Comparative analyses of response surface methodology and artificial neural network on medium optimization for Tetraselmis sp. FTC209 grown under mixotrophic condition
Mixotrophic metabolism was evaluated as an option to augment the growth and lipid production of marine microalga Tetraselmis sp. FTC 209. In this study, a five-level three-factor central composite design (CCD) was implemented in order to enrich the W-30 algal growth medium. Response surface methodol...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Hindawi Publishing Corporation
2013
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| Online Access: | http://psasir.upm.edu.my/id/eprint/28100/1/948940.pdf |
| _version_ | 1825947250216402944 |
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| author | Mohamed, Mohd Shamzi Tan, Joo Shun Mohamad, Rosfarizan Mokhtar, Mohd Noriznan Ariff, Arbakariya |
| author_facet | Mohamed, Mohd Shamzi Tan, Joo Shun Mohamad, Rosfarizan Mokhtar, Mohd Noriznan Ariff, Arbakariya |
| author_sort | Mohamed, Mohd Shamzi |
| collection | UPM |
| description | Mixotrophic metabolism was evaluated as an option to augment the growth and lipid production of marine microalga Tetraselmis sp. FTC 209. In this study, a five-level three-factor central composite design (CCD) was implemented in order to enrich the W-30 algal growth medium. Response surface methodology (RSM) was employed to model the effect of three medium variables, that is, glucose (organic C source), NaNO(primary N source), and yeast extract (supplementary N, amino acids, and vitamins) on biomass concentration, Xmax, and lipid yield, Pmax/Xmax. RSM capability was also weighed against an artificial neural network (ANN) approach for predicting a composition that would result in maximum lipid productivity, Prlipid. A quadratic regression from RSM and a Levenberg-Marquardt trained ANN network composed of 10 hidden neurons eventually produced comparable results, albeit ANN formulation was observed to yield higher values of response outputs. Finalized glucose (24.05 g/L), NaNO(4.70 g/L), and yeast extract (0.93 g/L) concentration, affected an increase of Xmax to 12.38 g/L and lipid a accumulation of 195.77 mg/g dcw. This contributed to a lipid productivity of 173.11 mg/L per day in the course of two-week cultivation. |
| first_indexed | 2024-03-06T08:10:21Z |
| format | Article |
| id | upm.eprints-28100 |
| institution | Universiti Putra Malaysia |
| language | English |
| last_indexed | 2024-03-06T08:10:21Z |
| publishDate | 2013 |
| publisher | Hindawi Publishing Corporation |
| record_format | dspace |
| spelling | upm.eprints-281002016-04-22T08:01:10Z http://psasir.upm.edu.my/id/eprint/28100/ Comparative analyses of response surface methodology and artificial neural network on medium optimization for Tetraselmis sp. FTC209 grown under mixotrophic condition Mohamed, Mohd Shamzi Tan, Joo Shun Mohamad, Rosfarizan Mokhtar, Mohd Noriznan Ariff, Arbakariya Mixotrophic metabolism was evaluated as an option to augment the growth and lipid production of marine microalga Tetraselmis sp. FTC 209. In this study, a five-level three-factor central composite design (CCD) was implemented in order to enrich the W-30 algal growth medium. Response surface methodology (RSM) was employed to model the effect of three medium variables, that is, glucose (organic C source), NaNO(primary N source), and yeast extract (supplementary N, amino acids, and vitamins) on biomass concentration, Xmax, and lipid yield, Pmax/Xmax. RSM capability was also weighed against an artificial neural network (ANN) approach for predicting a composition that would result in maximum lipid productivity, Prlipid. A quadratic regression from RSM and a Levenberg-Marquardt trained ANN network composed of 10 hidden neurons eventually produced comparable results, albeit ANN formulation was observed to yield higher values of response outputs. Finalized glucose (24.05 g/L), NaNO(4.70 g/L), and yeast extract (0.93 g/L) concentration, affected an increase of Xmax to 12.38 g/L and lipid a accumulation of 195.77 mg/g dcw. This contributed to a lipid productivity of 173.11 mg/L per day in the course of two-week cultivation. Hindawi Publishing Corporation 2013 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/28100/1/948940.pdf Mohamed, Mohd Shamzi and Tan, Joo Shun and Mohamad, Rosfarizan and Mokhtar, Mohd Noriznan and Ariff, Arbakariya (2013) Comparative analyses of response surface methodology and artificial neural network on medium optimization for Tetraselmis sp. FTC209 grown under mixotrophic condition. The Scientific World Journal, 2013. art. no. 948940. pp. 1-14. ISSN 2356-6140; ESSN: 1537-744X http://www.hindawi.com/journals/tswj/2013/948940/abs/ 10.1155/2013/948940 |
| spellingShingle | Mohamed, Mohd Shamzi Tan, Joo Shun Mohamad, Rosfarizan Mokhtar, Mohd Noriznan Ariff, Arbakariya Comparative analyses of response surface methodology and artificial neural network on medium optimization for Tetraselmis sp. FTC209 grown under mixotrophic condition |
| title | Comparative analyses of response surface methodology and artificial neural network on medium optimization for Tetraselmis sp. FTC209 grown under mixotrophic condition |
| title_full | Comparative analyses of response surface methodology and artificial neural network on medium optimization for Tetraselmis sp. FTC209 grown under mixotrophic condition |
| title_fullStr | Comparative analyses of response surface methodology and artificial neural network on medium optimization for Tetraselmis sp. FTC209 grown under mixotrophic condition |
| title_full_unstemmed | Comparative analyses of response surface methodology and artificial neural network on medium optimization for Tetraselmis sp. FTC209 grown under mixotrophic condition |
| title_short | Comparative analyses of response surface methodology and artificial neural network on medium optimization for Tetraselmis sp. FTC209 grown under mixotrophic condition |
| title_sort | comparative analyses of response surface methodology and artificial neural network on medium optimization for tetraselmis sp ftc209 grown under mixotrophic condition |
| url | http://psasir.upm.edu.my/id/eprint/28100/1/948940.pdf |
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