Research ArticleReceived: 17 March 2012 Revised: 30 July 2012 Accepted: 8 September 2012 Published online in Wiley Online Library:

(wileyonlinelibrary.com) DOI 10.1002/jctb.3954

A novel non-invasive digital imaging methodfor continuous biomass monitoring and celldistribution mapping in photobioreactorsBasar Uyar∗

Abstract

BACKGROUND: In this study, a simple, non-invasive, non-destructive digital imaging method was developed to determine thecell biomass concentration and distribution in photobioreactors. For this purpose, images of a panel type photobioreactorcontaining Rhodobacter capsulatus as a model photosynthetic microorganism were captured with a digital camera and processedto obtain RGB color information.

RESULTS: Acquired photobioreactor images were processed to quantify cell concentration, blue color component was shownto be suitable for estimating the cell concentration accurately. In a batch reactor, the biomass change over time was trackedefficiently by the proposed method as verified by mass and spectrophotometric analysis. Cell distribution profile in thephotobioreactor was revealed by further analyses of the selected captured images. It was shown that cells were denser at thebottom part of the photobioreactor, and the concentration gradient increased over time during the batch runs.

CONCLUSION: This research suggests that image analysis technique may be used as a simple method for continuous andonline monitoring of cell concentration in photobioreactors. The research also demonstrated that a cell distribution map in thephotobioreactor can be constructed to reveal non-homogeneity.c© 2012 Society of Chemical Industry

Keywords: photobioreactor; digital imaging; Rhodobacter capsulatus; cell distribution profile; color analysis; continuous biomassmonitoring

INTRODUCTIONOne of the main outputs in fermentation processes is biomassproduction.1 Numerous direct and indirect methods are availablefor determination of the biomass, in order to monitor itsprogress. Direct procedures involve dry weight determination, cellcounting by microscopy and plate counting methods. Indirectmethods include turbidimetry, spectrophotometry, estimationof cell components, and on-line monitoring of carbon dioxideproduction or oxygen utilization.2

Direct microscopic count is rapid, but limited by its inabilityto distinguish living from dead cells, also, samples must containrelatively high cell concentrations. Plate counting method detectsviable cells (i.e. those able to form colonies) but a minimumof 1–2 days incubation is usually necessary before the coloniesare countable. These visual cell counting techniques are timeconsuming and are very dependent on the person doing the work.Dry weight estimation methods, which involve separating thebiomass from a known volume of a homogeneous cell suspension,determines the weight of total cells, both living and dead. Thetime needed to obtain the results and the relatively large volumeof sample required for accurate weighing are limitations of this

method.2–4

Commonly used indirect methods for quantifying biomassconcentration are turbidimetry and spectrophotometry.1 Turbidi-metric methods measure the light scattered by a suspension

of cells, which is proportional to the cell concentration. Spec-trophotometric methods use absorbance or transmittance of acell suspension. Turbidimetric and spectrophotometric methodsprovide a simple, rapid and convenient means of total biomassestimation but they require the construction of appropriate calibra-tion curves, prepared using standard cell suspensions containingknown concentrations of cells. Also, care must be taken when inter-preting the results if the fermentation broth contains particulatematter or is highly colored.2

A disadvantage of most previously developed procedures is thatthey are only available offline, which requires manual sampling.After sampling, the sample is admitted to the ex situ analysissystem with a time delay, which is not exactly reproducible. Thus,the measurement result may not correspond to the real stateof the process. Moreover, sampling from bioreactors or culturevessels represents a contamination risk, increases the amount ofwork, and may disturb culture conditions. For these reasons, thereis a demand for methods which are non-invasive (not requiringsampling), which allow direct inline observation in real time andhas the potential for automation.5,6

∗ Correspondence to: Basar Uyar, Department of Chemical Engineering, KocaeliUniversity, Kocaeli, 41380, Turkey. E-mail: basaruyar@gmail.com

