Accepted Manuscript

Isolation and screening of heterocystous cyanobacterial strains for biodieselproduction by evaluating the fuel properties from fatty acid methyl ester(FAME) profiles

Antonyraj Matharasi Perianaika Anahas, Gangatharan Muralitharan

PII: S0960-8524(14)01605-8DOI: http://dx.doi.org/10.1016/j.biortech.2014.11.003Reference: BITE 14209

To appear in: Bioresource Technology

Received Date: 31 August 2014Revised Date: 31 October 2014Accepted Date: 2 November 2014

Please cite this article as: Anahas, A.M.P., Muralitharan, G., Isolation and screening of heterocystous cyanobacterialstrains for biodiesel production by evaluating the fuel properties from fatty acid methyl ester (FAME) profiles,Bioresource Technology (2014), doi: http://dx.doi.org/10.1016/j.biortech.2014.11.003

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Isolation and screening of heterocystous cyanobacterial strains for biodiesel production by

evaluating the fuel properties from fatty acid methyl ester (FAME) profiles

Antonyraj Matharasi Perianaika Anahas, Gangatharan Muralitharan*

Department of Microbiology, Centre for Excellence in Life Sciences, Bharathidasan University,

Palkalaiperur, Tiruchirappalli 620 024, Tamilnadu, India.

* Corresponding author: Gangatharan Muralitharan

E-mail address: drgm@bdu.ac.in

Tel.: +91-431-2407082

Fax: +91-431-2407045

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Abstract

This study reports on the biodiesel quality parameters of eleven heterocystous cyanobacterial strains

based on fatty acid methyl esters (FAME) profiles. The biomass productivity of the tested

cyanobacterial strains ranged from 9.33 to 20.67 mg L-1

d-1

while the lipid productivity varied

between 0.65 and 2.358 mg L-1 d-1. The highest biomass and lipid productivity was observed for

Calothrix sp. MBDU 013 but its lipid content is only 11.221 in terms of percent dry weight, next to

the Anabaena sphaerica MBDU 105, whose lipid content is high. To identify the most competent

isolate, a multi-criteria decision analyses (MCDA) was performed by including the key chemical and

physical parameters of biodiesel calculated from FAME profiles. The isolate Anabaena sphaerica

MBDU 105 is the most promising biodiesel feed stock based on decision vector through Preference

Ranking Organisation Method for Enrichment Evaluation (PROMETHEE) and Graphical Analysis

for Interactive Assistance (GAIA) analysis.

Keywords: Cyanobacteria; Heterocystous; Biodiesel quality; Lipid productivity; FAME profiles;

PROMETHEE; GAIA

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1. Introduction

The reliance of the global economy on fossil-derived fuels, coupled with the increasing

energy demand in emerging countries like India and China and the geo-political instability in some

world’s oil-producing regions, have led to soaring petroleum prices in the last years. Increased use of

fossil fuels will also increase atmospheric carbon dioxide (CO2), hastening the global warming crisis.

Thus, there is an urgent need to develop sustainable and affordable energy from renewable resources

(Khanal, 2008). Several emerging technologies are being implemented to replace fossil fuels by

promoting viable production of liquid fuels such as fatty acid esters (biodiesel), alkanes and higher

alcohols from renewable sources (Atsumi et al., 2008). In this regard, biodiesel from agricultural

crops (first generation biofuel system) is a renewable fuel that is attracting the most attention.

However, this production system presents significant environmental and economic restraints. The

increasing competition with agriculture for cultivable land used for food production has been

considered one of the most common constraints to first generation biofuels (Gressel, 2008).

Recently, there has been an emerging interest towards complementary concepts that employ

aquatic photobiological organisms, such as cyanobacteria and green algae, as the biotechnological

host for conversion of sunlight energy, H2O, and CO2 into hydrocarbon fuels (Liu et al., 2011a).

Third generation technology is based on algae or cyanobacteria that contain a high oil mass fraction

grown in ponds. Under proper conditions, these microorganisms can produce lipids for biodiesel

with yields per unit area that are many fold higher than those with any plant system (Chisti, 2008).

Biodiesel is a renewable fuel that can be produced from biological oils derived from plants,

animals or microbes. Biodiesel contains chain lengths between C14-C24 with varying degrees of

unsaturation (Varfolomeev and Wasserman, 2011). The fatty acid methyl esters (FAME) profile is

also dependent on the specific producing organism as well as its growing conditions (Saraf and

Thomas, 2007). Biodiesel contains relatively high oxygen content by weight which results in more

4

complete combustion than mineral diesel resulting in lower CO, particulate matter and hydrocarbon

emissions (Song et al., 2008).

Although eukaryotic algae and prokaryotic cyanobacteria can be used to generate raw

materials for nonpetroleum-based diesel production, cyanobacteria have certain advantages over

algae. First, lipid accumulation in oleaginous algae is mostly achieved by either imposing stress (i.e.,

adverse environmental conditions) or adding sugar (Miao and Wu, 2006). Second, cyanobacteria are

much more amenable to metabolic engineering to improve lipid content beyond that of the wild type

(Liu et al., 2011a).

Cyanobacteria have also been subjected to screening for lipid production (Basova, 2005). The

biosynthesis of fatty acid-based biofuels in cyanobacteria includes two steps, production and

transesterification of fatty acids (FA) to form alkyl fatty acid esters (Balasubramanian et al., 2012).

Considering that fuel properties are largely dependent on the fatty acid composition of the feedstock

from which biodiesel is prepared, FA profile was employed as a screening tool for selection of

cyanobacterial lipids with high amounts of monounsaturated FAs. The presence of double bonds in

the FAs from cyanobacterial lipids is related to their morphological complexicity (Vargas et al.,

1998). Few publications addressed the issues of enhancing the fatty acid profile of cyanobacteria

(Knothe, 2013).

Among the different groups of cyanobacteria, the filamentous nitrogen-fixing species are

particularly attractive for the production of biomass and chemicals, since they are able to use

atmospheric nitrogen as the sole nitrogen source. In addition, the filamentous nature of these

microalgae confers an advantage for harvesting the cells. The lack of fixed nitrogen in the growth

medium has positive economic implications and restricts the problem of contamination by other

microorganisms. Despite these clear advantages and their potential significance to biotechnology,

there has been very little applied research carried out with filamentous nitrogen-fixing cyanobacteria

5

and few strains have been successfully grown outdoors, with high biomass productivities (Moreno et

al. 1995).

