Optimization of glucoamylase production by Colletotrichum sp. KCP1 using statistical methodology

被引:0
|
作者
Vimal S. Prajapati
Ujjval B. Trivedi
Kamlesh C. Patel
机构
[1] Sardar Patel University,B R D School of Biosciences
来源
关键词
glucoamylase; sp. KCP1; Plackett-Burman design; response surface methodology; solid state fermentation;
D O I
暂无
中图分类号
学科分类号
摘要
Glucoamylase is a key enzyme used in the food processing as well as in commercial production of glucose from starch. A natural fungal strain identified as Colletotrichum sp. KCP1 using 18S rDNA partial genome sequencing has been studied for optimization of glucoamylase production. Media components were screened and optimized through the statistical approach for the synthesis of glucoamylase in solid state fermentation using wheat bran as the substrate. The medium components influencing the enzyme production were identified using Plackett-Burman design. Among various variables screened along with wheat bran as major growth substrate, starch, whey, and casein acid hydrolyasate were found to be most significant. The optimum concentrations of these significant parameters were determined employing the response surface central composite design, revealing starch concentration (1.5 g), whey (0.1 mL), and casein acid hydrolysate (0.1 g) per 5 g of wheat bran for highest enzyme production.
引用
收藏
页码:31 / 38
页数:7
相关论文
共 50 条
  • [21] Optimization of medium composition for alkaline protease production by Marinobacter sp. GA CAS9 using response surface methodology - A statistical approach
    Kumar, Ramamoorthy Sathish
    Ananthan, Gnanakkan
    Prabhu, Antonyraj Selva
    BIOCATALYSIS AND AGRICULTURAL BIOTECHNOLOGY, 2014, 3 (02): : 191 - 197
  • [22] Statistical optimization of culture media components for enhanced production of lipase by lipolytic yeasts, Pichia sp. and Trichosporon coremiiforme using response surface methodology
    Dar, Mudasir A.
    Loedji, Matthew Arriel Christiano
    Lunggani, Arina Tri
    Napitupulu, Toga Pangihotan
    Kanti, Atit
    Sudiana, I. Made
    BIOMASS CONVERSION AND BIOREFINERY, 2025,
  • [23] Statistical medium optimization for the production of collagenolytic protease by Pseudomonas sp SUK using response surface methodology
    Bhagwat, Prashant K.
    Jhample, Sowmya B.
    Dandge, Padma B.
    MICROBIOLOGY, 2015, 84 (04) : 520 - 530
  • [24] Optimization of lipids’ ultrasonic extraction and production from Chlorella sp. using response-surface methodology
    Bilel Hadrich
    Ismahen Akremi
    Mouna Dammak
    Mohamed Barkallah
    Imen Fendri
    Slim Abdelkafi
    Lipids in Health and Disease, 17
  • [25] Optimization of Alkaline Protease Production from Bacillus sp. by Response Surface Methodology
    Sumant Puri
    Qasim Khalil Beg
    Rani Gupta
    Current Microbiology, 2002, 44 : 286 - 290
  • [26] Optimization of Physical Factor for amylase Production by Arthrobacter sp. by Response Surface Methodology
    Kim, Hyun-do
    Im, Young-kum
    Choi, Jong-il
    Han, Se Jong
    KOREAN CHEMICAL ENGINEERING RESEARCH, 2016, 54 (01): : 140 - 144
  • [27] Optimization of protease production by Streptomyces sp. A6 using statistical approach for reclamation of shellfish waste
    Anil Kumar Singh
    H. S. Chhatpar
    World Journal of Microbiology and Biotechnology, 2010, 26 : 1631 - 1639
  • [28] Statistical optimization of lipid production by the diatom Gyrosigma sp. grown in industrial wastewater
    Govindan, Natanamurugaraj
    Maniam, Gaanty Pragas
    Yusoff, Mashitah M.
    Ab Rahim, Mohd Hasbi
    Chatsungnoen, Tawan
    Ramaraj, Rameshprabu
    Chisti, Yusuf
    JOURNAL OF APPLIED PHYCOLOGY, 2020, 32 (01) : 375 - 387
  • [29] Production and statistical optimization of cholesterol-oxidase generated by Streptomyces sp. AN strain
    Amany A. Alam
    Doaa A. Goda
    Nadia A. Soliman
    Dina I. Abdel-Meguid
    Ebaa E. El-Sharouny
    Soraya A. Sabry
    Journal of Genetic Engineering and Biotechnology, 20
  • [30] Statistical optimization of culture media for production of phycobiliprotein by Synechocystis sp. PCC 6701
    Seong-Joo Hong
    Choul-Gyun Lee
    Biotechnology and Bioprocess Engineering, 2008, 13 : 491 - 498