Evaluation of Isochrysis galbana (clone T-ISO) cell numbers by digital image analysis of color intensity

Abstract

In microalgal cultivation, measuring cell numbers as a means to monitor growth rates is a long-standing problem. Many automated counting systems and schemes have been developed; among these are image analysis systems. However, such imaging systems have presented difficulties in dealing with the complexities of computer recognition of individual microscopic cells. It is known that the coloration of microalgae suspension is species specific and that color intensity increases are typically associated with increasing numbers. Using this qualitative insight, the present work describes the design, construction, and comparative performance of an inexpensive digital imaging system optimized for counting microalgal cells. The system circumvents the need to count individual cells and extracts cell numbers directly from the macroscopic color intensity of a microalgal suspension. The results suggest, using Isochrysis galbana (T-ISO) as an illustrative example, that this scheme is potentially useful for inexpensive and automated biomonitoring of microalgal cell numbers. Percentage difference comparisons with a standard Coulter Counter indicated that the three algorithms tested provided better than 10% accuracy over density thresholds of 1.52× 10 6 to 8.10×10 6 cells mL −1 with precision of 4% attainable at high density concentrations.

Continuous cultivation of photosynthetic microorganisms: Approaches, applications and future trends

“…Other approach for on-line monitoring of biomass properties (concentration and composition), growth rate, photosynthetic efficiency and yield is the use of software sensors (Briassoulis et al, 2010;Córdoba-Matson et al, 2009;Ifrim et al, 2014;Jung and Lee, 2006;Li et al, 2003;Su et al, 2003).…”

“…The two main approaches for modeling the photosynthetic microorganisms growth are based in a modified Monod equation (Baquerisse et al, 1999;Becerra-Celis et al, 2008) or in the Droop model (Droop, 1968;Mairet et al, 2011;Toroghi et al, 2013). The majority of the software sensors developed for photosynthetic microorganisms are for the estimation of biomass concentration (Havlik et al, 2013a(Havlik et al, , 2013b using models that relate the biomass concentration with different parameters such as: pH (Berenguel et al, 2004;Ifrim et al, 2014), dissolved oxygen , local irradiance , solar irradiation (Quinn et al, 2011) and image analysis (Córdoba-Matson et al, 2009;Jung and Lee, 2006). Due to their complexity and multi parameter influence, mathematical models that incorporate the estimation of biomass composition are less common, and are usually applied in situations where specific nutritional conditions are set (Klok et al, 2013a(Klok et al, , 2013bMairet et al, 2011;Quinn et al, 2011).…”

Lumostatic strategy for microalgae cultivation utilizing image analysis and chlorophyll a content as design parameters

“…Microalgae have gained considerable attention and have been studied over the last decade by virtue of their high productivity, carbon sequestration potential and oil yield (Córdoba-Matson et al, 2010;Degrenne et al, 2011;Jena et al, 2011;Kliphuis et al, 2010). Microalgae are found to have higher photosynthetic efficiencies than most photoautotrophic organisms and have 10-50 times higher carbon removal efficiencies than other terrestrial plants (Li et al, 2008).…”

Monitoring of Microalgal Processes

“…Software sensors estimate the biomass concentration using correlations and measurements of local irradiance [254], color images of light distribution in a photobioreactor [245], and macroscopic color intensity in a cultivation flask [242]. Biomass concentration is estimated together with specific growth rate, PE, and average light intensity using a growth model and DO measurement [66].…”

“…This strategy does not analyze individual cells and is noninvasive because images of the photobioreactor from the outside are used. Examples include the use of simple correlations of the color intensity in a cultivation flask to estimate biomass concentration [242] and the extraction of the blue component from segmented images of the whole photobioreactor [243]. Another example is the capture of color images of a photobioreactor and using the average gray value or using ANNs to infer concentration values from intensities at selected local points [244,245].…”