Department of Chemical Engineering, Kocaeli University, Kocaeli, 41380, Turkey

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Among the non-invasive methods that can be used on-line,image analysis techniques, which analyze the features of anobject from visual data, fulfill these requirements and appearto be suitable for estimating cell content in cell suspensioncultures. Both microscopic and macroscopic images of the culturevessels have been used for quantification of cell masses and for

texture analysis to evaluate cell suspensions.1,5–7 Two main colormodels have been used in the color analyses of cultured cells;hue–saturation–intensity (HSI) or red–green–blue (RGB).6,8

One of the pioneering studies in this field used images ofcallus colonies and suspension cultures of anthocyanin-producingAjuga pyramidalis cells to track both biomass accumulation andanthocyanin pigment formation over time accurately withoutinterrupting the culture cycle.9 In another study, an image-processing system consisting of a microscope and camera wereused to acquire and process images of strawberry (Fragariaananassa) cells cultured in a solid medium, RGB color componentsobtained were used to monitor growth and the pigmentationbehavior of cells.7 Ceoldo et al. used image (color) analysis to studygrowth and anthocyanin production of carrot (Daucus carota) cellsin suspension cultures.8 Ibaraki et al. (2001) concluded that analysisof macroscopic images have good potential for developing simpleand non-invasive quality evaluations in cell suspension cultures.6

Although notable documented studies with image analysistechniques usually involved plant cells as described above,applications to microorganism cultures are also available.

Image-processing techniques have been used previously toanalyze the morphology of filamentous fungi growing in sub-merged fermentations,10 to monitor human keratinocyte growthin bioreactors,11 and to determine the biomass of yeasts (Saccha-romyces cerevisiae spp) immobilized into alginate hydrogel matrix.1

Photobioreactors are transparent culture vessels in whichphotosynthetic microorganisms (algae, bacteria) are employed tocarry out bioprocesses. The transparency allows visual monitoringof the bioreactor content from outside, thus digital imagingtechniques become very useful in these systems for analysis andevaluation of the bioprocess.

The literature on the application of image analysis methodsto a photobioreactor system is scarce; one such study involvesusing image-analysis techniques to analyze the light-distributionprofile in a photobioreactor containing Synechococcus sp. andthen relating the digitized images of the light-distribution profileto the cell density.12 The authors were able to predict the cellconcentrations in the photobioreactor with a <5% error.

Among photosynthetic bacteria, purple nonsulfur bacteria(PNSB) that can be found in natural setting such as pond water,mud or a sewage sample are used for investigation of basicmetabolic events (i.e. nitrogen fixation, carbon fixation, anoxygenicphotosynthesis, membrane bioenergetics),13 in sewage treatmentprocesses, for production of biomass, biopolymers such as poly-3-hydroxybutyrate (PHB), molecular hydrogen, vitamins and otherorganic molecules.14 The well known genera of PNS bacteria areRhodospirillum, Rhodopseudomonas and Rhodobacter.13 Anaerobiccultures of PNSB have a yellowish-brown color, the color of ananaerobic culture can turn deep red when exposed to air.15 Thecolor of the bacteria is due to the pigments of bacteriochlorophylland carotenoid.16

There are currently no reports in the literature on the applicationof image analysis methods to a photobioreactor containing PNSB.

This study explores the application of the image analysismethod to a photobioreactor system. In this context, acquiredphotobioreactor images were processed to quantify PNSB cell

concentration, the data obtained were used to construct acontinuous cell growth curve and a map of cell distribution inthe photobioreactor.