Although cyanobacteria are being commonly used as biofactories but research is still focused

on standard model marine and freshwater species rather than exploring potential strains from unusual

sites. Hence, isolating and screening of potential cyanobacteria from unexplored sources is an

indispensable research area for unveiling the untapped resourceful species for biofuel/bioproduct

generation (Olguin, 2012). The present research aimed to address this shortfall by comparing eleven

filamentous heterocystous cyanobacterial strains (free-living and symbiotic) and pointing out the

most suitable candidates for biodiesel production. The approach is to compare their volumetric lipid

productivities and their fatty acid profiles responsible for the biodiesel properties. Other selection

criteria included were cetane number (CN), iodine value (IV), cloud point (CP) and cold filter

plugging point (CFPP), estimated based on FAME profiling. Such an approach can clearly identify

the best strains for biofuel production based not only on the volumetric lipid productivity but also on

their adequate oil composition.

2. Methods

2.1 Cyanobacterial strains isolation and cultivation

A total of eleven cyanobacterial strains were used in this study. Among which six strains

were isolated from rice field and fresh water ponds in and around Tiruchirappalli and Thanjavur

district, Tamilnadu, India. Other five symbiotic cyanobacterial strains were isolated from Azolla and

Cycas circinalis according to the method described earlier (Thajuddin et al., 2010). The cells were

subjected to purification by serial dilution followed by plating on to sterile BG-11N0 agar medium

(Rippka et al., 1979). The plates were incubated under constant light intensity (50 µE m–2 s–1) for up

to 10 days at 25 ºC. Later the developed colonies were isolated and purified by a method described

by Wolk (1988) and the plates were examined periodically to select the cyanobacterial colonies,

6

which were separated from bacterial colonies. A loopful of axenic cyanobacterial colonies were

subcultured into 50 ml of BG-11N0 medium and incubated under above mentioned conditions. Purity

of the culture was tested by repeated plating and by regular observation under a microscope.

All the cyanobacterial strains were grown in 500 ml Erlenmeyer flasks containing 200 ml of

BG-11N0 medium and incubated at 28±2 ºC, 14/10-h light/dark cycle, with the light intensity of 50

µE m–2

s–1

under static conditions. The cultures were mildly shaken by hand on alternate days. All

experiments were carried out in triplicates.

2.2 Morphological and molecular characterization of the isolates

The growth behaviour of individual free-living and symbiotic isolates on BG-11N0 agar

plates were followed over a 3-week period and recorded using bright field (Optika, Italy) and

confocal laser scanning microscope (CLSM) (LSM 710, Carl Zeiss, Germany).Generic assignment

of the isolates was based on morphological criteria (Rippka et al., 1979). For molecular confirmation

of the isolates, genomic DNA was isolated and PCR amplification of the 16S rRNA gene was carried

out as described previously (Thajuddin et al., 2010). The sequences of the purified PCR products

(GeneJET PCR Purification Kit, Thermo Scientific, USA) were determined by using an ABI 310

automatic DNA sequencer (Applied Biosystems, CA, USA). The 16S rRNA gene sequences

determined in this study were deposited in the GenBank database and the accession numbers are

listed in Table 1.

2.3 Growth kinetic parameters

Growth kinetic parameters were obtained in triplicates for the tested cyanobacterial strains

during the cultivation period. Cells were harvested after 24th day of growth by centrifugation and

lyophilized. The parameters analyzed included:

1. Biomass productivity (Pdwt) as the dry biomass produced (in grams per liter per day), during the

stationary growth phase (Griffiths and Harrison, 2009). For Pdwt determination, samples were

collected at the stationary phase and cells were harvested by centrifugation for 5 min at 3000 ×g at 4

7

ºC. The cell pellets were washed with distilled water, lyophilized at -40 ºC for 48 h and their dry

weights were determined gravimetrically.

2. Total lipid content (Lc) extracted using chloroform/methanol (Folch et al., 1957), was reported as

percentage of the total biomass (% dwt).

3. Volumetric lipid productivity (Lp) was calculated following the equation Lp = Pdwt ×Lc and

expressed as milligrams per liter per day (Liu et al., 2011b).

2.4 Lipid extraction

Lipid extraction was done following the method of Folch et al. (1957). A known quantity (50

mg) of freeze dried biomass was extracted with chloroform : methanol (2:1) using pestle and mortar.

The extraction was repeated until the biomass was decolorized completely. The extract was filtered

through Whatman No. 1 filter paper where a third volume of distilled water was added to remove

water-soluble impurities. Then the filtrate was vortexed and let stand for separation of two layers and

the lower lipid layer was transferred carefully. The pooled extracts were passed through anhydrous

sodium sulfate and stored in a pre-weighed glass vial. Solvents were removed by rotary evaporation

(Buchi Rotovapor R-205, Buchi, India). Lipids were quantified gravimetrically and the lipid content

was expressed as percent on dry weight basis.

2.5 Preparation of FAME

Identification and quantification of fatty acids were done according to the modified method

of Miller and Berger (1985). For preparation of FAME, a known amount of lipid was saponified by

boiling it with 1 ml of saponification reagent (15 g NaOH in 100 ml of 1:1 methanol: water) for 30

min. The sample was then boiled in a water bath at 80 ºC for 20 min with 2 ml of methylation

reagent (1:1.18 methanol : 6 N HCl). After cooling, 1 ml of extraction solvent (1:1 distilled hexane:

anhydrous diethylether) was added and mixed thoroughly. Thereafter the lower aqueous phase was

discarded and the remaining upper phase was washed with 3 ml of base wash solution (1.2% NaOH

w/v). Finally, 2 µl of the organic phase was injected in a gas chromatograph.

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2.6 Gas chromatography analysis

Fatty acids profile was determined by the capillary column gas chromatographic method

applied to the oil methyl esters (Miller and Berger, 1985). The FAME samples were analyzed by gas

chromatograph (Shimadzu, QP 2010, Japan) with flame ionization detector (FID). 2 µl of each

sample was injected into SP-2560 column (Supelco, USA) (100 m × 0.25 mm I.D. × 0.20 µm film

thickness). The temperature program as follows, oven: 140 ºC (5 min.) to 240 ºC at 4 ºC/min., hold

15 min; carrier gas: helium, 20 cm/sec., detector temperature 260 ºC, and split ratio of 100:1. The run

time for a single sample was 55 min. Each sample was analyzed in triplicates. FAs were identified

and quantified by comparing the retention time and area of the authentic standards Supelco FAME

mix C4 - C24 (Bellefonte, PA, USA).

