Volume 9, Issue 1 (2018)                   JMBS 2018, 9(1): 117-122 | Back to browse issues page

XML Persian Abstract Print

Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Zahedi A, Rashvand M. M. Feasibility of Optimum Temperature for Growth of Nannochloropsis Oculata Microalga by Using Image Processing System. JMBS 2018; 9 (1) :117-122
URL: http://biot.modares.ac.ir/article-22-24308-en.html
1- Energy SystemsDepartment, New Technologies Faculty, Iran University of Science & Technology, Tehran, Iran, Iran University of Science & Technology, Daneshgah Street, Hengam Street, Resalat Square, Tehran, Iran. Postal Code: 1684613114
2- Agricultural Machinery Department, Agricultural Engineering & Technology Faculty, Tehran University, Karaj, Iran
Abstract:   (4095 Views)
Aims: Biodiesel is considered as a clean fuel, because it is free of any aromatic compound. In recent years, in order to reduce the cost of production of Biodiesel, many studies have been conducted on the extraction of biofuels from microalgae around the world. Thus, this study was conducted with the aim of investigating the feasibility of optimum temperature for growth of Nannochloropsis Oculata microalga by using image processing system.
Materials and Methods: In this experimental study, a piece of Nannochloropsis Oculata microalga containing 100,000 cells per ml was cultured in 15°C, 20°C, and 25°C. In order to evaluate the growth rate, active microalgae were sampled at 24-hour intervals, and their growth was studied, using machine vision systems. The data were analyzed, using Matlab 2012 and Weka 3 software by multivariable analysis of variance, linear regression algorithm, multilayer perceptron, Gaussian processing and simple linear regression analysis.
Findings: The maximum cell density of Nannochloropsis Oculata on the 8th day was 286.23×104±0.38×105 cells per ml in treatment at 25°C and the minimum cell density was 168.58×104±0.48×105 cells per ml in treatment at 15°C. Specific growth rate was significantly increased at temperature of 25°C compared to the treatments at 15°C and 20°C. Linear regression algorithms (r2=0.84), multilayer perceptron (r2=0.88) and Gaussian processing (r2=0.78) showed good results, but simple linear regression indicated that the algorithm was unsuccessful (r2=0.45).
Conclusion: The image processing technique provides a successful estimation of the growth process of Nannochloropsis Oculata at different temperature levels.
Full-Text [PDF 353 kb]   (2872 Downloads)    
Subject: Agricultural Biotechnology
Received: 2016/06/15 | Accepted: 2017/10/13 | Published: 2018/03/20

