Screening and Optimization of Squalene Production from Microalgae Aurantiochytrium sp.
Microalgae are an alternative potential source of squalene because they grow rapidly, relatively easy to culture and accumulate large amounts of squalene. The objectives of this research are to isolate and screen squalene producing heterotrophic microalgae from mangrove forests of Thailand and to op...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | บทความวารสาร |
| Language: | English |
| Published: |
Science Faculty of Chiang Mai University
2019
|
| Online Access: | http://it.science.cmu.ac.th/ejournal/dl.php?journal_id=8951 http://cmuir.cmu.ac.th/jspui/handle/6653943832/64061 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Institution: | Chiang Mai University |
| Language: | English |
| Summary: | Microalgae are an alternative potential source of squalene because they grow rapidly, relatively easy to culture and accumulate large amounts of squalene. The objectives of this research are to isolate and screen squalene producing heterotrophic microalgae from mangrove forests of Thailand and to optimize culture conditions. Five hundred and eighty-five strains of the microalgae were isolated from the mangrove forests along the gulf of Thailand and southern, Thailand. After screening, the isolate S02-459 was selected for further studied due to its high squalene content. Sequencing of partial 18S rRNA gene revealed that it belong to the genus Aurantiochytrium. Evaluation of eight factors influencing growth and squalene accumulation, i.e., glucose concentration, yeast extract, peptone, monosodium glutamate, pH, shaking speed, salinity and temperature were carried out by using Plackett-Burman design. Four factors, namely, glucose concentration, yeast extract concentration, peptone concentration and shaking speed were significantly (p<0.05) affected biomass and squalene production. Central Composite design was employed for optimized these four parameters for enhancement the squalene production. These factors were assessed using quadratic model. The coefficient of determination (R2) of more than 0.9 and p-value less than 0.05 indicated that the model was acceptable. The optimized conditions from CCD showed that 30 g/L of glucose, 5.0 g/L of yeast extract, 20 g/L of peptone and shaking speed at 75 rpm were appropriate. Verifications of the predicted values were 41.19±1.86 mg/L of squalene production and 10.85±0.10 mg/g cell dry weight of squalene content. The deviation of experimental values and predicted values for squalene production and content were -1.81% and -6.78%, respectively. |
|---|