Multispectral image analysis for algal biomass quantification

Murphy, Thomas E.; Macon, Keith; Berberoğlu, Halil

doi:10.1002/btpr.1714

Cited by 19 publications

(18 citation statements)

References 38 publications

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“…The local areal biomass concentration, X A , expressed in grams dry weight per square meter (g/m 2 ), was measured using the multispectral imaging technique presented by Murphy et al Briefly, this method uses an RGB digital image of the biofilm to recover its biomass concentration using correlations between biomass and image color that were generated under the same background and lighting conditions as the experimental setup. The areal biomass concentration was periodically measured at six discretized regions along the length of the biofilm.…”

Section: Methodsmentioning

confidence: 99%

“…The biofilm was illuminated with diffuse fluorescent lighting with a color temperature of 4100 K, the spectral content of which was reported by Murphy et al at 10 nm resolution . The boundary conditions for light intensity in the biofilm can be written as,

\begin{matrix} I_{λ} (y = 0, θ) = (1 - r_{normalb}) G_{λ, i n} / π for 0 \leq θ \leq π / 2 \\ I_{λ} (y = L_{normalb}, θ) = r_{normalp} G_{λ}^{+} (y = L_{normalb}) / π for π / 2 \leq θ \leq π \end{matrix}

where θ is the zenith angle with respect to the normal into the biofilm.…”

Section: Modeling Analysismentioning

confidence: 99%

See 1 more Smart Citation

Flux balancing of light and nutrients in a biofilm photobioreactor for maximizing photosynthetic productivity

Murphy

Berberoğlu

2014

Biotechnology Progress

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This article reports a combined experimental and numerical study on the efficient operation of Porous Substrate Bioreactors. A comprehensive model integrating light transport, mass transport, and algal growth kinetics was used to understand the productivity of photosynthetic biofilms in response to delivery rates of photons and nutrients. The reactor under consideration was an evaporation driven Porous Substrate Bioreactor (PSBR) cultivating the cyanobacteria Anabaena variabilis as a biofilm on a porous substrate which delivers water and nutrients to the cells. In an unoptimized experimental case, this reactor was operated with a photosynthetic efficiency of 2.3%, competitive with conventional photobioreactors. Moreover, through a scaling analysis, the location at which the phosphate delivery rate decreased the growth rate to half of its uninhibited value was predicted as a function of microorganism and bioreactor properties. The numerical model along with the flux balancing techniques presented herein can serve as tools for designing and selecting operating parameters of biofilm based cultivation systems for maximum productivity.

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Section: Methodsmentioning

confidence: 99%

\begin{matrix} I_{λ} (y = 0, θ) = (1 - r_{normalb}) G_{λ, i n} / π for 0 \leq θ \leq π / 2 \\ I_{λ} (y = L_{normalb}, θ) = r_{normalp} G_{λ}^{+} (y = L_{normalb}) / π for π / 2 \leq θ \leq π \end{matrix}

where θ is the zenith angle with respect to the normal into the biofilm.…”

Section: Modeling Analysismentioning

confidence: 99%

Flux balancing of light and nutrients in a biofilm photobioreactor for maximizing photosynthetic productivity

Murphy

Berberoğlu

2014

Biotechnology Progress

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“…PAM fluorometry was used for determining the biomass concentration in a shake flask by measuring the in vivo maximum fluorescence yield F m through the flask bottom after a dark period and assuming proportionality between average F m and biomass concentration [111]. A study exists in which red, green, and blue (RGB) values in images of algal cultures taken with an RGB camera demonstrated a correlation with biomass concentration [112], but the color intensities were relatively insensitive to biomass concentration at concentrations typically used in large-scale cultivation systems.…”

