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

Accepted Manuscript 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 PII: DOI: Reference:

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

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Bioresource Technology

Received Date: Revised Date: Accepted Date:

31 August 2014 31 October 2014 2 November 2014

Please cite this article as: Anahas, A.M.P., Muralitharan, G., Isolation and screening of heterocystous cyanobacterial strains 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: [email protected] 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

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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

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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, 5

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 6

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. 7

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), 8

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 = ∑ (ap n × ACn)

(7)

BAPE = ∑ (bp n ×ACn)

(8)

where ap n and bp n 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) 9

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. 10

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 11

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 (% 12

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 13

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. 14

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).

15

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 16

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 17

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

18

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. References 1. Atsumi, S., Hanai, T., Liao, J.C., 2008. Non-fermentative pathways for synthesis of branched-chain higher alcohols as biofuels. Nature 451, 86–89. 2. Balasubramanian, L., Subramanian, G., Nazeer, T.T., Simpson, H.S., Rahuman, S.T., Raju, P., 2012. Cyanobacteria cultivation in industrial wastewaters and biodiesel production from their biomass: A review. Biotechnol. Appl. Biochem. 59, 220–225. 3. Basova, M.M., 2005. Fatty acid composition of lipids in microalgae. Int. J. Algae 7, 33-57. 4. Brans, J. P., Mareschal, B., 2005. PROMETHEE methods, multiple criteria decision analysis: state of the art surveys, 163-186. 5. Chisti, Y., 2008. Biodiesel from microalgae beats bioethanol. Trends Biotechnol. 26,126 131. 19

6. Da Ros, P.C.M., Silva, C.S.P., Silva-Stenico, M.E., Fiore, M.F., De Castro, H.F., 2013. Assessment of chemical and physic-chemical properties of cyanobacterial lipids for biodiesel production. Mar. Drugs 11, 2365–2381. 7. Folch, J., Lees, M., Sloan-Stanley, G.H., 1957. A simple method for the isolation and purification of total lipids from animal tissue. J. Biol. Chem. 226, 497-509. 8. Francisco, E.C., Neves, D.B., Jacob-Lopes, E., Franco, T.T., 2010. Microalgae as feedstock for biodiesel production: carbon dioxide sequestration, lipid production and biofuel quality. J. Chem. Technol. Biotechnol. 85, 395–403. 9. Gressel, J., 2008. Transgenics are imperative for biofuel crops. Plant Sci. 174, 246–263. 10. Griffiths, M.J., Harrison, S.T.L., 2009. Lipid productivity as a key characteristic for choosing algal species for biodiesel production. J. Appl. Phycol. 21, 493–507. 11. Hoekman, S.K., Broch, A., Robbins, C., Ceniceros, E., Natarajan, M., 2012. Review of biodiesel composition, properties, and specifications. Renew. Sust. Energ. Rev. 16, 143–169. 12. Hu, Q., Sommerfeld, M., Jarvis, E., Ghirardi, M., Posewitz, M., Seibert, M., Darzins, A., 2008. Microalgal triacylglycerols as feedstocks for biofuel production: perspectives and advances. Plant J. 54, 621–639. 13. Karatay, S.E., Donmez, G., 2011. Microbial oil production from thermophile cyanobacteria for biodiesel production. Appl. Energ. 88, 3632–3635. 14. Khanal, S.K., 2008. Bioenergy generation from residues of biofuel industries. In: Khanal, S.K., (Ed.), Anaerobic biotechnology for energy production: principles and applications. John Wiley & Sons, Iowa, pp. 161–88. 15. Knothe, G., 2002. Structure indices in FA chemistry: how relevant is the iodine value? J. Am. Oil. Chem. Soc. 79, 847–854.

