| Title |
Optimization of biomass, vitamins, and carotenoid yield on light energy in a flat‐panel reactor using the A‐stat technique
|
|---|---|
| Published in |
Biotechnology & Bioengineering, December 2004
|
| DOI | 10.1002/bit.20346 |
| Pubmed ID | |
| Authors |
Maria J. Barbosa, Jan Willem Zijffers, Adrian Nisworo, Woulter Vaes, Jan van Schoonhoven, René H. Wijffels |
| Abstract |
Acceleration-stat (A-stat) cultivations in which the dilution rate is continuously changed at a constant acceleration rate, leading to different average light intensities inside the photobioreactor, can supply more information and reduce experimental time compared with chemostat cultivations. The A-stat was used to optimize the biomass and product yield of continuous cultures of the microalgae D. tertiolecta in a flat-panel reactor. In this study, four different accelerations were studied, a pseudo steady state was maintained at acceleration rates of 0.00016 and 0.00029 h(-2) and results were similar to those of the chemostat. An increase in the acceleration rate led to an increase in the deviation between results obtained in the A-stat and in the chemostats. We concluded that it is advantageous to use the A-stat instead of chemostats to determine culture characteristics and optimize a specific photobioreactor. The effect of average light intensity inside the photobioreactor on the production of vitamins C and E, lutein, and beta-carotene was studied using the A-stat. The highest concentrations of these products were 3.48 +/- 0.46, 0.33 +/- 0.06, 5.65 +/- 0.24, and 2.36 +/- 0.38 mg g(-1), respectively. These results were obtained at different average light intensities, showing the importance of optimizing each product on light intensity. |
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Mendeley demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Netherlands | 2 | 1% |
| Italy | 2 | 1% |
| Estonia | 2 | 1% |
| United States | 1 | <1% |
| Sweden | 1 | <1% |
| Portugal | 1 | <1% |
| Iran, Islamic Republic of | 1 | <1% |
| Spain | 1 | <1% |
| Germany | 1 | <1% |
| Other | 3 | 2% |
| Unknown | 170 | 92% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 37 | 20% |
| Student > Master | 30 | 16% |
| Student > Ph. D. Student | 29 | 16% |
| Student > Bachelor | 11 | 6% |
| Other | 9 | 5% |
| Other | 26 | 14% |
| Unknown | 43 | 23% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 54 | 29% |
| Engineering | 34 | 18% |
| Chemical Engineering | 12 | 6% |
| Biochemistry, Genetics and Molecular Biology | 9 | 5% |
| Environmental Science | 8 | 4% |
| Other | 17 | 9% |
| Unknown | 51 | 28% |