2.7 Evaluation of biodiesel fuel properties from FAME profiles

In order to screen the most suitable cyanobacterial strain for biodiesel production, several

chemical and physical properties attesting for the quality of biodiesel were estimated from FAME

profiles directly. Chemical biodiesel quality parameters like cetane number (CN), iodine value (IV),

saponification value (SV), degree of unsaturation (DU), long chain saturated factor (LCSF) and cold

filter plugging point (CFPP) were calculated using empirical equations (1) - (6) (Francisco et al.,

2010), the allylic and bis-allylic position equivalents (APE and BAPE) from the equations (7) and (8)

(Knothe, 2002) and cloud point (CP) and pour point (PP) from the equations (9) and (10) (Sarin et

al., 2009).

CN = 46.3 + (5,458/SV) – (0.225 × IV) (1)

SV and IV were calculated following the equations (2) and (3), where D is the number of

double bonds, M is the FA molecular mass, and N is the percentage of each FA component.

SV = ∑ (560 × N) / M (2)

IV = ∑ (254 × DN) / M (3)

The DU was calculated using the equation (4),

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DU = MUFA + (2 ×PUFA) (4)

where,

MUFA - monounsaturated fatty acids

PUFA - polyunsaturated fatty acids (in wt %)

The long-chain saturated factor (LCSF) was estimated by weighting up the values of longer

chain fatty acids (C16, C18, C20, C22, C24 wt %) using the following equation (5).

LCSF = (0.1 × C16) + (0.5 × C18) + (1 ×C20) + (1.5 × C22) + (2 × C24) (5)

Cold filter plugging point (CFPP) in equation (6) related to chain saturation and length of

FAME.

CFPP = (3.1417 × LCSF) – 16.477 (6)

APE and BAPE are the theoretical measure of the number of singly allylic carbons present

and the number of doubly allylic carbons present respectively in the fatty oil or ester, assuming that

all poly-olefinic unsaturation is methylene interrupted. The equations (7) and (8) used to calculate

these criteria were developed previously (Knothe, 2002) as follows:

APE = ∑ (apn × ACn) (7)

BAPE = ∑ (bpn ×ACn) (8)

where apn and bpn are the number of allylic and bis-allylic positions in a specific fatty acid,

respectively, and ACn is the amount (mass-percent) of each fatty acid in the mixture.

CP is defined as the temperature at which the solid phase begins to form, is another feature

related to biodiesel cold flow properties and is more favourable as an industry standard than CFPP as

it is more indicative of biodiesel performance in the field. Pour point (PP) is the lowest temperature

at which the fuel becomes semi solid and loses its flow characteristics being no longer pumpable;

hence it is a measure of the fuel gelling point. The pour point is always lower than the cloud point.

CP = (0.526 × C16) – 4.992 (9)

PP = (0.571 × C16) – 12.240 (10)

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Equations (9) and (10) were used to estimate the CP and PP value on the basis of C16:0

content (wt. %) in FA profiles.

In addition to the above mentioned chemical parameters, other physical parameters like

viscosity (υ), density (ρ) and higher heating value (HHV), also critical for the fuel quality of the

biodiesel were estimated from the FAME profiles of the tested cyanobacterial strains following the

equations (11) – (13) (Ramirez-Verduzco et al., 2012) respectively.

ln(υi) = −12.503 + 2.496 × ln (Mi) − 0.178 × N (11)

ρi = 0.8463 + 4.9/ Mi + 0.0118 × N (12)

HHVi = 46.19 – 1794/ Mi − 0.21 × N (13)

where (ʋi is the kinematic viscosity of at 40 °C in mm2/s; ρi is the density at 20 °C in g/cm3; and

HHVi is the higher heating value in MJ/kg of ith FAME.

2.8 Cyanobacterial strain selection based on biodiesel parameters

Selection of suitable cyanobacterial strains involves multi-criteria decision analyses (MCDA)

considering above mentioned chemical and physical fuel quality parameters into an account. MCDA

analyses using Preference Ranking Organisation Method for Enrichment Evaluation (PROMETHEE)

and Graphical Analysis for Interactive Assistance (GAIA) showed promising method towards the

preferred solution in decision making algorithms (Brans and Mareschal, 2005). In this work, tested

cyanobacterial strains were ranked using PROMETHEE-GAIA algorithm (Visual PROMETHEE,

v1.4.0.0) for biodiesel production suitability. This algorithm performs principal component analysis

(PCA) to reduce the dimensionality of the problem to two spatial dimensions (called the GAIA

plane) for visual interpretation of the problem. Unlike PCA, PROMETHEE-GAIA has a critical

difference in that it provides a decision vector for the analyst. This enables the decision maker to

view different alternatives in the GAIA plane, and to be directed towards preferred solutions by the

decision vector. In this study, ranking was undertaken by giving equal weight to all biodiesel quality

parameters with the threshold values presented in Table 3.

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3. Results and discussion

3.1 Cyanobacterial growth kinetic parameters

In the present study, a total of eleven heterocystous cyanobacterial strains were isolated

(Table 1). Among the eleven isolates, six strains were isolated from rice field and fresh water ponds

(S.No. 1 to 6, Table 1) representing four different genera viz. Camptylonemopsis, Calothrix, Nostoc

and Anabaena, whereas four symbiotic cyanobacterial strains (S.No. 7 to 10, Table 1) were isolated

from Azolla species collected from different regions and one symbiotic Nostoc sp. MBDU 007 was

from Cycas circinalis. All the isolates are filamentous heterocystous forms. The characteristics

morphological features are used for genus and species assignment, which is further authenticated

through 16S rRNA gene sequencing. GenBank accession number of each isolates are shown in Table

1. Biomass productivity, lipid content and volumetric lipid productivity were analyzed for the eleven

cyanobacterial isolates and the results are shown in Table 1. All the tested cyanobacterial strains

showed good biomass productivity except Anabaena sphaerica MBDU 105, but it showed the

highest lipid content of 18.651(% dwt) among other cyanobacterial strains. The biomass productivity

in terms of dry biomass for the tested cyanobacterial strains ranged from 9.33-20.67 mg L−1

day−1

. A

lipid content of 11.221 and 10.382 (% dwt) was shown to be produced by freshwater Calothrix sp.

MBDU 013 and symbiotic Calothrix dolichomeres MBDU 013 respectively.