MATERIALS AND METHODSBacteria and culture mediaRhodobacter capsulatus (DSM 1710) was used in this study as amodel photofermentative PNSB. The nutrient media used was themodified medium of Biebl and Pfennig17 having the followingcomposition (g L-1): malate (carbon source), 2; sodium glutamate(nitrogen source), 1.8; KH2PO4, 3; MgSO4.7H2O, 0.5; yeast extract,0.2; CaCl2.2H2O, 0.015; Fe(III)citrate, 0.005. The malate and sodiumglutamate concentrations used were previously reported to favor

the biomass growth in Rhodobacter species.18–20 The final pHof the nutrient media was adjusted to 6.7–6.8 by addition ofNaOH. pH was not controlled in the photobioreactors during theruns, however, a high concentration of KH2PO4, which serves as aneffective pH buffer21, allowed pH to stay stable in the neutral range(6.7–7.4) during both batch runs. The nutrient solution obtainedwas sterilized by heat at 121◦C for 15 min.

PhotobioreactorA panel type photobioreactor constructed from transparent acrylicsheet (PMMA) was employed in this study. The dimensions of thephotobioreactor were (Height:17.5 cm, Length: 17.5 cm, Width:5 cm) and working volume was 720 mL. The thickness of theacrylic sheet was 10 mm. There was a sampling port on theside of the panel. The photobioreactor was equipped with adigital temperature probe for monitoring culture temperature.The photobioreactor was sterilized chemically using ethanol (70%solution) then cleaned with sterile distilled water before the runs.At the start of a run, 10% inoculation by volume was made from thegrown bacterial culture into the photobioreactor. Figure 1 showsthe photobioreactor setup containing grown bacteria.

Image acquisition and processingA digital web camera (Microsoft Webcam SCB-0340N) connectedto a PC was positioned vertically at a distance of 50 cm from thephotobioreactor. The angle between the camera lens axis and thelighting source was 45◦ to prevent reflected light from the photo-bioreactor surface interfering with the color of acquired images. Atotal of eight small white paper pieces were placed on the frameof the photobioreactor; these were used as white color referencesfor comparing the white balance between captured images.

After the inoculation, images of the photobioreactor werecaptured every 15 min during the runs and were stored in aPC. The resolution of the captured images was 640×480. Theimage analyses were performed by a software program exclusivelydeveloped for this work using Microsoft Visual Basic 6.0.

For average cell concentration determination, the part of thecaptured pictures that shows the photobioreactor surface wasselected, that is an area consisting of 62500 (250 × 250) pixelscorresponding to a resolution of 37 dots per inch. R, G, B values ofeach of those pixels was measured (0–255) and average R, G, and Bvalues were calculated. Due to the automatic white balance featureof the camera used, the color consistency between images wasalso checked to minimize errors originated from the camera, thisis done by determining the color components of the white partsplaced on the photobioreactor frame for each image; the differencein RGB values of these white parts between consecutive images

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Figure 1. The photobioreactor containing grown bacteria.

were usually less than 1% and no modification was necessary. Ifmore than 1% difference was measured, the white balance of theimages was adjusted prior to photobioreactor color analysis assuggested by Miyanaga et al.7

For cell distribution map construction, the surface of thephotobioreactor was divided into 36 equal square parts (40 ×40 pixels each), RGB values of the 1600 pixels on each partwere measured and average value was calculated. In order tominimize errors originated from the uneven light distributionand reflections on the photobioreactor surface, the first picturecaptured right after inoculation was used as a reference. The celldistribution at this stage was assumed to be homogenous dueto the initial mixing of the photobioreactor, low concentrationof the cells and fresh nutrient medium that contains active andhighly motile cells. The cell distribution map constructed showedthat the error range for the whole surface of the photobioreactorsurface was within ±0.1 optical density. This error range was nothigh enough to affect the conclusions of this study, therefore nocolor correction or calibration was made to the images prior tocolor analysis.

Operating conditionsThe temperature in the photobioreactor was in the range 30–33◦Cduring the runs. A 75 W incandescent lamp was used to attaina continuous illumination at the surface of the photobioreactor.Since the bacteria are able to grow under anaerobic conditions andfoaming was virtually absent in the system, headspace volume wasnot required and thus the bioreactor was filled completely withthe nutrient media during inoculation. Details of this experimentalsetup were given previously.22 The photobioreactor was run inbatch mode.