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16. Knothe, G.H., 2005. Dependence of biodiesel fuel properties on the structure of fatty acid alkyl esters. Fuel Process Technol. 86, 1059–1070. 17. Knothe, G., 2011. A technical evaluation of biodiesel from vegetable oils vs. algae. Will algae-derived biodiesel perform? Green Chem. 13, 3048–3065. 18. Knothe, G., 2013. Production and properties of biodiesel from algal oils. In: Borowitzka, M.A., Moheimani, N.R. (Eds.), Algae for biofuels and energy. Springer, Dordrecht, pp. 207– 221. 19. Liu, X., Sheng, J., Curtiss III, R., 2011a. Fatty acid production in genetically modified cyanobacteria.Proc. Natl. Acad. Sci. USA. 108, 6899-6904. 20. Liu, J., Huang, J., Sun, Z., Zhong, Y., Jiang, Y., Chen, F., 2011b. Differential lipid and fatty acid profiles of photoautotrophic and heterotrophic Chlorella zofingiensis: assessment of algal oils for biodiesel production. Bioresour. Technol. 102, 106–110. 21. Miao, X.L., Wu, Q.Y., 2006. Biodiesel production from heterotrophic microalgal oil. Bioresour. Technol. 97, 841–846. 22. Miller, L., Berger, T., 1985. Bacteria identification by gas chromatography of whole cell fatty acids. Hewlett Packard, Gas Chromatography, Application note 228-41, pp. 1-8. 23. Moreno, J., Rodriguez, H., Vargas, M. A., Rivas, J., Guerrero, M. G., 1995. Nitrogen-fixing cyanobacteria as source of phycobiliprotein pigments. Composition and growth performance of ten filamentous heterocystous strains. J. Appl. Phycol. 7, 17–23. 24. Olguin, E.J., 2012. Dual purpose microalgae–bacteria-based systems that treat wastewater and produce biodiesel and chemical products within a Biorefinery. Biotechnol. Adv. 30, 1031–1046. 25. Ramirez-Verduzco, L.F., Rodriguez-Rodriguez, J.E., Jaramillo-Jacob, A.R., 2012. Predicting cetane number, kinematic viscosity, density and higher heating value of biodiesel from its fatty acid methyl ester composition. Fuel 91, 102–111. 21

26. Rippka, R., Deruells, J., Waterbury, J.B., Herdman, M. Stanier, R.Y., 1979. Generic assignments, strain histories and properties of pure cultures of cyanobacteria. J. Gen. Microbiol. 111, 1- 61. 27. Sahu, A., Pancha, I., Jain, D., Paliwal, C., Ghosh, T., Patidar, S., Bhattacharya, S., Mishra, S., 2013. Fatty acids as biomarkers of microalgae. Phytochemistry 89, 53-58. 28. Saraf, S., Thomas, B., 2007. Influence of feedstock and process chemistry on biodiesel quality. Process Saf. Environ. 85, 360–364. 29. Sarin, A., Arora, R., Singh, N.P., Sarin, R., Malhotra, R.K., Kundu, K., 2009. Effect of blends of Palm-Jatropha-Pongamia biodiesels on cloud point and pour point. Energy 34, 2016–2021. 30. Song, D., Fu, J., Shi, D., 2008. Exploitation of oil-bearing microalgae for biodiesel. Chinese J. Biotechnol. 24, 341–348. 31. Song, M., Pei, H., Hu, W., Ma, G., 2013. Evaluation of the potential of 10 microalgal strains for biodiesel production. Bioresour. Technol. 141, 245-251. 32. Thajuddin, N., Muralitharan, G., Sundaramoorthy, M., Ramamoorthy, R., Ramachandran, S., Akbarsha, M. A., Gunasekaran, M., 2010. Morphological and genetic diversity of symbiotic cyanobacteria from cycads. J. Basic. Microbial. 50, 254-265. 33. Varfolomeev, S.D., Wasserman, L.A., 2011. Microalgae as source of biofuel, food, fodder and medicines. Appl. Biochem. Microbiol. 47, 789–807. 34. Vargas, M.A., Rodriguez, H., Moreno, J., Olivares, H., Delcampo, J.A., Rivas, J., Guerrero, M.G., 1998. Biochemical composition and fatty acid content of filamentous nitrogen-fixing cyanobacteria. J. Phycol. 34, 812–817. 35. Wolk, C.P., 1988. Purification and storage of nitrogen fixing filamentous cyanobacteria. Methods Enzymol. 167, 93–100.

22

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.

23

Figure 1-2

Total lipid content (% DW)

Fig. 1

a

b

c

d

e

f

g

h

i

j

k

l

m

n

Nostoc sp. MBDU 007

Anabaena sp. MBDU 006

Nostoc piscinale MBDU 013

Calothrix linearis MBDU 005

Calothrix dolichomeres MBDU 013

Anabaena sphaerica MBDU 105

Nostoc sp. MBDU 013

Nostoc sp. MBDU 009

Calothrix sp. MBDU 013

Calothrix marchica MBDU 602

Camptylonemopsis minor MBDU 013

Fatty acid content (% )

Fig. 2 100 90

80

70

60

50

40

30

20

10

0

Figure 3

Fig. 3

(a)

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

(b)

Table 1. S. No.

