Microalgae biomass quantification by digital image processing and RGB color analysis


Digital image processing based on a red–green–blue (RGB) color analysis was applied to measure the cell concentration of three microalgae: Chlorella vulgaris, Botryococcus braunii, and Ettlia sp. The experiments were performed by using diluted and concentrated cultures of these microalgae to prepare different concentrations of dry cell weight (DCW). A charge-coupled device (CCD) camera was used to image the microalgae samples in a dark chamber homogenously illuminated from the bottom. The method showed to be a simple yet efficient technique for microalgae biomass estimation with an effective measurement range up to 3 g DCW L−1. Especially, the blue color value linearly decreased with DCW in this dynamic range of measurement in all the tested microalgae. The general correlation based on the conversion of RGB values to gray tones by application of a luminescence algorithm also showed similar patterns. The blue color value predicted the biomass concentrations of C. vulgaris, B. braunii, and Ettlia sp. with average errors of 13, 16, and 8 %, respectively, which were much lower than those of the gray tones conversion. Thus, the method presented in this study can be a base for the development of a more general method for microalgae biomass measurement.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5


  1. Barnard K, Funt B (2002) Camera characterization for color research. Color Res Appl 27:152–163

    Article  Google Scholar 

  2. Chen X, Goh QY, Tan W, Hossain I, Chen WN, Lau R (2011) Lumostatic strategy for microalgae cultivation utilizing image analysis and chlorophyll a content as design parameters. Bioresour Technol 102:6005–6012

    CAS  PubMed  Article  Google Scholar 

  3. Chisti Y (2007) Biodiesel from microalgae. Biotechnol Adv 25:294–306

    CAS  PubMed  Article  Google Scholar 

  4. Coltelli P, Barsanti L, Evangelista V, Frassanito AM, Passarelli V, Gualtieri P (2013) Automatic and real time recognition of microalgae by means of pigment signature and shape. Environ Sci-Proc Impac 15:1397–1410

    CAS  Article  Google Scholar 

  5. Córdoba-Matson M, Gutiérrez J, Porta-Gándara M (2010) Evaluation of Isochrysis galbana (clone T-ISO) cell numbers by digital image analysis of colour intensity. J Appl Phycol 22:427–434

    Article  Google Scholar 

  6. Dierssen HM, Kudela RM, Ryan JP, Zimmerman RC (2006) Red and black tides: quantitative analysis of water-leaving radiance and perceived colour for phytoplankton, coloured dissolved organic matter, and suspended sediments. Limnol Oceanogr 51:2646–2659

    Article  Google Scholar 

  7. Gouveia L, Oliveira AC (2009) Microalgae as a raw material for biofuels production. J Ind Microbiol Biot 36:269–274

    CAS  Article  Google Scholar 

  8. Havlik I, Lindner P, Scheper T, Reardon KF (2013) On-line monitoring of large cultivations of microalgae and cyanobacteria. Trends Biotechnol 31:406–414

    CAS  PubMed  Article  Google Scholar 

  9. Jung SK, Lee SB (2006) In situ monitoring of cell concentration in a photobioreactor using image analysis: comparison of uniform light distribution model and artificial neural networks. Biotechnol Progr 22:1443–1450

    CAS  Article  Google Scholar 

  10. Kazemipour F, Meleder V, Launeau P (2011) Optical properties of microphytobenthic biofilms (MPBOM): biomass retrieval implication. J Quant Spectrosc Radiat Transf 112:131–142

    CAS  Article  Google Scholar 

  11. Kim J, Yoo G, Lee H, Lim J, Kim K, Kim CW, Park MS, Yang JW (2013) Methods of downstream processing for the production of biodiesel from microalgae. Biotechnol Adv 31:862–876

    CAS  PubMed  Article  Google Scholar 

  12. Kumar K, Sirasale A, Das D (2013) Use of image analysis tool for the development of light distribution pattern inside the photobioreactor for the algal cultivation. Bioresour Technol 143:88–95

    CAS  PubMed  Article  Google Scholar 

  13. Largeau C, Casadevall E, Berkaloff C, Dhamelincourt P (1980) Sites of accumulation and composition of hydrocarbons in Botryococcus braunii. Phytochemistry 19:1043–1051

    CAS  Article  Google Scholar 

  14. Murphy TE, Macon K, Berberoglu H (2013) Multispectral image analysis for algal biomass quantification. Biotechnol Progr 29:808–816

    CAS  Article  Google Scholar 

  15. Murphy TE, Macon K, Berberoglu H (2014) Rapid algal culture diagnostics for open ponds using multispectral image analysis. Biotechnol Progr 30:233–240

    CAS  Article  Google Scholar 

  16. Rowan KS (1989) Photosynthetic pigments of algae. Cambridge University Press, Cambridge

    Google Scholar 

  17. Sandnes JM, Ringstad T, Wenner D, Heyerdahl PH, Källqvist T, Gislerød HR (2006) Real-time monitoring and automatic density control of large-scale microalgal cultures using near infrared (NIR) optical density sensors. J Biotechnol 122:209–215

    CAS  PubMed  Article  Google Scholar 

  18. Stanier RY, Kunisawa R, Mandel M, Cohen-Bazire G (1971) Purification and properties of unicellular blue-green algae (order Chroococcales). Bacteriol Rev 35:171–205

    CAS  PubMed Central  PubMed  Google Scholar 

  19. Uyar B (2013) A novel non-invasive digital imaging method for continuous biomass monitoring and cell distribution mapping in photobioreactors. J Chem Technol Biot 88:1144–1149

    CAS  Article  Google Scholar 

  20. Wijffels RH, Barbosa MJ (2010) An outlook on microalgal biofuels. Science 329:796–799

    CAS  PubMed  Article  Google Scholar 

  21. Yoo C, Choi G, Kim HS, Oh HM (2013) Ettlia sp. YC001 showing high growth rate and lipid content under high CO2. Bioresour Technol 127:482–488

    CAS  PubMed  Article  Google Scholar 

Download references


This research was partially supported by a grant from the Advanced Biomass R&D Center, a Global Frontier Program by the Korean Ministry of Science, ICT & Future Planning. MHS would like to thank KFAS (Korea Foundation for Advanced Studies) for supporting this work and acknowledge the financial support of the University of Tehran under grant number 8104956/1/03.

Author information



Corresponding author

Correspondence to Hee-Mock Oh.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Sarrafzadeh, M.H., La, HJ., Lee, JY. et al. Microalgae biomass quantification by digital image processing and RGB color analysis. J Appl Phycol 27, 205–209 (2015). https://doi.org/10.1007/s10811-014-0285-7

Download citation


  • Biofuels
  • Chlorophyll
  • Microbial biomass estimation
  • Photobioreactor
  • RGB color model