Section: Biomass Concentrationmentioning

confidence: 99%

Monitoring of Microalgal Processes

Havlík

Scheper

Reardon

2015

Microalgae Biotechnology

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Process monitoring, which can be defined as the measurement of process variables with the smallest possible delay, is combined with process models to form the basis for successful process control. Minimizing the measurement delay leads inevitably to employing online, in situ sensors where possible, preferably using noninvasive measurement methods with stable, low-cost sensors. Microalgal processes have similarities to traditional bioprocesses but also have unique monitoring requirements. In general, variables to be monitored in microalgal processes can be categorized as physical, chemical, and biological, and they are measured in gaseous, liquid, and solid (biological) phases. Physical and chemical process variables can be usually monitored online using standard industrial sensors. The monitoring of biological process variables, however, relies mostly on sensors developed and validated using laboratory-scale systems or uses offline methods because of difficulties in developing suitable online sensors. Here, we review current technologies for online, in situ monitoring of all types of process parameters of microalgal cultivations, with a focus on monitoring of biological parameters. We discuss newly introduced methods for measuring biological parameters that could be possibly adapted for routine online use, should be preferably noninvasive, and are based on approaches that have been proven in other bioprocesses. New sensor types for measuring physicochemical parameters using optical methods or ion-specific field effect transistor (ISFET) sensors are also discussed. Reviewed methods with online implementation or online potential include measurement of irradiance, biomass concentration by optical density and image analysis, cell count, chlorophyll fluorescence, growth rate, lipid concentration by infrared spectrophotometry, dielectric scattering, and nuclear magnetic resonance. Future perspectives are discussed, especially in the field of image analysis using in situ microscopy, infrared spectrophotometry, and software sensor systems.

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“…This limitation is not unique to algal farming: conventional agricultural practices have incorporated sensing and monitoring technologies to better manage inputs such as herbicides, fertilizers, and irrigation [18]. Similarly, real-time on-line monitoring approaches are being developed to for continuous assessment of algal raceways and bioreactors [19][20][21][22][23][24][25][26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

Spectroradiometric monitoring for open outdoor culturing of algae and cyanobacteria

Reichardt

Collins

McBride³

et al. 2014

Appl. Opt.

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We assess the measurement of hyperspectral reflectance for outdoor monitoring of green algae and cyanobacteria cultures with a multichannel, fiber-coupled spectroradiometer. Reflectance data acquired over a 4-week period are interpreted via numerical inversion of a reflectance model, in which the above-water reflectance is expressed as a quadratic function of the single backscattering albedo, which is dependent on the absorption and backscatter coefficients. The absorption coefficient is treated as the sum of component spectra consisting of the cultured species (green algae or cyanobacteria), dissolved organic matter, and water (including the temperature dependence of the water absorption spectrum). The backscatter coefficient is approximated as the scaled Hilbert transform of the culture absorption spectrum with a wavelength-independent vertical offset. Additional terms in the reflectance model account for the pigment fluorescence features and the water-surface reflection of sunlight and skylight. For the green algae and cyanobacteria, the wavelength-independent vertical offset of the backscatter coefficient is found to scale linearly with daily dry weight measurements, providing the capability for a nonsampling measurement of biomass in outdoor ponds. Other fitting parameters in the reflectance model are compared with auxiliary measurements and physics-based calculations. The model-derived magnitudes of sunlight and skylight water-surface reflections compare favorably with Fresnel reflectance calculations, while the model-derived quantum efficiency of Chl-a fluorescence is found to be in agreement with literature values. Finally, the water temperatures derived from the reflectance model exhibit excellent agreement with thermocouple measurements during the morning hours but correspond to significantly elevated temperatures in the afternoon hours.

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Multispectral image analysis for algal biomass quantification

Cited by 19 publications

References 38 publications

Flux balancing of light and nutrients in a biofilm photobioreactor for maximizing photosynthetic productivity

Flux balancing of light and nutrients in a biofilm photobioreactor for maximizing photosynthetic productivity

Monitoring of Microalgal Processes

Spectroradiometric monitoring for open outdoor culturing of algae and cyanobacteria

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