Comparison between response surface methodology and genetic algorithm analysis to optimize lactic acid production by Lactobacillus rhamnosus and Lactobacillus acidophilus under ultrasonic pretreatment.

Lactobacillus rhamnosus; Lactobacillus acidophilus genetic algorithm lactic acid response surface methodology ultrasound

Journal

FEMS microbiology letters
ISSN: 1574-6968
Titre abrégé: FEMS Microbiol Lett
Pays: England
ID NLM: 7705721

Informations de publication

Date de publication:
15 02 2022
Historique:
received: 20 07 2021
revised: 17 11 2021
accepted: 11 01 2022
pubmed: 14 1 2022
medline: 5 3 2022
entrez: 13 1 2022
Statut: ppublish

Résumé

This work compared response surface methodology (RSM) and genetic algorithm (GA) analysis to optimize the lactic acid content by Lactobacillus rhamnosus PTCC 1637 and Lactobacillus acidophilus PTCC 1643 in a medium based on date syrup. Three parameters including concentrations of sucrose [10 and 20% (w/w)] and yeast extract [1, 2 and 3% (w/w)] along with different amplitudes of ultrasound (30 kHz, 25 and 50%) were investigated in terms of their impacts on both viable cell counts and lactic acid production. Regarding RSM and GA, optimized samples were selected by achieving high lactic acid concentration. The results indicated that an increase in the amounts of sucrose and yeast extract led to increasing the cell growth and lactic acid production. Application of ultrasound at 25% amplitude significantly (P < 0.05) stimulated the fermentation process. However, increasing the amplitude to 50% significantly (P < 0.05) decreased the lactic acid production compared with the control samples. The best treatment was observed at 20% sucrose, 3% yeast extract and 25% ultrasound amplitude. The present results indicate that the best productivity of lactic acid can be achieved at appropriate fermentation conditions, including a suitable amplitude of ultrasound and supplementation of date syrup.

Identifiants

pubmed: 35026006
pii: 6506449
doi: 10.1093/femsle/fnac005
pii:
doi:

Substances chimiques

Lactic Acid 33X04XA5AT

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

© The Author(s) 2022. Published by Oxford University Press on behalf of FEMS.

Auteurs

Dornoush Jafarpour (D)

Department of Food Science and Technology, Faculty of Agriculture, Fasa Branch, Islamic Azad University, Fasa, Iran.

Seyed Mohammad Bagher Hashemi (SMB)

Department of Food Science and Technology, Faculty of Agriculture, Fasa University, Fasa, Iran.

Elahe Abedi (E)

Department of Food Science and Technology, Faculty of Agriculture, Fasa University, Fasa, Iran.

Maryam Mousavifard (M)

Department of Civil Engineering, Faculty of Engineering, Fasa University, Fasa, Iran.

Mehran Sayadi (M)

Department of Food Safety and Hygiene, School of Health, Fasa University of Medical Sciences, Fasa, Iran.

Articles similaires

Selecting optimal software code descriptors-The case of Java.

Yegor Bugayenko, Zamira Kholmatova, Artem Kruglov et al.
1.00
Software Algorithms Programming Languages
1.00
Humans Magnetic Resonance Imaging Brain Infant, Newborn Infant, Premature
Humans Algorithms Software Artificial Intelligence Computer Simulation

Unsupervised learning for real-time and continuous gait phase detection.

Dollaporn Anopas, Yodchanan Wongsawat, Jetsada Arnin
1.00
Humans Gait Neural Networks, Computer Unsupervised Machine Learning Walking

Classifications MeSH