Biomass productivity and lipid content (% dwt) are the two most studied parameters in search

of the prominent strain for large-scale cultivation of cyanobacteria for biofuel production (Griffiths

and Harrison, 2009). In fact, many cyanobacterial species have been subjected to screening for lipid

production, but no substantial total lipids have been found in cyanophycean organism examined in

the laboratory under normal growth condition. During stress, an average of 9.8 (% dwt) lipid content

was shown by cyanobacteria compared to 45.7 (% dwt) of lipid content for green algae (Hu et al.,

2008). Although it is widely accepted that stress conditions increased the total lipid content up to 42

(% dwt) in many eukaryotic microalgae (Chlorella and Botryococcus), this adaptive way is still not

12

confirmed in cyanobacteria species (Hu et al., 2008). The lipid content of several strains of

cyanobacteria reported earlier ranged from 5-45 (% dwt) depending on the species and

environmental conditions including nutrients and stress conditions (Griffiths and Harrison, 2009;

Karatay and Donmez, 2011).

The comparison of total lipid content (% dwt) of heterocystous cyanobacteria reported so far

including the strains tested in this study are shown in Fig. 1. Through this comparison, it is revealed

notably that the ability of tested heterocystous cyanobacterial strains to accumulate lipid surpassed

the average total lipid content of 7.9-12.9 (% dwt) reported for heterocystous cyanobacterial species

earlier (Vargas et al., 1998; Sahu et al., 2013).

The high intracellular lipid content was one of the key criteria for evaluating the potentiality

of microalgal species for biodiesel production. However, lipid content alone is an inappropriate

measure for yield, since it also lies on growth rate and biomass production. Current studies started to

concentrate more on lipid productivity for biodiesel production. Lipid productivity, the product of

biomass productivity and lipid content, is one of the most obvious and easily quantifiable features

related to biodiesel production (Griffiths and Harrison, 2009). Therefore, it is necessary to further

assess the lipid productivity of species which have been promoted for their high lipid content, since

the selection of a suitable species for scale-up production also depends on growth rate, biomass, and

lipid productivity.

Lipid productivity varied between 0.645-2.358 mg L−1

day−1

for the tested heterocystous

cyanobacterial strains. Surprisingly, the top biomass producer in the present study i.e., Calothrix sp.

MBDU 013 correspond to the top lipid producers, while its lipid content was lower than Anabaena

sphaerica MBDU 105 which stands second in terms of lipid productivity. On the other hand, Nostoc

sp. MBDU 013, Anabaena sp. MBDU 006 and Nostoc sp. MBDU 007 showed an average lipid

productivity of 1.458 mg L−1

day−1

, though the lipid content (% dwt) of these strains are lower.

Similar to our results, a biomass productivity of 30.8 mg L−1

day−1

and a lipid content of 23.7 (%

13

dwt) was reported for filamentous heterocystous cyanobacterium, Trichormus sp. CENA77 (Da Ros

et al., 2013). Whereas a maximum lipid productivity of 14.2 mg L−1 day−1 and the biomass

productivity of 52.7 mg L−1 day−1 was reported for the unicellular cyanobacterium, Synechococcus

sp.PCC7942 by the same author (Da Ros et al., 2013). Therefore, biomass productivity may be

considered as an adequate criterion for biodiesel production only when associated with lipid

productivity (Lp) (Griffiths and Harrison, 2009).

3.2 Comparison of FAME profiles

Besides the favorable lipid productivity, the selected strains should have a FA profile that

allows obtaining biodiesel with the desired physico-chemical properties to be used as a fuel. The FA

profile of tested heterocystous cyanobacterial strains characterized by GC yielded 21 FAs with

carbon chains ranging from (C4-C24) and different degrees of unsaturation. Through the analysis of

the FAs composition data in Table 2, a useful comparison of the eleven cyanobacterial lipids with

respect to the saturated, monounsaturated and polyunsaturated compounds are provided in Fig.2,

which indicated that the composition of all the tested cyanobacterial strains varied significantly.

FAME profiles of all the tested cyanobacterial strains showed high amount of saturated fatty acids

(SFAs) ranged from 35.7 3% to 77.40%; compared to monounsaturated fatty acids (MUFAs) (3.73%

to 15.63%) and polyunsaturated fatty acids (PUFAs) (9.71% to 48.46%). Highest level of SFAs were

present in Nostoc sp. MBDU 007 (77.40%) followed by Nostoc sp. MBDU 013 (67.06%), Anabaena

sphaerica MBDU 105 (67.02%), Calothrix marchica MBDU 602 (65.82%) and Calothrix sp.

MBDU 013 (62.84%). Highest percentage of MUFAs were present in Nostoc sp. MBDU 009

(17.67%) followed by Anabaena sphaerica MBDU 105 (15.63%), Calothrix linearis MBDU 005

(14.56%) and Nostoc piscinale MBDU 013 (14.35%). Our results are in agreement with other reports

in the literature indicating that cyanobacteria, especially the filamentous strains, have a high content

of PUFAs. Vargas et al. (1998) reported that twelve different species of heterocystous cyanobacterial

strains contain PUFA ranging from 23.2% to 41.70% of the dry weight. Except Nostoc sp. MBDU

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009, Nostoc piscinale MBDU 013 and Anabaena sp. MBDU 006, the other tested cyanobacterial

strains were considered as suitable feedstock for biodiesel production. The consensus view is that the

most favourable biodiesel would have rather low levels of polyunsaturated and low levels of

saturated FAs to decrease oxidative stability and cold flow problems and monounsaturated fatty

acids of palmitoleic acid (16:1) and oleic acid (18:1) were capable of giving the finest compromise

between oxidative stability and cold flow (Hoekman et al., 2012).

It was shown that the most common feedstocks suitable for biodiesel production were

enriched in the five most common C16–C18 fatty acids, namely, palmitic (16:0), stearic (18:0), oleic

(18:1), linoleic (18:2), and linolenic (18:3) acids (Hoekman et al., 2012). The data in Table 2

highlighted that out of eleven cyanobacterial strains tested, ten strains possessed considerable

amounts of C16 and C18 FAs, in the range of 40% to 56%, except by Nostoc sp. MBDU 007.

Overall, palmitic acid (C16:0) was the most common FAs in these oils, with the individual amounts

varying significantly.

