Optimization of the fermentation process for fructosyltransferase production by Aspergillus niger FS054

Abstract This study systematically optimized the fermentation process for fructosyltransferase (FTase) production by Aspergillus niger FS054, integrating traditional experimental designs with machine learning approaches. Single–factor experiments initially identified critical medium components (carb...

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Main Authors: Yingzi Wu, Yuewen Zhang, Xiaoyu Zhong, Huiling Xia, Mingyang Zhou, Wenjin He, Yi Zheng
Format: Article
Language:English
Published: BMC 2025-07-01
Series:Microbial Cell Factories
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Online Access:https://doi.org/10.1186/s12934-025-02798-7
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author Yingzi Wu
Yuewen Zhang
Xiaoyu Zhong
Huiling Xia
Mingyang Zhou
Wenjin He
Yi Zheng
author_facet Yingzi Wu
Yuewen Zhang
Xiaoyu Zhong
Huiling Xia
Mingyang Zhou
Wenjin He
Yi Zheng
author_sort Yingzi Wu
collection DOAJ
description Abstract This study systematically optimized the fermentation process for fructosyltransferase (FTase) production by Aspergillus niger FS054, integrating traditional experimental designs with machine learning approaches. Single–factor experiments initially identified critical medium components (carbon source, nitrogen sources, phosphate, and metal ions) and cultivation parameters (pH, liquid volume, inoculum size, temperature, and shaking speed). Subsequent Plackett–Burman screening identified sucrose, yeast extract paste, and $$\hbox {NH}_4\hbox {Cl}$$ NH 4 Cl as the most influential medium factors. Through Box–Behnken response surface methodology (RSM), the optimal medium composition was determined as sucrose 156.65 g/L, yeast extract paste 42 g/L, and $$\hbox {NH}_4\hbox {Cl}$$ NH 4 Cl 1.68 g/L, yielding an enzyme activity of 3249.00 ± 24.39 U/L (99.16% agreement with RSM predictions). Further optimization of cultivation conditions using a hybrid backpropagation neural network–genetic algorithm (BP–GA) model identified optimal parameters as pH 5.5, a liquid volume of 96.6 mL (in a 250 mL shaker), and inoculum size of 2.4 $$\times$$ × $$10^{4}$$ 10 4 spores/mL, achieving a final enzyme activity of 3422.14 ± 36.86 U/L (1.1% deviation from the predicted 3460 U/L), representing a 4.2-fold increase over initial conditions. This work demonstrates the synergistic application of classical experimental design and artificial intelligence, significantly enhancing FTase productivity and potentially offering a more economical enzyme source for industrial–scale fructooligosaccharide (FOS) biosynthesis.
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spelling doaj-art-e5168ceff5aa4b5bb641dbb2a3807c862025-08-20T03:06:27ZengBMCMicrobial Cell Factories1475-28592025-07-0124111610.1186/s12934-025-02798-7Optimization of the fermentation process for fructosyltransferase production by Aspergillus niger FS054Yingzi Wu0Yuewen Zhang1Xiaoyu Zhong2Huiling Xia3Mingyang Zhou4Wenjin He5Yi Zheng6College of Life Sciences, Fujian Normal UniversityCollege of Life Sciences, Fujian Normal UniversityCollege of Life Sciences, Fujian Normal UniversityCollege of Life Sciences, Fujian Normal UniversityCollege of Life Sciences, Fujian Normal UniversityCollege of Life Sciences, Fujian Normal UniversityCollege of Life Sciences, Fujian Normal UniversityAbstract This study systematically optimized the fermentation process for fructosyltransferase (FTase) production by Aspergillus niger FS054, integrating traditional experimental designs with machine learning approaches. Single–factor experiments initially identified critical medium components (carbon source, nitrogen sources, phosphate, and metal ions) and cultivation parameters (pH, liquid volume, inoculum size, temperature, and shaking speed). Subsequent Plackett–Burman screening identified sucrose, yeast extract paste, and $$\hbox {NH}_4\hbox {Cl}$$ NH 4 Cl as the most influential medium factors. Through Box–Behnken response surface methodology (RSM), the optimal medium composition was determined as sucrose 156.65 g/L, yeast extract paste 42 g/L, and $$\hbox {NH}_4\hbox {Cl}$$ NH 4 Cl 1.68 g/L, yielding an enzyme activity of 3249.00 ± 24.39 U/L (99.16% agreement with RSM predictions). Further optimization of cultivation conditions using a hybrid backpropagation neural network–genetic algorithm (BP–GA) model identified optimal parameters as pH 5.5, a liquid volume of 96.6 mL (in a 250 mL shaker), and inoculum size of 2.4 $$\times$$ × $$10^{4}$$ 10 4 spores/mL, achieving a final enzyme activity of 3422.14 ± 36.86 U/L (1.1% deviation from the predicted 3460 U/L), representing a 4.2-fold increase over initial conditions. This work demonstrates the synergistic application of classical experimental design and artificial intelligence, significantly enhancing FTase productivity and potentially offering a more economical enzyme source for industrial–scale fructooligosaccharide (FOS) biosynthesis.https://doi.org/10.1186/s12934-025-02798-7FructosyltransferaseAspergillus nigerFermentation optimizationFructooligosaccharides
spellingShingle Yingzi Wu
Yuewen Zhang
Xiaoyu Zhong
Huiling Xia
Mingyang Zhou
Wenjin He
Yi Zheng
Optimization of the fermentation process for fructosyltransferase production by Aspergillus niger FS054
Microbial Cell Factories
Fructosyltransferase
Aspergillus niger
Fermentation optimization
Fructooligosaccharides
title Optimization of the fermentation process for fructosyltransferase production by Aspergillus niger FS054
title_full Optimization of the fermentation process for fructosyltransferase production by Aspergillus niger FS054
title_fullStr Optimization of the fermentation process for fructosyltransferase production by Aspergillus niger FS054
title_full_unstemmed Optimization of the fermentation process for fructosyltransferase production by Aspergillus niger FS054
title_short Optimization of the fermentation process for fructosyltransferase production by Aspergillus niger FS054
title_sort optimization of the fermentation process for fructosyltransferase production by aspergillus niger fs054
topic Fructosyltransferase
Aspergillus niger
Fermentation optimization
Fructooligosaccharides
url https://doi.org/10.1186/s12934-025-02798-7
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AT yuewenzhang optimizationofthefermentationprocessforfructosyltransferaseproductionbyaspergillusnigerfs054
AT xiaoyuzhong optimizationofthefermentationprocessforfructosyltransferaseproductionbyaspergillusnigerfs054
AT huilingxia optimizationofthefermentationprocessforfructosyltransferaseproductionbyaspergillusnigerfs054
AT mingyangzhou optimizationofthefermentationprocessforfructosyltransferaseproductionbyaspergillusnigerfs054
AT wenjinhe optimizationofthefermentationprocessforfructosyltransferaseproductionbyaspergillusnigerfs054
AT yizheng optimizationofthefermentationprocessforfructosyltransferaseproductionbyaspergillusnigerfs054