Optimization of Multi-Strain Solid State Fermentation to Improve the Content of Soybean Meal Protein by Response Surface Analysis

Guo Hao Yang; Jun Jun Guan; Jin Shui Wang; Feng Jia

doi:10.4028/www.scientific.net/AMR.690-693.1234

Paper Titles

Influence of Cortical Layer Thickness of Alveolar Bone on Stress Distribution over Implant Supported Region
p.1212

Kinetics Study of Vitamin A Precursor Synthesis by Immobilized Lipase-Catalyzed Regioselective Monoacetylation in n-Hexane
p.1218

Mechanism of Peptide Changes during Drying Fermented Soybean Meal
p.1224

Numerical Simulation of Cell Growth Pattern and Determination of Fractal Dimension of Cell Cluster
p.1229

Optimization of Multi-Strain Solid State Fermentation to Improve the Content of Soybean Meal Protein by Response Surface Analysis
p.1234

Optimization of Solid State Fermentation to Improve the Degree of Hydrolysis Soybean Meal Protein
p.1239

Preparation and Characteristics of Chitin-Gelatin Composite Film
p.1243

Prokaryotic Expression and Purification of Tomato Cnr Protein
p.1247

Protein Extraction of the Long-Term Room Temperature Storage Wheat Straw
p.1252

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 690-693Optimization of Multi-Strain Solid State...

Optimization of Multi-Strain Solid State Fermentation to Improve the Content of Soybean Meal Protein by Response Surface Analysis

Abstract:

The aim of this study was to optimize the variables which affect the protein content of fermented soybean meal (FSBM) with multi-strain in solid state condition. Response surface methodology (Box-behnken design) was used to design the experiments. Also, the effects of each factor and their interactions on protein content were analyzed. Regression equation between the influencing factors (moisture content, inoculation ratio and fermentation period) and the response value (protein content) was established and the results revealed that the optimal conditions were 56% moisture content, 9.5% inoculation ratio and fermentation periods was 43.5 h. Under these conditions, the actual protein content of FSBM reached 56.96%, which was very close to the predicted value (57.08%). The good correlation between predicted and actual values proved the validity of the response model.

You might also be interested in these eBooks

Materials Design, Processing and Applications

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 690-693)

Pages:

1234-1238

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.690-693.1234

Citation:

Cite this paper

Online since:

May 2013

Authors:

Guo Hao Yang, Jun Jun Guan, Jin Shui Wang, Feng Jia

Keywords:

Compound Strains, Fermentation, Optimization, Solid, Soybean Meal

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Y.H. Huang, Y.J. Lai and C.C. Chou: Journal of Bioscience and Bioengineering Vol. 112 (2011), p.49

Google Scholar

[2] I.M. Chung, S.H. Seo, J.K. Ahn and S.H. Kim: Food Chemistry Vol. 127 (2011), p.960

Google Scholar

[3] G.. Francis, H.P.S. Makkar and K. Becker: Aquaculture Vol. 199 (2001), p.197

Google Scholar

[4] L.K. Karr-Lilienthal, C.T. Kadzere, C.M. Grieshopc and G.C. Fahey Jr: Livestock Production Science Vol. 97 (2005), p.1

Google Scholar

[5] S. Refstie, S. Sahlstrom, E. Brathen, G. Baeverfjord, and P. Krogedal: Aquaculture Vol. 246 (2005), p.331

Google Scholar

[6] D. Teng, M. Gao, Y.L. Yang, B. Liu, Z.G. Tian and J.H. Wang: Biocatalysis and Agricultural Biotechnology Vol. 1 (2012), p.32

Google Scholar

[7] K. Helrich: Official methods of analysis of the association of official analytical chemists (Association of Ofﬁcial Analytical Chemists Inc., U.S 1990).

DOI: 10.5962/bhl.title.44636

Google Scholar

[8] Z. Zhu, G. Y Zhang, Y. Luo, W. Ran and Q.R Shen: Bioresource Technology Vol. 112 (2012), p.254

Google Scholar