Two runs were carried out in the photobioreactor; the runslasted until the characteristic growth curves including lag, log andstationary phases are obtained. That corresponded to a duration ofapproximately 5 days for the first run, and 7 days for the latter run.

Analytical methodsThe temperature inside the photobioreactor was measured usinga digital thermometer probe. The pH was measured with a pH-meter (Mettler-Toledo). For determination of the bacterial cellconcentration by spectrophotometry, a 1 mL sample was takenfrom the sampling port of the photobioreactor. Optical density

(OD) of the sample at 660 nm was measured by a UV-visiblespectrophotometer (Hach Lange DR5000). Nutrient medium wasused as a blank solution. For the determination of bacterial dry cellweight, a 10 mL sample was taken from the sampling port of thephotobioreactor and centrifuged at 6000 rpm for 15 min. Then,the supernatant was removed, the pellet residue was dried at 40◦Cin an oven until constant weight and weighed. Overall, 11 and 13samplings were made from the photobioreactor during the firstand the second batch runs, respectively. Since the photobioreactordoes not have any headspace, taking samples from the mediacreates a negative pressure inside the photobioreactor whichcauses stress on the reactor material. Removal of the small samplesfor OD measurement (1 mL) was tolerated by the system (mainlyby bacterial generation of hydrogen and carbondioxide), howeverduring sampling for bacterial dry cell weight determination (10mL) an equal amount of distilled water was injected into thephotobioreactor. Otherwise, the pressure difference building upmay promote air leakage that can disrupt the anaerobic conditionsinside. The dilution introduced by water was taken into accountduring calculations.

RESULTS AND DISCUSSIONCorrelation between biomass and colorIn order to explore the possibility of estimating cell concentrationfrom images, a preliminary study was carried out. Eleven solutionsat different cell concentrations were prepared by adding culturemedium to a cell concentrate. ODs and cell dry weights of thosesolutions were measured. Images of the photobioreactor filledwith these culture solutions were also obtained. The images wereprocessed to obtain mean Red (R), Green (G) and Blue (B) data. Themean color values were compared with OD and cell dry weight ofeach solution in Table 1.

The color characteristics of cell cultures were as follows: the Rcomponent value was much larger than the G or B componentvalues. Also, it is clear that RGB values are high (combined coloris close to white) when OD is low. As OD increases, color valuesdecrease (color gets darker). The decreases in mean color valueswere 94.1, 134.1, 125.7 for R, G and B, respectively. The higherdecrease in G and B compared to R results in an overall red colorin dense cultures.

This finding is expected; in the visible range (400–700 nm),the absorption of the bacteria is minimum at 650–700 nm regionwhich corresponds to red and maximum at 400–500 nm regionwhich corresponds to blue.22 That means low emission of blueand green color and high emission of orange-red color.

In order to determine a possible correlation between the RGBcolor components and the biomass, OD versus R, G, and B graphswere plotted on arithmetic, semi-logarithmic and logarithmicscales and correlation analyses between OD and color values wereperformed. The coefficients of determination for linear regressions(R2) are tabulated in Table 2.

It was observed that G value was too close to zero if OD of thesolution is higher than 1.0 (Table 1), which affected the sensitivityof the color analysis. Therefore the existence of a correlation wastested in solutions that satisfy G > 9 (=OD<1.0). Comparably, Rvalue is too close to the upper limit (255) if OD of the solution islower than 0.3 (Table 1), resulting in low sensitivity, thus existenceof a correlation was tested in solutions that satisfy OD>0.3. Onthe other hand, B value range was 35–160 for all of the solutionstested (Table 1) and was not too close to lower or upper limits of

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Table 1. OD, cell dry weight (CDW, in g L-1) and RGB values ofphotobioreactor containing different concentrations of cells