1. Nejat Veziroğlu T, Şahin S. 21st Century's energy: Hydrogen energy system. Energy Convers Manag. 2008;49(7):1820-31. [Link] [DOI:10.1016/j.enconman.2007.08.015]
2. Smoot LD. Coal combustion. In: Bisio A. The wiley encyclopedia of energy and the environment. 1st Volume. Bisio A, Boots S, editors. New Jersey: Wiley; 1997. pp. 405-13. [Link]
3. Qu L, Wang Z, Zhang J. Influence of waste cooking oil biodiesel on oxidation reactivity and nanostructure of particulate matter from diesel engine. Fuel. 2016;181:389-95. [Link] [DOI:10.1016/j.fuel.2016.04.113]
4. Huang G, Chen F, Wei D, Zhang XW, Chen G. Biodiesel production by microalgal biotechnology. Appl Energy. 2010;87(1):38-46. [Link] [DOI:10.1016/j.apenergy.2009.06.016]
5. Metting FB. Biodiversity and application of microalgae. J Ind Microbiol Biotechnol. 1996;17(5-6):477-89. [Link] [DOI:10.1007/BF01574779]
6. Yan C, Mu-oz R, Zhu L, Wang Y. The effects of various LED (light emitting diode) lighting strategies on simultaneous biogas upgrading and biogas slurry nutrient reduction by using of microalgae Chlorella sp. Energy. 2016;106:554-61. [Link] [DOI:10.1016/j.energy.2016.03.033]
7. Demirbas A. Progress and recent trend in biodiesel fuels. Energy Convers Manag. 2009;50(1):14-34. [Link] [DOI:10.1016/j.enconman.2008.09.001]
8. Sheehan J, Camobreco V, Duffield J, Graboski M, Shapouri H. An overview of biodiesel and petroleum diesel life cycles [Internet]. Colorado: National Renewable Energy Laboratory; 1998 [cited 2016 Sep 20]. Available from: https://www.nrel.gov/docs/legosti/fy98/24772.pdf https://doi.org/10.2172/1218369 [Link] [DOI:10.2172/1218368]
9. Li X, Hu HY, Zhang YP. Growth and lipid accumulation properties of a freshwater microalga Scenedesmus sp. under different cultivation temperature. Bioresour Technol. 2011;102(3):3098-102. [Link] [DOI:10.1016/j.biortech.2010.10.055]
10. Griffiths MJ, Harrison STL. Lipid productivity as a key characteristic for choosing algal species for biodiesel production. J Appl Phycol. 2009;21(5):493-507. [Link] [DOI:10.1007/s10811-008-9392-7]
11. Han F, Pei H, Hu W, Zhang S, Han L, Ma G. The feasibility of ultrasonic stimulation on microalgae for efficient lipid accumulation at the end of the logarithmic phase. Algal Res. 2016;16:189-94. [Link] [DOI:10.1016/j.algal.2016.03.014]
12. Kim SK, editor. Handbook of marine microalgae: Biotechnology advances. Amsterdam: Elsevier Science; 2015. [Link]
13. Ma X, Liu J, Liu B, Chen T, Yang B, Chen F. Physiological and biochemical changes reveal stress-associated photosynthetic carbon partitioning into triacylglycerol in the oleaginous marine alga Nannochloropsis oculata. Algal Res. 2016;16:28-35. [Link] [DOI:10.1016/j.algal.2016.03.005]
14. Omid M, Mahmoudi A, Omid MH. Development of pistachio sorting system using principal component analysis (PCA) assisted artificial neural network (ANN) of impact acoustics. Expert Syst Appl. 2010;37(10):7205-12. [Link] [DOI:10.1016/j.eswa.2010.04.008]
15. Akbarpour E, Pazir MK, Zendehboudi AA. The effects of different concentration of salinities on the biochemical components and growth rate of single cell microalgae, Tetraselmis chuii. Iran Sci Fish J. 2014;23(1):9-22. [Persian] [Link]
16. Attaran Fariman G, Roozitalab M, Zadabas Shahabadi H, Sharifian S. Effects of salinity on growth and fatty acid composition of green microalgae Dunaliella bardawil as a candidate source for biofuel production. J Aquat Ecolo. 2015;4(3):50-61. [Persian] 23- Malakootian M, Hatami B, Dolatshahi S, Rajabizade A. Determination of optimal temperature of Nannochloropsis oculata microalgae for biofuel production (biodiesel). Energy Manag. 2014;4(1):62-9. [Link]
17. Soulièsa A, Pruvost J, Castelain C, Burghelea T. Microscopic flows of suspensions of the green non-motile Chlorella micro-alga at various volume fractions: Applications to intensified photobioreactors. J Non-Newton Fluid Mech. 2016;231:91-101. [Link] [DOI:10.1016/j.jnnfm.2016.03.012]
18. Yam KA, Papadakis SE. A simple digital imaging method for measuring and analyzing color of food surfaces. J Food Eng. 2004;61(1):137-42. [Link] [DOI:10.1016/S0260-8774(03)00195-X]
19. Almeida SGM, Guimarães FG, Ramírez JA. Feature extraction in Brazilian Sign Language Recognition based on phonological structure and using RGB-D sensors. Expert Syst Appl. 2014;41(16):7259-71. [Link] [DOI:10.1016/j.eswa.2014.05.024]
20. Pedreschi F, León J, Mery D, Moyano P. Development of a computer vision system to measure the color of potato chips. Food Res Int. 2006;39(10):1092-8. [Link] [DOI:10.1016/j.foodres.2006.03.009]
21. Neuhauser S, Handler J. Colour analysis of the equine endometrium: Comparison of spectrophotometry and computer-assisted analysis of photographs within the L*a*b* colour space system. Vet J. 2013;197(3):753-60. [Link] [DOI:10.1016/j.tvjl.2013.04.013]
22. Teimouri N, Omid M, Mollazade K, Rajabipour A. A novel artificial neural networks assisted segmentation algorithm for discriminating almond nut and shell from background and shadow. Comput Electron Agric. 2014;105:34-43. [Link] [DOI:10.1016/j.compag.2014.04.008]
23. Malakootian M, Hatami B, Dolatshahi S, Rajabizade A. Determination of optimal temperature of Nannochloropsis oculata microalgae for biofuel production (biodiesel). Energy Manag. 2014;4(1):62-9. [Link]

Add your comments about this article : Your username or Email:

Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.