Oleic acid (C18:1) appeared to be the second most common FA in the tested cyanobacterial

strains, with Anabaena sp. MBDU 006, Nostoc piscinale MBDU 013 and Nostoc sp. MBDU 009

containing highest amounts of 13.57%, 12.07% and 11.18%, respectively. Our results corroborated

with the concept that C18 FAs mainly composed of PUFAs were less prominent in algal oils than in

vegetable oils (Knothe, 2011). Following palmitic acid (C16:0), lauric acid (C12:0) seemed to be a

very common saturated FA in the tested cyanobacterial strains.

3.3 Evaluation of biodiesel fuel properties from FAME profiles

Thirteen important biodiesel fuel properties for the eleven tested heterocystous cyanobacterial strains

were shown in Table 3. A systematic analysis of the FAME composition and comparative fuel

properties is very important for suitable strain selection for biodiesel production. The estimated CN

for the tested cyanobacterial strains varied from 42.61 to 65.02, with an average value of 56.80. The

cetane number (CN) is indicative of the time delay in the ignition of fuel, for diesel cycle engines.

15

The higher the CN, the shorter is the ignition time. CN increases with the length of the unbranched

carbon chain of the FAME components (Knothe, 2005). According to the ASTM D6751

international standard, the minimum CN should be at 47, where as in IS 15607 (India) and EN 14214

(Europe) standards 51 is the minimum CN value of biodiesel (Hoekman et al., 2012). In the present

study, except Anabaena sp. MBDU 006 and Nostoc piscinale MBDU 013, all the other tested

cyanobacterial strains showed good CN value (between 40 and 65) for the biodiesel properties.

Another biodiesel quality parameter not included in the ASTM or Indian standards but

deserved a place in EN 14214 is IV which represents the DU by weighted sum of the masses of

MUFA and PUFA and play an important role in biodiesel oxidative stability. Except for the Nostoc

piscinale MBDU 013, IV of all other tested cyanobacterial strains fall within the maximum limit of

120 as per the EN 14214 standard. Similar to our study, lower IV values of 57 and 68 g I2/100 g

were shown for M. aeruginosa NPCD-1, and Trichormus sp. CENA77, respectively (Da Ros et al.,

2013). High unsaturation levels may result in polymerization of glycerides, formation of deposits

and susceptibility to oxidative attack (Francisco et al., 2010).

The key low-temperature flow properties for winter fuel specification are CFPP, CP and PP.

There are no European or US specifications for low temperature properties (each country is free to

determine its own limits according to local weather conditions), but it is well known that biodiesel

fuels suffer from cold flow properties way more (i.e. they are higher) than mineral diesel fuel.

Saturated FA has higher melting points than unsaturated FA compounds. When most saturated

molecules of FA esters are present in oils, crystallization may occur at temperatures within the

normal engine operation range (Franciso et al., 2010), what gives biodiesel poor CFPP properties.

Present investigation revealed that the levels of stearic acid were generally very low (below 3.18 %)

in seven of the eleven tested cyanobacterial strains (Table 2) and contributed for the lower

temperatures of CFPP (Table 3).

16

LCSF of lipid feedstock is a critical parameter for oxidation stability, cetane number, IV and

cold filter plugging point (CFPP) of the biodiesel obtained. It was reported that the longer the

biodiesel carbon chains, the worse their low-temperature properties. This parameter is, therefore, an

important element in determining the cold response of the produced biodiesel. Among the tested

cyanobacterial strains, the highest LCSF value of 27.47 was observed in the FAME profile of

Nostoc piscinale MBDU 013, whereas Anabaena sp. MBDU 006 showed only 5.08 (wt. %). In

another study using cyanobacterial strains like M. aeruginosa NPCD-1, Synechococcus sp. PCC7942

and Trichormus sp. CENA77, a higher LCSF values were shown, since they contain a higher

concentration of palmitic and stearic FAs (Da Ros et al., 2013).

CP value is closely affected by the solid phase consisting mainly of the saturated methyl

esters at the equilibrium point and can accurately be predicted only by the amount of saturated

methyl esters (C16:0 and C18:0), regardless of the composition of unsaturated esters fraction (Sarin

et al., 2009). There are no definite specifications of cloud point (CP), due to the different climate

conditions prevailing in the United States and Europe. In the present study, CP values for the tested

cyanobacterial strains vary differently corroborating with other studies using microalgae (Song et al.,

2013). In terms of PP, our results are in agreement with the statement that the pour point is always

lower than the cloud point (Sarin et al., 2009).

The APE and BAPE are effective in predicting the oxidation stability of the biodiesel

(Knothe, 2002). For both the parameters, Anabaena sp. MBDU 006 showed a higher value, while

Nostoc sp. MBDU 007 showed the lower value among the tested cyanobacterial strains.

There is no specification on the higher heating value in any of the biodiesel standards

mentioned previously. It is already known that the energy content of fatty acid methyl esters is

directly proportional to chain length (again for pure fatty acids). The FAME-derived HHVs of all

tested cyanobacterial strains, except Calothrix sp. MBDU 013 and Nostoc piscinale MBDU 013

were found to comply within the set range (39.8–40.4 MJ kg−1

) for regular biodiesel, which is

17

normally 10% to 12% less than the petroleum-derived diesel (46MJ kg−1) (Ramirez-Verduzco et al.,

2012). HHV value of 41.5 was shown for the filamentous non heterocystous cyanobacterium

Lyngbya kuetzingii by Song et al. (2013).

Density (ρ), for which a standard value has been set at 0.86–0.90 g cm−3

according to EN

14214, is another important parameter for biodiesel quality. FAME profile derived ρ-values of eleven

cyanobacterial strains were found to be within this range. Similar ρ-values were found in microalgal

and cyanobacterial species tested already (Song et al., 2013).

Furthermore, biodiesel must have an appropriate kinematic viscosity (υ) to ensure that an

adequate fuel supply reaches injectors at different operating temperatures (Ramirez-Verduzco et al.,

2012). Since υ is inversely proportional to temperature, it also affects the CFPP for engine operation

at low temperatures. Kinematic viscosity limits are set to 2.5–6.0 mm2 s−1, 1.9–6.0 mm2 s−1 and 3.5–

5.0 mm2 s−1 as per IS 15607, ASTM 6751-02 and EN 14214 respectively. All cyanobacterial species

listed in Table 3 were in the prescribed viscosity range with 1.48–4.66 mm2 s−1, therefore meeting

the standards.