Solution OD CDW R G B

1 2.400 1.67 153.8 2.9 35.1

2 1.896 1.31 165.5 3.8 39.6

3 1.588 1.10 177.8 4.6 43.9

4 1.281 0.89 190.2 5.9 48.3

5 0.990 0.68 203.2 9.3 52.7

6 0.725 0.5 227.3 27.9 62.0

7 0.534 0.37 238.0 46.1 72.1

8 0.367 0.25 244.4 68.8 87.4

9 0.239 0.17 247.1 94.5 110.1

10 0.147 <0.10 247.7 117.6 135.2

11 0.086 <0.10 247.9 137.0 160.8

Table 2. The coefficient of determination for linear regression (R2)

OD log (OD)

R 0.971 0.995

log (R) 0.985 0.990

G 0.933 0.992

log (G) 0.988 0.838

B 0.703 0.969

log (B) 0.857 0.997

color detection range therefore all of the solutions were used forcorrelation analysis.

From these regression analyses, it was envisioned that the cellconcentration in a photobioreactor could be estimated from theimages as the color values and the cell concentration were properlycorrelated. Highest accuracy in OD estimation was obtained whenB value was used; the coefficient of determination for linearregression (R2) was 0.997 between log(OD) and log(B) (Table 2).Additionally, unlike R and G values, B value is applicable tothe entire range of cell concentrations tested. Therefore, thecorrelation equation between B and OD given below (Equation(1)) is suggested to be used for predicting OD:

OD = 10(−2.1277logB+3.6809) (1)

In a comparable study, Acevedo et al.,1 also preferred to use Bvalues for correlating color and yeast (Saccharomyces cerevisiae)biomass.

Monitoring biomass concentration in a photobioreactorIn order to validate the method developed, two consecutivebatch runs were performed in the photobioreactor which wasmonitored by the camera. Color analyses of the images obtainedwere made and average B values were measured. The ODscorresponding to B values were calculated using Equation (1). RealOD values were also measured in a spectrophotometer and celldry weight measurements were carried out at regular intervalsas described in the Materials and methods section. The estimatedOD values from image analysis were compared with real OD andcell dry weight in Fig. 2(a) and 2(b).

As can be seen from the graphs, the OD estimated by themodel fits the spectrophotometry and dry cell weight data quite

Figure 2. Biomass concentration of PNSB in photobioreactor duringbatch runs: ( ) OD predicted from image analysis; ( ) OD measuredby spectrophotometer; ( ) cell dry weight. (a) First batch run; (b) secondbatch run.

well: considering both runs, R2 was 0.972 between OD andmodel prediction, and 0.965 between cell dry weight and modelprediction. It can thus be deduced that PNSB cell concentrationcan be predicted accurately from photobioreactor color. Usingimage analysis has further advantage over the two other methods;a more precise growth curve can be constructed since there aremore data points. In the present case, as can be seen in Fig. 2, theshort lag time at the start of the runs and the fluctuations in cellconcentration during the stationary phase which is not detectedby OD and dry weight measurements were clearly visible in themodel estimate.

The results obtained suggest that an image analysis methodalone can be used for continuous and online monitoring of cellconcentration inside photobioreactors or it can be used as asupporting tool to traditional cell concentration determinationtechniques (OD and dry weight determination) to fill the gapsbetween sample collections.

Other advantages of the suggested method over other cellconcentration determination methods are listed as follows: (a) Themethod does not require expensive and complicated equipment,and is a much cheaper alternative to other equipment performinga comparable task (i.e. turbidometer, spectrophotometer). (b)The method is non-invasive; sampling is not necessary, cultureconditions are not disturbed, the risk of contamination iseliminated. This property may be very valuable in cases wheresampling needs to be avoided (i.e. in small sized bioreactors)and in processes where frequent sampling is necessary. (c) Thesystem can be automated to obtain continuous data for precisemonitoring of the bioprocesses.

On the other hand, there may be some limitations to thistechnique too: (a) Less colored microorganisms or dark coloredmedia (such as wastewaters) may decrease its predictive power.

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Figure 3. Biomass distribution in the photobioreactor: (a) 24 h after inoculation; (b) 48 h after inoculation; (c) 96 h after inoculation; (d) 168 h afterinoculation.