Cyanobacterial strains

1.

Camptylonemopsis minor MBDU 013

2.

Calothrix marchica MBDU 602

KC971090

3.

Calothrix sp. MBDU 013

KC971094

4.

Nostoc sp. MBDU 009

KP096229

5.

Nostoc sp. MBDU 013

JN542385

6.

Anabaena sphaerica MBDU 105 Calothrix dolichomeres MBDU 013

KP096231

8.

Calothrix linearis MBDU 005

KP096228

9.

Nostoc piscinale MBDU 013

KP096230

7.

GenBank Accession no. KC971096

KP096227

Source of Isolation

Rice field, Thiruverumbur, Tiruchirappalli 10° 48' 18'' N, 78° 41' 7'' E Rice field, Budalur, Thanjavur 10° 79' 67'' N, 78° 97' 6'' E Fresh water pond, Thiruverumbur, Tiruchirappalli 10° 48' 18'' N, 78° 41' 7'' E Rice field, Mathur, Tiruchirappalli 10° 72' 70'' N, 78° 58' 5'' E Fresh water pond, Thiruverumbur, Tiruchirappalli 10° 48' 18'' N, 78° 41' 7'' E Rice field, Poondi, Thanjavur 10° 85' 51'' N, 78° 94' 9'' E Azolla sp. Thiruverumbur, Tiruchirappalli 10° 48' 18'' N, 78° 41' 7'' E Azolla sp. Kallanai, Thanjavur 10° 83' 21'' N, 78° 81' 7'' E Azolla sp.

Biomass productivity (mg.L-1.day-1) 14.13 ± 0.001

Lipid content (% dwt) 7.910 ± 0.218

Lipid productivity (mg.L-1.day-1) 1.202 ± 0.017

17.33 ± 0.001

6.774 ± 0.140

1.083 ± 0.022

20.67 ± 0.000

11.221 ± 0.137

2.358 ± 0.141

16.00 ± 0.001

7.903 ± 0.305

1.340 ± 0.275

20.00 ± 0.001

6.749 ± 0.131

1.419 ± 0.095

9.33 ± 0.000

18.651 ± 0.243

1.681 ± 0.208

11.40 ± 0.001

10.382 ± 0.208

1.048 ± 0.010

18.33 ± 0.000

6.426 ± 0.223

1.126 ± 0.071

17.33 ± 0.003

4.682 ± 0.996

0.645 ± 0.092

10.

Anabaena sp. MBDU 006

KC971092

11.

Nostoc sp. MBDU 007

KP096232

Thiruverumbur, Tiruchirappalli 10° 48' 18'' N, 78° 41' 7'' E Azolla sp. Kollidam river, Tiruchirappalli 10° 87' 00'' N, 78° 69' 9'' E Cycas circinalis, Gundur, Tiruchirappalli 10° 73' 51'' N, 78° 73' 06'' E

16.33 ± 0.001

8.620 ± 0.246

1.463 ± 0.044

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-

n.d.

n.d.

n.d.

n.d.

n.d.

n.d.

n.d.

n.d.

n.d.

n.d.

n.d.

Eicosapentaenoic 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-

n.d.

0.41

n.d.

n.d.

n.d.

n.d.

n.d.

n.d.

0.71

2.08

n.d.

9.20

5.01

3.98

2.34

7.67

12.16

17.34

14.50

1.45

3.45

2.84

Docosahexaenoic NI 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; 6Anabaena 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 I2 100g−1

DU

LCSF

CFPP

CP

PP

(wt. %)

(wt. %)

(ºC)

(ºC)

(ºC)

APE

BAPE

υ

HHV

SFA

MUFA

PUFA

(g m )

(MJ Kg−1)

(%)

(%)

(%)

ρ 2 −1

(mm

)

−3

fat) Biodiesel Standard EN 14214

≥ 51

-

≤120

-

-

≤5/-20

-

-

-

-

3.5 -5.0

0.860.90

NA

-

-

-

Biodiesel Standard ASTM D6751-02

≥ 47

-

NA

-

-

NA

-

-

-

-

1.9 -6.0

0.860.90

NA

-

-

-

Biodiesel Standard IS 15607

≥ 51

-

NA

-

-

6/18

-

3/15

-

-

2.5 -6.0

0.860.90

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

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.

24