3.4 Selection of suitable cyanobacterial strains for biodiesel production

To be an ideal source of sustainable biodiesel, selected cyanobacterial strains should contain

sufficient lipid with good biodiesel properties. Two free-living and two symbiotic cyanobacterial

strains, Nostoc sp. MBDU 009, Nostoc sp. MBDU 013, Nostoc sp. MBDU 007 and Nostoc piscinale

MBDU 013 were identified to have poor biodiesel properties. A multi-criteria decision method

(MCDM) software PROMETHEE-GAIA was used to make objective selections for large-scale

production. Suitable cyanobacterial strains were selected from the tested eleven strains (Fig. 3 (a, b))

based on the following equally weighed biodiesel fuel characteristics: IV, LCSF, CFPP, DU, CN, SV

υ, ρ, HHV; SFAs, MUFA and PUFA, CP, PP, APE, BAPE including lipid productivity. The

preference functions of criteria (fuel properties) were modeled as Min (i.e., lower values are

preferred for good biodiesel) or Max (higher values are preferred for good biodiesel) and was shown

18

in Table 3. The length of the criteria vectors and their directions indicate the influence of these

criteria on the decision vector (red line in Fig. 3a) and preference of the species (Fig. 3a). For

example the CN is maximum in Anabaena sphaerica MBDU 105, Calothrix linearis MBDU 005 and

Nostoc sp. MBDU 007 whereas IV is at the minimum value for these organisms. On the other hand,

Anabaena sphaerica MBDU 105, Calothrix sp. MBDU 013 and Calothrix dolichomeres MBDU 013

showed maximum of total lipid, whereas Nostoc sp. MBDU 013, Nostoc piscinale MBDU 013 and

Calothrix linearis MBDU 005 represented the minimum according to Fig. 3a.

The decision vector indicates the most preferable species, i.e., those that align with the

direction of this vector and the outermost criteria in the direction of the decision vector are the most

preferable (Brans and Mareschal, 2005). For example CN, IV, LCSF, CFPP and lipid productivity in

Fig. 3a were correlated, whereas PUFA was not-correlated with these criteria and SFAs had no or

little influence on these. The length of the criteria vectors indicates their influence on the decision

vector and therefore the ranking (Brans and Mareschal, 2005). Very short criteria vectors (ρ, υ and

HHV) indicate that the microalgal species showed little to no variance in these important biodiesel

quality parameters, thus they do not influence the length and direction of the decision vector (Fig.

3a). It can be concluded that removal of these biodiesel quality parameters i.e. ρ, υ and HHV will not

change the ranking of cyanobacterial biodiesel and these are therefore, at least in this case, not

effective components for the selection of suitable strains for biodiesel production. In contrast, CFPP,

SFAs, and LCSF were highly variable criteria and they had a strong effect on the decision vector.

Based on Fig. 3a and the calculated outranking flows, the most suitable species for biodiesel

production in decreasing order are Anabaena sphaerica MBDU 105, Calothrix sp. MBDU 013,

Calothrix linearis MBDU 005, Calothrix marchica MBDU 602, Calothrix dolichomeres MBDU

013, and Camptylonemopsis minor MBDU 013 (Fig. 3b).

4. Conclusion

19

Biomass productivity (g L−1 day−1), oil content (% dwt) and lipid productivity (Lp) seemed to

be the adequate criteria for estimating the potential of different cyanobacterial species for biodiesel

production. Among the eleven heterocystous cyanobacterial strains tested in this study, two fresh

water isolates i.e. Calothrix sp. MBDU 013 and Anabaena sphaerica MBDU 105 have high biomass,

volumetric lipid productivity and desirable biodiesel qualities. In conclusion, this paper highlights

the role of qualitative composition of cyanobacterial oil and demonstrates the dependence of

biodiesel fuel properties such as CN, DU, BAPE, CP and CFPP on the FAME profile.

Acknowledgements

The authors are grateful to the University Grants Commission (UGC), Government of India,

for the financial support. AMP Anahas acknowledges the Maulana Azad National Fellowship

Scheme (MANF) for the fellowship. We thank Mr. Ajai Kumar of Advanced Instrumentation

Research Facility (AIRF) Jawaharlal Nehru University, New Delhi for GC analysis. DST-PURSE

program is kindly acknowledged for providing the CLSM facility to BDU.

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23

Table captions

Table 1. Cyanobacterial strains used in this study with their biomass productivity, lipid content and

lipid productivity

Table 2. Fatty acids compositional profiles of the screened heterocystous cyanobacterial strains ((%

wt) of total FAME)

Table 3. Estimated biodiesel properties from the FAME profiles of eleven heterocystous

cyanobacterial strains.

Figure captions

Fig. 1. Comparison of total lipid content (%, dry weight) of eleven cyanobacterial strains in this

study and other cyanobacterial species (a–n) from the literatures under the same cultivation

conditions. Key to references: a – l (Vargas et al., 1998); m, n (Sahu et al., 2013).

Fig. 2. The percentage of saturated fatty acids, monounsaturated fatty acids, polyunsaturated fatty

acids and others in fatty acid compositions of tested cyanobacterial strains.

Fig. 3. (a) Graphical Analysis for Interactive Assistance (GAIA) plot of eleven cyanobacterial strains

from the present study showing 14 criteria (13 biodiesel properties from Table 3, lipid productivity

from Table 1) and decision vector, and (b) corresponding ranking of species based on their

outranking flow.

a d e b i j g h c f k l m n

To

tal li

pid

co

nte

nt

(% D

W)

Fig. 1

Figure 1-2

100

Cam

pty

lon

em

op

sis

min

or

MB

DU

013

Calo

thri

x m

arc

hic

a M

BD

U 6

02

Calo

thri

x s

p. M

BD

U 0

13

An

ab

aen

a s

ph

aeri

ca M

BD

U 1

05

No

sto

c s

p. M

BD

U 0

09

No

sto

c s

p.

MB

DU

013

Calo

thri

x lin

eari

s M

BD

U 0

05

Calo

thri

x d

olich

om

ere

s M

BD

U 0

13

An

ab

aen

a s

p. M

BD

U 0

06

No

sto

c p

iscin

ale

MB

DU

013

No

sto

c s

p. M

BD

U 0

07

70

40

20

90

80

10

60

50

30

0

Fatt

y a

cid

co

nte

nt

(% )

Fig. 2

Rank

Cyanobacterial strains Phi

1

Anabaena sphaerica MBDU

105

0.1099

2

Calothrix sp. MBDU 013 0.0659

3

Calothrix linearis MBDU 005 0.0288

4

Calothrix marchica MBDU 602

0.0213

5

Calothrix dolichomeres MBDU

013

0.0080

6

Camptylonemopsis minor

MBDU 013

0.0023

7

Anabaena sp. MBDU 006 0.0008

8

Nostoc sp. MBDU 007 -0.0299

9

Nostoc sp. MBDU 013 -0.0171

10 Nostoc sp. MBDU 009

-0.0454

11 Nostoc piscinale MBDU 013 -0.1618

(a) (b)

Fig. 3 Figure 3

Table 1.