(b) Change in the light intensity (such as outdoor reactors that areexposed to the diurnal light cycle) would require more complexcalibration curves which should consider light intensity as well. (c) Itneeds to be calibrated for each bacteria type and photobioreactorsystem, and possibly needs recalibration if bioprocess conditionswhich may alter pigmentation of bacteria change.

Biomass distribution mappingAgitation of photobioreactors is difficult due to limitationsimposed by reactor geometry and fragility of the constructionmaterial. Although the cells are motile, in the absence of agitation,spatial variations in cell concentration can be expected in practice.

In this part of the study, it was proposed to construct a mapof cell distribution in the photobioreactor. Conventional cell con-centration detection techniques that require sampling cannot beemployed for this purpose for practical reasons. The non-invasivemethod discussed in this study renders this analysis possible.

Sample pictures of the second run were selected thatcorresponded to different bioprocess stages; mid-log phase (24thand 48th hours), end of log phase-start of stationary phase(96th hour), and late stationary phase (168th hour). The imageswere analyzed as explained in Materials and methods and celldistribution maps were constructed (Fig. 3). As is seen in Fig. 3,cell distribution is not homogeneous in the photobioreactor; thecells are denser at the bottom part. Moreover, the ratio of the cellsat the bottom part increased during the batch run; the ratio ofthe cells in bottom 1/6 part of the photobioreactor to the cells intop 1/6 part of the photobioreactor was 2.0, 3.1, 3.2, and 3.7 after24, 48, 96 and 168 h, respectively. The change in the ratio wasprobably due to the aging culture and increase in the dead cellsthat settle to the bottom. Indeed, the cell concentration at hours96 and 168 were almost the same (Fig. 2(b)), yet bottom/top cellratio increased from 3.2 to 3.7.

The only comparable previously published study contains mea-surements of cell movements and the irradiance intensity patternin PBRs using computer-automated radioactive particle tracking(CARPT), an advanced non-invasive diagnostic technique.23

Since non-homogeneity in bioreactors may affect overall biopro-cess efficiency, monitoring cell distribution in a photobioreactorhas the potential of becoming a routine task if it can be carriedout easily without sophisticated and expensive equipment. Basedon the results reported here, image analysis technique may besuggested to fulfill this role. In addition to cell mapping, numericaldata obtained can be used in studies that investigate the innerdynamics of the photobioreactors and in simulation work.

CONCLUSIONA digital imaging technique was used for non-invasive cellbiomass determination in a photobioreactor. Rhodobacter

capsulatus concentration was estimated accurately from bluecolor information. A cell distribution map of the photobioreactorwas constructed and it was shown that cells were denser atthe bottom part of the photobioreactor. It was also foundthat the ratio of the number of cells at the bottom part tothose in the top part increased over time during a batchrun. This research shows that an image analysis techniquemay be useful for monitoring the cell concentration and forconstructing a cell distribution map in photobioreactors thatcontain PNSB.

ACKNOWLEDGEMENTSThe author thanks Dr Yavuz Ozturk for his gift of the bacteriaculture. This research was supported financially by KocaeliUniversity (BAP2010/84).

REFERENCES1 Acevedo CA, Skurtys O, Young ME, Enrione J, Pedreschi F and Osorio F,

A non-destructive digital imaging method to predict immobilizedyeast-biomass. LWT - Food Sci Technol 42:1444–1449 (2009).

2 Waites MJ, Morgan NL, Rockey JS and Higton G, Industrial Microbiology:An Introduction. Blackwell Science, London (2001).

3 Madigan MT, Martinko JM and Parker J, Brock Biology of Microorganisms.Prentice Hall, New Jersey (2000).

4 Olofsdotter M, Image processing: a non-destructive method formeasuring growth in cell and tissue culture. Plant Cell Rep12:21621–21629 (2001).

