of lactobacillus casei and propionibacterium jensenii in rice bran extract
title_full	Optimisation of co-culture fermentation of lactobacillus casei and propionibacterium jensenii in rice bran extract
title_fullStr	Optimisation of co-culture fermentation of lactobacillus casei and propionibacterium jensenii in rice bran extract
title_full_unstemmed	Optimisation of co-culture fermentation of lactobacillus casei and propionibacterium jensenii in rice bran extract
title_sort	optimisation of co-culture fermentation of lactobacillus casei and propionibacterium jensenii in rice bran extract
granting_institution	Universiti Teknologi Malaysia
granting_department	Faculty of Engineering - School of Chemical & Energy Engineering
publishDate	2022
url	http://eprints.utm.my/id/eprint/101589/1/SitiMarsilawatiMohamedEsivanPSChe2022.pdf
_version_	1776100733805920256

Optimisation of co-culture fermentation of lactobacillus casei and propionibacterium jensenii in rice bran extract

Similar Items