A Novel Multispectral Imaging Method for Real-time Algal Culture Monitoring

Transcription

1 Solar Energy and Renewable Fuels Laboratory A Novel Multispectral Imaging Method for Real-time Algal Culture Monitoring Thomas E. Murphy, Keith B. Macon, and Halil Berberoglu Mechanical Engineering Department The University of Texas at Austin 7 th Annual Algae Biomass Summit October 2, 2013, Orlando FL

2 Motivation: Real time culture diagnostics Conventional methods for culture monitoring are slow, expensive, and highly localized Diagnostic task Method Disadvantages Biomass quantification Dry weight measurement Optical density or turbidity Slow (hours), localized Localized, slow (minutes) OR expensive Invasive species detection Culture health monitoring Microscopy Molecular analysis PAM fluorometry Dissolved oxygen Localized, expensive, slow (minutes to hours) Localized, expensive, slow (minutes to hours) Expensive Requires additional information What if all of this information could be gathered simultaneously in real time? Real time culture monitoring enables informed harvesting and dilution strategies automated nutrient, ph, and temperature control 2

3 Multispectral image analysis of algal ponds Multispectral image analysis can provide delay-free culture diagnostics pertaining to biomass concentration, invasive species presence, culture health, and more Biomass quantification Envisioned large scale implementation Ratios of spectral backscattered light in green band (555 nm) and blue band (~443 nm) used to quantify chlorophyll or particulate organic carbon (POC) concentration [1,2] Pigment monitoring Ratio of upwelling and downwelling spectral radiance used to reconstruct pigment content of monoculture [3] [1] Monitoring photosynthetic health In higher plants, changes in spectral reflectance are used to identify specific stresses such as senescence, dehydration, and herbicides [6] [4] [5] Automated, real time culture monitoring Problem: Optically thick ponds (τ ~ 100) cause saturation in backscattered intensity [1] Yoder J, Kennelly M. What have we learned about ocean variability from satellite ocean color imagers? Oceanography. 2006;19(1): [2] Allison DB, Stramski D, Mitchell BG. Empirical ocean color algorithms for estimating particulate organic carbon in the Southern Ocean. Journal of Geophysical Research. 2010;115(C10):C [3] Reichardt TA, Collins AM, Garcia OF, et al. Spectroradiometric Monitoring of Nannochloropsis salina Growth. Algal Research. 2012;1(1):22 31 [4] [5] [6] Carter GA. Responses of leaf spectral relfectance to plant stress. American Journal of Botany. 1993;80(3):

4 A custom reference platform for culture imaging Effectively reduces the optical thickness of the culture Design options: floating vs. fixed platform Proof of concept: floating reference platform Spectral intensity from submerged plate normalized by top plate Anabaena variabilis at: 0.2 g/l 0.3 g/l 4

5 Remote sensing of biomass concentration Crosssection (m 2 /kg) Backscattered intensity decreases with biomass concentration Backscattered intensity governed by the radiative transport equation (RTE) di (S, ) I S I S I S d ds 4 S, (, ) (, ) (, ) (, ) 4 i i i attenuation in the absorption out-scattering in-scattering direction of travel Solved using discrete ordinates method 1 Define: surface reflection backscattered I effective reflectance r s, Ibs, e,λ r e, I Effective reflectance, r e w, reflected from white reference plate Exemplary case: backscattered intensity from a monoculture of Chlorella sp. Chlorella sp. 2 Absorption cross section 3 Effective reflectance 600 carotenoids Increasing biomass 500 chl a concentration 400 chl b g/l g/l 0.5 g/l Wavelength, λ (nm) Wavelength, λ (nm) [1] Berberoglu, H., J. Yin, and L. Pilon, Light transfer in bubble sparged photobioreactors for H2 production and CO2 mitigation. International Journal of Hydrogen Energy 32, no. 13 (2007): [2] [3] Berberoglu H, Gomez PS, Pilon L. Radiation characteristics of Botryococcus braunii, Chlorococcum littorale, and Chlorella sp. used for fixation and biofuel production. Journal of Quantitative Spectroscopy and Radiative Transfer. 2009;110(17):

6 Blue reflectance, b Biomass quantification: experimental approach Green reflectance, g A abs (m 2 /kg) Red reflectance, r Predicted biomass concentration, X p (g/l) Wide band visible spectrophotometer (webcam) Absorption cross section 600 blue 500 green 400 red Wavelength, λ (nm) b = b g / b w g = g g / g w r = r g / r w 0.2 Culture 1 Culture 2 Culture Culture 1 Culture Culture Culture 2 Culture 3 Culture Biomass concentration, X (g/l) X b for 0.2 b 0.9 X r for b 0.2 & r R R Validation 0.2 Measured biomass concentration, X m (g/l) Normalized root mean squared error of 18% 6

7 Wide band red reflectance Wide band red reflectance Absorption cross section, A abs (m 2 /kg) Contamination detection Mismatch in absorption spectra between green algae and cyanobacteria enables detection of invasion of one group by the other Phycobilisome (cyanobacterial light-harvesting complex) Anabaena Chlorophyll b Chlorella Wavelength, λ (nm) 0.2 Chlorella sp. A. variabilis 0.2 Effective reflectance at 480 nm, r e,480 Effective reflectance, r e,λ Wide band blue reflectance Effective reflectance of Chlorella culture at 0.3 g/l 0.50 No A. variabilis A. variabilis at 3 g/l Wavelength, λ (nm) Calculate ratio of intensities at 480 nm (chl b) and 620 nm (phycobilisome peak) for detection of invasion of green algae culture by cyanobacteria Effective reflectance at 620 nm, r e, Chlorella sp. A. variabilis 0.2 Expected for monoculture Invaded culture Invasion ratio, X A.v. /X C 7

8 Other data products from multispectral imaging Prediction and avoidance of culture crashes o Example: identification of thermal stress [1] [2] Increase in B:G ratio coincided with decrease in photosynthetic yield Irreversible photosystem damage at t = 150 min Spectral backscattering as a warning sign of impending crash Flocculation monitoring by spatial variance analysis vs. Identification of non-photosynthetic bacteria by scattering analysis vs. [1] [2] 8

9 Conclusions A reference platform was presented for probing the color properties of algal ponds The effective spectral reflectance was correlated to biomass concentration using radiation transport theory and experimental wide band measurements A method was designed for identifying contamination using the spectral signature of a culture Multispectral imaging can enable rapid and simultaneous performance of a plethora of diagnostic tasks in large scale algal ponds 9

10 Acknowledgements The National Science Foundation (CBET ) NASA Ames Center Innovation Fund (CIF ) NASA Texas Space Grant Consortium 10

11 Solar Energy and Renewable Fuels Laboratory THANK YOU! Thomas E. Murphy Keith B. Macon Halil Berberoglu The University of Texas at Austin Mechanical Engineering Department Solar Energy and Renewable Fuels Lab 7 th Annual Algae Biomass Summit October 2, 2013, Orlando FL