5 Hopfner T, Bluma A, Rudolph G, Lindner P and Scheper T, A review ofnon-invasive optical-based image analysis systems for continuousbioprocess monitoring. Bioprocess Biosyst Eng 33:247–256 (2010).

6 Ibaraki Y and Kenji K, Application of image analysis to plant cellsuspension cultures. LWT - Food Sci Technol 30:193–203 (2001).

7 Miyanaga K, Seki M and Furusaki S, Analysis of pigmentation inindividual cultured plant cells using an image processing system.Biotechnol Lett 22:977–981 (2000).

8 Ceoldo S, Levi M, Marconi AM, Baldan G, Giarola M and Guzzo F, Imageanalysis and in vivo imaging as tools for investigation of productivitydynamics in anthocyanin-producing cell cultures of Daucus carota.New Phytol 166:339–352 (2005).

9 Smith MAL, Reid JF, Hansen AC, Li Z and Madhavi DL, Non-destructivemachine vision analysis of pigment-producing cell cultures. JBiotechnol 40:1–11 (1995).

10 Cox PW and Thomas CR, Classification and measurement of fungalpellets by automated image analysis. Biotechnol Bioeng 39:945–952(1992).

11 Kino-Oka M and Prenosil JE, Development of an on-line monitoringsystem of human keratinocyte growth by image analysis and itsapplication to bioreactor culture. Biotechnol Bioeng 67:234–239(2000).

12 Jung S-K and Lee SB, Image analysis of light distribution in aphotobioreactor. Biotechnol Bioeng 84:394–397 (2003).

13 Kars G and Gunduz U, Towards a super H2 producer: improvementsin photofermentative biohydrogen production by geneticmanipulations. Int J Hydrogen Energy 35:6646–6656 (2010).

14 Dworkin M, Falkow S, Rosenberg E, Schleifer K-H and Stackebrandt E,The Prokaryotes, Vol. 5: Proteobacteria: Alpha and Beta Subclasses.Springer (2006).

J Chem Technol Biotechnol (2012) c© 2012 Society of Chemical Industry wileyonlinelibrary.com/jctb

www.soci.org B Uyar

15 van Niel C, Studies on the pigments of the purple bacteria. Antonie vanLeeuwenhoek 12:156–166 (1947).

16 Pellerin NB and Gest H, Diagnostic features of the photosyntheticbacterium Rhodopseudomonas sphaeroides. Curr Microbiol :339–344(1983).

17 Biebl H and Pfennig N, Isolation of Members of the FamilyRhodosprillaceae. Springer-Verlag, New York (1981).

18 Koku H, Eroglu I, Gunduz U, Yucel M and Turker L, Kinetics of biologicalhydrogen production by the photosynthetic bacterium Rhodobactersphaeroides O.U. 001. Int J Hydrogen Energy 28:381–388 (2003).

19 Uyar B, Eroglu I, Yucel M and Gunduz U, Photofermentative hydrogenproduction from volatile fatty acids present in dark fermentationeffluents. Int J Hydrogen Energy 34:4517–4523 (2009).

20 Ozgur E, Uyar B, Ozturk Y, Yucel M, Gunduz U and Eroglu I, Biohydrogenproduction by Rhodobacter capsulatus on acetate at fluctuatingtemperatures. Resource Conserv Recycl 54:310–314 (2010).

21 Uyar B, Hydrogen production by microorganisms in solar bioreactor.PhD thesis, Middle East Technical University, Ankara (2008).

22 Uyar B, Eroglu I, Yucel M, Gunduz U and Turker L, Effect of light intensity,wavelength and illumination protocol on hydrogen production inphotobioreactors. Int J Hydrogen Energy 32:4670–4677 (2007).

23 Luo H-P, Kemoun A, Al-Dahhan MH, Sevilla JMF, Sanchez JLG,Camacho FG, et al., Analysis of photobioreactors for culturing high-value microalgae and cyanobacteria via an advanced diagnostictechnique: CARPT. Chem Eng Sci 58:2519–2527 (2003).

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