S.

No.

Cyanobacterial strains GenBank

Accession

no.

Source of Isolation Biomass

productivity

(mg.L-1

.day-1

)

Lipid content

(% dwt)

Lipid

productivity

(mg.L-1

.day-1

)

1. Camptylonemopsis minor

MBDU 013

KC971096 Rice field, Thiruverumbur,

Tiruchirappalli 10° 48' 18'' N, 78° 41' 7'' E

14.13 ± 0.001 7.910 ± 0.218 1.202 ± 0.017

2. Calothrix marchica MBDU 602

KC971090 Rice field, Budalur, Thanjavur

10° 79' 67'' N, 78° 97' 6'' E

17.33 ± 0.001 6.774 ± 0.140 1.083 ± 0.022

3. Calothrix sp. MBDU 013 KC971094 Fresh water pond,

Thiruverumbur,

Tiruchirappalli

10° 48' 18'' N, 78° 41' 7'' E

20.67 ± 0.000 11.221 ± 0.137 2.358 ± 0.141

4. Nostoc sp. MBDU 009 KP096229 Rice field, Mathur,

Tiruchirappalli

10° 72' 70'' N, 78° 58' 5'' E

16.00 ± 0.001 7.903 ± 0.305 1.340 ± 0.275

5. Nostoc sp. MBDU 013 JN542385

Fresh water pond,

Thiruverumbur,

Tiruchirappalli

10° 48' 18'' N, 78° 41' 7'' E

20.00 ± 0.001 6.749 ± 0.131 1.419 ± 0.095

6. Anabaena sphaerica MBDU

105

KP096231 Rice field, Poondi, Thanjavur

10° 85' 51'' N, 78° 94' 9'' E

9.33 ± 0.000 18.651 ± 0.243 1.681 ± 0.208

7. Calothrix dolichomeres

MBDU 013

KP096227 Azolla sp.

Thiruverumbur,

Tiruchirappalli

10° 48' 18'' N, 78° 41' 7'' E

11.40 ± 0.001 10.382 ± 0.208 1.048 ± 0.010

8. Calothrix linearis MBDU 005 KP096228 Azolla sp.

Kallanai, Thanjavur

10° 83' 21'' N, 78° 81' 7'' E

18.33 ± 0.000 6.426 ± 0.223 1.126 ± 0.071

9. Nostoc piscinale MBDU 013 KP096230 Azolla sp. 17.33 ± 0.003 4.682 ± 0.996 0.645 ± 0.092

Thiruverumbur,

Tiruchirappalli 10° 48' 18'' N, 78° 41' 7'' E

10. Anabaena sp. MBDU 006 KC971092 Azolla sp. Kollidam river, Tiruchirappalli

10° 87' 00'' N, 78° 69' 9'' E

16.33 ± 0.001 8.620 ± 0.246 1.463 ± 0.044

11. Nostoc sp. MBDU 007 KP096232 Cycas circinalis, Gundur,

Tiruchirappalli 10° 73' 51'' N, 78° 73' 06'' E

14.00 ± 0.002 9.577 ± 1.988 1.492 ± 0.128

Table 2.

Fatty acids Names 1 2 3 4 5 6 7 8 9 10 11

C4:0 Butyric 5.09 1.76 3.24 1.45 6.75 6.54 1.38 n.d. 3.52 0.91 n.d.

C6:0 Caproic 2.08 2.21 0.44 0.13 1.21 0.68 0.19 n.d. 0.33 0.15 n.d.

C8:0 Caprylic n.d. 2.58 2.15 0.31 1.10 1.11 1.95 1.11 0.13 n.d. 13.40

C10:0 Capric 5.53 7.72 5.57 0.90 3.08 5.36 6.14 5.60 0.14 n.d. 4.95

C11:0 Undecanoic n.d. 0.49 0.42 0.07 0.41 n.d. 0.34 0.34 0.04 n.d. n.d.

C12:0 Lauric 6.98 9.27 6.92 1.95 5.81 7.36 7.08 6.69 0.16 0.78 7.99

C13:0 Tridecanoic 3.13 4.04 3.69 2.35 6.96 2.84 2.99 3.98 0.56 0.72 4.45

C14:0 Myristic 1.26 0.69 0.69 0.82 0.95 0.60 0.69 0.57 n.d. n.d. 0.80

C14:1 Myristoleic n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d.

C15:0 Pentadecanoic n.d. 3.88 6.77 1.83 4.79 5.89 3.39 5.35 0.10 1.23 6.05

C15:1 cis-10- Pentadecanoic 3.74 1.72 1.52 2.03 0.73 2.37 2.21 3.41 0.78 n.d. n.d.

C16:0 Palmitic 20.38 25.42 23.52 4.78 13.06 25.23 26.13 27.95 0.84 1.48 19.61

C16:1 Palmitoleic n.d. 0.17 0.80 0.17 2.14 0.72 0.38 0.20 n.d. 0.13 1..35

C17:0 Heptadecanoic 2.22 2.25 1.57 5.84 5.75 3.65 2.19 2.10 3.22 3.23 4.96

C17:1 cis-10-Heptadecanoic n.d. 5.10 3.47 1.87 4.88 4.55 3.44 5.26 n.d. n.d. 6.21

C18:0 Stearic 2.67 2.57 5.27 6.15 8.32 0.89 3.18 2.33 6.24 8.18 3.02

C18:1n9t Elaidic 1.53 3.86 1.78 1.04 1.38 0.74 2.17 2.95 n.d. n.d. 0.66

C18:1n9c Oleic 2.46 1.86 1.02 11.18 2.76 0.79 3.73 2.15 13.57 12.07 1.82

C18:2n6t Linolelaidic 5.74 2.93 5.17 3.01 4.62 2.64 1.75 2.94 16.22 15.96 3.02

C18:2n6c Linoleic 2.93 3.91 7.54 12.71 6.17 2.91 3.43 3.44 n.d. n.d. 2.16

C20:0 Arachidic 4.49 2.89 2.36 3.13 8.83 4.11 3.25 2.61 16.51 0.84 10.39

C18:3n6 γ-Linolenic 15.35 8.53 11.66 10.31 2.28 2.57 11.54 5.86 n.d. 15.06 2.75

C20:1n9 cis-11-Eicosenoic n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d.

