Statistical optimization of process variables for the large-scale production of Metarhizium anisopliae conidiospores in solid-state fermentation
Optimization of conidial production was achieved by response surface methodology (RSM), a powerful mathematical approach widely applied in the optimization of fermentation process, using the three substrates; rice, barley and sorghum at variable pH, moisture content and yeast extract concentrations....
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| Veröffentlicht in: | Bioresource technology 2008-04, Vol.99 (6), p.1530-1537 |
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| creator | Bhanu Prakash, G.V.S. Padmaja, V. Siva Kiran, R.R. |
| description | Optimization of conidial production was achieved by response surface methodology (RSM), a powerful mathematical approach widely applied in the optimization of fermentation process, using the three substrates; rice, barley and sorghum at variable pH, moisture content and yeast extract concentrations. These three factors were found to be important, affecting
Metarhizium anisopliae spore production. A 2
3 full factorial central composite design and RSM were applied to determine the optimal concentration of each variable. A second-order polynomial was determined by the multiple regression analysis of the experimental data. Moisture content of 75.68% for sorghum, 73.21% for barley and 22.34% for rice produced optimal results. Maximal conidial yield was recorded for rice at a pH of 7.01; at 7.06 for sorghum and at 6.76 for barley. |
| doi_str_mv | 10.1016/j.biortech.2007.04.031 |
| format | Article |
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Metarhizium anisopliae spore production. A 2
3 full factorial central composite design and RSM were applied to determine the optimal concentration of each variable. A second-order polynomial was determined by the multiple regression analysis of the experimental data. Moisture content of 75.68% for sorghum, 73.21% for barley and 22.34% for rice produced optimal results. Maximal conidial yield was recorded for rice at a pH of 7.01; at 7.06 for sorghum and at 6.76 for barley.</description><identifier>ISSN: 0960-8524</identifier><identifier>EISSN: 1873-2976</identifier><identifier>DOI: 10.1016/j.biortech.2007.04.031</identifier><identifier>PMID: 17590331</identifier><language>eng</language><publisher>Kidlington: Elsevier Ltd</publisher><subject>Aspergillus - metabolism ; Biological and medical sciences ; Bioreactors ; Biotechnology ; Biotechnology - instrumentation ; Biotechnology - methods ; Conidiospores ; Fermentation ; Fundamental and applied biological sciences. Psychology ; Hordeum ; Hordeum vulgare ; Hydrogen-Ion Concentration ; Industrial Microbiology ; Metarhizium - metabolism ; Metarhizium anisopliae ; Methods. Procedures. Technologies ; Microbial engineering. Fermentation and microbial culture technology ; Models, Theoretical ; Oryza ; Oryza sativa ; Regression Analysis ; Reproducibility of Results ; Response surface methodology ; Solid-state fermentation ; Sorghum</subject><ispartof>Bioresource technology, 2008-04, Vol.99 (6), p.1530-1537</ispartof><rights>2007</rights><rights>2015 INIST-CNRS</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c517t-a1111ce62a50116182b626711ed1ecb240a80d664c45fa75663e8117f9303a6a3</citedby><cites>FETCH-LOGICAL-c517t-a1111ce62a50116182b626711ed1ecb240a80d664c45fa75663e8117f9303a6a3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.biortech.2007.04.031$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3550,27924,27925,45995</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=22115157$$DView record in Pascal Francis$$Hfree_for_read</backlink><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/17590331$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Bhanu Prakash, G.V.S.</creatorcontrib><creatorcontrib>Padmaja, V.</creatorcontrib><creatorcontrib>Siva Kiran, R.R.</creatorcontrib><title>Statistical optimization of process variables for the large-scale production of Metarhizium anisopliae conidiospores in solid-state fermentation</title><title>Bioresource technology</title><addtitle>Bioresour Technol</addtitle><description>Optimization of conidial production was achieved by response surface methodology (RSM), a powerful mathematical approach widely applied in the optimization of fermentation process, using the three substrates; rice, barley and sorghum at variable pH, moisture content and yeast extract concentrations. These three factors were found to be important, affecting
Metarhizium anisopliae spore production. A 2
3 full factorial central composite design and RSM were applied to determine the optimal concentration of each variable. A second-order polynomial was determined by the multiple regression analysis of the experimental data. Moisture content of 75.68% for sorghum, 73.21% for barley and 22.34% for rice produced optimal results. Maximal conidial yield was recorded for rice at a pH of 7.01; at 7.06 for sorghum and at 6.76 for barley.</description><subject>Aspergillus - metabolism</subject><subject>Biological and medical sciences</subject><subject>Bioreactors</subject><subject>Biotechnology</subject><subject>Biotechnology - instrumentation</subject><subject>Biotechnology - methods</subject><subject>Conidiospores</subject><subject>Fermentation</subject><subject>Fundamental and applied biological sciences. Psychology</subject><subject>Hordeum</subject><subject>Hordeum vulgare</subject><subject>Hydrogen-Ion Concentration</subject><subject>Industrial Microbiology</subject><subject>Metarhizium - metabolism</subject><subject>Metarhizium anisopliae</subject><subject>Methods. Procedures. Technologies</subject><subject>Microbial engineering. 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Metarhizium anisopliae spore production. A 2
3 full factorial central composite design and RSM were applied to determine the optimal concentration of each variable. A second-order polynomial was determined by the multiple regression analysis of the experimental data. Moisture content of 75.68% for sorghum, 73.21% for barley and 22.34% for rice produced optimal results. Maximal conidial yield was recorded for rice at a pH of 7.01; at 7.06 for sorghum and at 6.76 for barley.</abstract><cop>Kidlington</cop><pub>Elsevier Ltd</pub><pmid>17590331</pmid><doi>10.1016/j.biortech.2007.04.031</doi><tpages>8</tpages></addata></record> |
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| source | MEDLINE; Elsevier ScienceDirect Journals Complete |
| subjects | Aspergillus - metabolism Biological and medical sciences Bioreactors Biotechnology Biotechnology - instrumentation Biotechnology - methods Conidiospores Fermentation Fundamental and applied biological sciences. Psychology Hordeum Hordeum vulgare Hydrogen-Ion Concentration Industrial Microbiology Metarhizium - metabolism Metarhizium anisopliae Methods. Procedures. Technologies Microbial engineering. Fermentation and microbial culture technology Models, Theoretical Oryza Oryza sativa Regression Analysis Reproducibility of Results Response surface methodology Solid-state fermentation Sorghum |
| title | Statistical optimization of process variables for the large-scale production of Metarhizium anisopliae conidiospores in solid-state fermentation |
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