Development of Multivariate Model on the Effect of Different Parameters on Synechococcus sp. PCC 7002 Growth
Understanding of the correlative effects of combined variables on the growth rate of the cyanobacteria is fundamental to the exploitation of cyanobacteria as a biological mechanism to produce biofuels. Cyanobacteria (blue-green algae) are phototrophic microorganisms that offers attractive benefits,...
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| Format: | Monograph |
| Language: | English |
| Published: |
2017
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/57238/1/EDW%20B14-133-1018%20END%20OF%20PROJECT%20REPORT%20FORM.pdf http://irep.iium.edu.my/57238/ |
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| Summary: | Understanding of the correlative effects of combined variables on the growth rate of the cyanobacteria is fundamental to the exploitation of cyanobacteria as a biological mechanism to produce biofuels. Cyanobacteria (blue-green algae) are phototrophic microorganisms that offers attractive benefits, among which is a direct conversion of CO2 to a range of valuable products such as carbon-based biofuels. One model of cyanobacteria species is the cyanobacterium Synechococcus sp. PCC 7002. This paper describes the model developed to investigate the combined impacts of the variables on the growth of the Synechococcus sp. PCC 7002. The variables understudy include the temperature of the media, light intensity, the concentration of NaNO3, and the concentration of the NPK. The data is obtained from a lab scale study in which the Synechococcus sp. PCC 7002 underwent mutagenesis procedures. It is hypotheses that certain combination of the variables plays a key role in determining the growth rate of Synechococcus sp. 7002. The growth rate is determined through the measurement of four response variables, carbohydrate concentration, percentage of CO2 uptake, cell dry weight (CDW), and optical density (OD). Two multivariate models were developed: a principal component analysis model (PCA) which unearths the underlying relationship between the variables, and a partial least squares (PLS) model which demonstrates the variances and correlations between the variables and the responses. Promising results were yield from the proposed models. Distinctive correlations between the variables were clearly described by the PCA model whilst the PLS model enables a reliable prediction of the response variables.
Key words: Principal Component Analysis (PCA), Partial Least Squares (PLS), cyanobacteria, Synechococcus, growth. |
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