C18:3n3 α-Linolenic n.d. n.d. n.d. n.d. n.d. 1.93 n.d. n.d. 13.99 n.d. n.d.

C21:0 Henicosanoic 5.15 n.d. n.d. 10.96 n.d. 2.71 2.59 n.d. n.d. 13.71 1.73

C20:2 cis-11,14-Eicosadienoic n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d.

C22:0 Behenic n.d. n.d. 0.16 n.d. n.d. n.d. 0.18 n.d. n.d. n.d. n.d.

C20:3n6 cis-8,11,14-Eicosatrienoic n.d. 0.62 0.19 7.28 0.26 n.d. 0.38 n.d. 10.00 9.74 1.76

C22:1n9 Erucic n.d. n.d. n.d. n.d. n.d. 1.53 n.d. n.d. n.d. n.d. n.d.

C20:3n3 cis-11,14,17-Eicosatrienoic n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d.

C20:4n6 Arachidonic n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. 7.52 n.d. n.d.

C23:0 Tricosanoic n.d. n.d. n.d. 4.41 n.d. n.d. n.d. n.d. n.d. 7.07 n.d.

C22:2 cis-13,16-Docosadienoic n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d.

C24:0 Lignoceric n.d. n.d. n.d. 2.48 n.d. n.d. n.d. n.d. 3.87 n.d. n.d.

C20:5n3 cis-5,8,11,14,17-

Eicosapentaenoic

n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d.

C24:1 Nervonic n.d. n.d. n.d. 1.34 n.d. n.d. n.d. n.d. n.d. 3.43 n.d.

C22:6n3 cis-4,7,10,13,16,19-

Docosahexaenoic

n.d. 0.41 n.d. n.d. n.d. n.d. n.d. n.d. 0.71 2.08 n.d.

NI 9.20 5.01 3.98 2.34 7.67 12.16 17.34 14.50 1.45 3.45 2.84

NI not identified, n.d. not detected

1 - Camptylonemopsis minor MBDU 013; 2 -Calothrix marchica MBDU 602; 3- Calothrix sp. MBDU 013; 4- Nostoc sp. MBDU 009; 5- Nostoc sp. MBDU 013; 6-

Anabaena sphaerica MBDU 105; 7- Calothrix dolichomeres MBDU 013; 8- Calothrix linearis MBDU 005; 9 -Nostoc piscinale MBDU 013; 10 - Anabaena sp. MBDU 006;

11- Nostoc sp. MBDU 007

Table 3

Cyanobacterial strains CN SV

(mg

KOHg−1

)

IV

(g

I2100g−1

fat)

DU

(wt. %)

LCSF

(wt. %)

CFPP

(ºC)

CP

(ºC)

PP

(ºC)

APE BAPE υ

(mm2 −1

)

ρ

(g m−3

)

HHV

(MJ

Kg−1

)

SFA

(%)

MUFA

(%)

PUFA

(%)

Biodiesel Standard EN 14214 ≥ 51 - ≤120 - - ≤5/-20 - - - - 3.5 -5.0 0.86-

0.90

NA - - -

Biodiesel Standard ASTM

D6751-02

≥ 47 - NA - - NA - - - - 1.9 -6.0 0.86-

0.90

NA - - -

Biodiesel Standard IS 15607 ≥ 51 - NA - - 6/18 - 3/15 - - 2.5 -6.0 0.86-0.90

NA - - -

Min/Max max min min min min min max max min min max max max max max min

Threshold value for

PROMETHEE

51 - 120 - - 18 - - - - - 0.90 - - - -

Camptylonemopsis minor

MBDU 013

55.61 227.36 65.29 107.10 7.79 8.00 5.72 -0.60 39.39 52.07 2.18 0.88 39.12 59.02 7.74 24.03

Calothrix marchica MBDU 602 57.96 235.27 51.24 98.69 6.71 4.63 8.38 2.27 23.91 36.50 1.59 0.88 39.01 65.82 12.73 16.43

Calothrix sp. MBDU 013 55.33 233.74 63.64 111.98 7.60 7.41 7.38 1.19 36.04 51.57 2.42 0.88 38.89 62.84 8.60 24.57

Nostoc sp. MBDU 009 52.82 202.43 90.84 113.30 11.65 20.12 -2.47 -9.51 36.35 64.32 4.66 0.88 39.76 46.66 17.67 33.32

Nostoc sp. MBDU 013 61.68 236.27 37.72 93.77 14.29 28.44 1.87 -4.78 15.35 30.28 1.70 0.88 39.17 67.06 11.91 13.35

Anabaena sphaerica MBDU

105

62.85 228.67 32.51 87.17 7.08 5.76 8.28 2.16 14.58 21.69 2.89 0.88 39.56 67.02 10.74 10.07

Calothrix dolichomeres MBDU

013

59.88 213.33 53.31 96.09 7.74 7.84 8.75 2.68 28.33 39.48 3.08 0.88 39.88 61.75 3.73 17.16

Calothrix linearis MBDU 005 65.02 195.23 41.01 83.19 6.57 4.19 9.71 3.71 18.12 29.63 1.48 0.88 40.81 58.67 14.56 12.26

Nostoc piscinale MBDU 013 42.61 206.25 134.00 132.67 27.47 69.84 -4.54 -11.76 44.22 74.02 1.49 0.87 38.96 35.73 14.35 48.46

Anabaena sp. MBDU 006 48.85 188.38 117.39 123.48 5.08 -0.49 -4.20 -11.39 46.22 74.12 4.57 0.87 39.94 38.35 15.63 42.56

Nostoc sp. MBDU 007 62.22 238.78 30.79 96.82 13.87 27.09 5.32 -1.04 10.70 18.38 4.03 0.88 39.17 77.40 10.05 9.71

24

Highlights

• Eleven heterocystous cyanobacterial strains were screened for biodiesel production.

• Biomass and lipid productivity along with the lipid content were examined.

• Biodiesel quality parameters were evaluated from FAME profiles.

• The best strain was selected using PROMETHEE-GAIA algorithm.

• Anabeana sphaerica MBDU105 is selected as the best strain for biodiesel production.