Exploration of Microalgae Biorefinery by Optimizing Sequential

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Exploration of microalgae biorefinery by optimizing sequential extraction of major metabolites from Scenedesmus obliquus Faiz Ahmad Ansari, Amritanshu Shriwastav, Sanjay Kumar Gupta, Ismail Rawat, and Faizal Bux Ind. Eng. Chem. Res., Just Accepted Manuscript • DOI: 10.1021/acs.iecr.6b04814 • Publication Date (Web): 10 Mar 2017 Downloaded from http://pubs.acs.org on March 14, 2017

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Industrial & Engineering Chemistry Research

1

Exploration of microalgae biorefinery by optimizing sequential

2

extraction of major metabolites from Scenedesmus obliquus

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Faiz Ahmad Ansari#, Amritanshu Shriwastav#$1, Sanjay Kumar Gupta$2, Ismail Rawat, Faizal

5

Bux*

6 7

Institute for Water and Wastewater Technology,

8

Durban University of Technology, P O Box1334, Durban, 4000, South Africa.

9 10

*Corresponding author

11

Prof.FaizalBux

12

Director

13

Institute for Water and Wastewater Technology

14

Durban University of Technology

15

P O Box1334, Durban, 4000, South Africa.

16

Tel:+27 31 373 2346

17

Fax: +27 31 373 2777

18

Email: [email protected]

19 20

#

21

$

22

1

23

Indian Institute of Technology Bombay, Mumbai - 400 076, INDIA

24

2

25

Indian Institute of Technology Delhi, New Delhi-110016, INDIA

F.A.A. and A.S. contributed equally to this paper. Present Address: Centre for Environmental Science and Engineering

Environmental Engineering, Department of Civil Engineering

26

ACS Paragon Plus Environment


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Abstract

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The effects of six different sequential extractions of proteins, lipids and carbohydrates on

29

their yields and subsequent biomass recoveries was investigated. The maximum yields of

30

lipids, proteins, and carbohydrates were 26.50 ± 1.32 %, 28.14 ± 1.97 % and 16.40 ± 0.43 %,

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respectively in primary extraction of biomass. Compared to the primary extractions, lipid

32

yields were significantly lowered by 20-22 % in secondary extractions. The maximum loss of

33

proteins in secondary (post lipid extraction) and tertiary extractions was 34.79 % and 56 %,

34

respectively. The most significant loss (38- 44.5%)in carbohydrates recordedafter tertiary

35

extractions.Among all the extraction sequences, the sequence of proteins- lipids-

36

carbohydrates extracted algae(PLCEA) showed optimum recovery of individual metabolite.

37

For this extraction sequence, the yields of proteins,lipids and carbohydrates were found to be

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28.14 %, 22 %, 10.17 %, respectively. It was also characterised by the highest residual

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biomass available for second (80 %) and third (61%) steps of extraction. Finally, the

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cumulative yields of these metabolites were converted into net value gains. The extraction

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sequence PLCEA could result in 66.5 % net value gain overcoming the cost of biomass

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generation.

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Key Words: Microalgae; Biorefinery; Proteins; Lipids; Carbohydrates; Scenedesmus

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obliquus.

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1. Introduction

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Microalgae biofuels are a sustainable and renewable alternative to fossil fuels.1,2 Though the

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microalgae biodiesel production remains the most sought after alternative for transport fuels,

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several challenges remain.1,3 Generation of biodiesel (fatty acid alkyl esters) from microalgae

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lipid is a multistep process which includes cultivation, biomass harvesting, lipid extraction,

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transesterification and product purification. Many of these processes demand high input cost

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which makes the process uneconomical which is the major challenge to commercial

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microalgae biodiesel production.4

55 56

However, shifting the focus from a single product strategy (biodiesel production only)

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to integrated biomass processing for the extraction of major metabolites alongside lipids may

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help to develop a profitable microalgae biofuel and biorefinery in the near future.5

59 60

Since the conceptualization of microalgae biorefinery, various researchers have

61

investigated its feasibility for extracting different metabolites from many algal species.5-

62

10

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biohydrogen from Nannochloropsis sp. Similarly, biorefineries for Chlorella vulgaris were

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analyzed for different compounds, such as lipids and methane (Amon 1949)12, various

65

pigments and bioelectricity.13Olguín (2012)14 also investigated the production of hydrogen,

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biodiesel, biogas, and other valuable products from Arthrospira. However, earlier studies

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reported that the production of high value compounds rather than additional forms of energy

68

(biogas or hydrogen) will be more economically sustainable for biofuel production from

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algae (Central Pollution Control Board (CPCB), New Delhi 2003)15.Therefore, major

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metabolites targeted for extraction from algae are proteins, lipids, and carbohydrates based on

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their relative abundance in algal cell and their market value.9, 10

Nobre et al., 201311 studied the extraction of lipids, carotenoids, fatty acids, and



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The microalgae biorefinery concept has continuously been explored in recent years,

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however there are still several challenges. High efficiency extraction of proteins, lipids and

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carbohydrates in a sequential extraction process remains one of the challenges yet to be

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addressed. As the metabolite extraction processes differ in nature, their sequential use affects

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the extraction potential of the successive metabolites differently.16 Efforts have been made to

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use alternative low to medium energy consuming processes for extraction such as pulse

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electric field, ionic liquids and surfactants.8,11However, these techniques are still under

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development and subject to further research before they could be optimized for commercial

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implementation for all target metabolites.16

81 82

Undeniably, reports on extraction of individual metabolites are available in

83

abundance. However, studies on identification and optimisation of an extraction sequence are

84

rare and scattered for individual metabolites. To overcome these inadequacies, we have

85

investigated the effect of various sequential extraction processes on the yields of proteins,

86

lipids and carbohydrates in detail. The metabolite yields, biomass recovery potentials and the

87

value extractions of individual extraction sequences were carried out and compared to each

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other to ascertain the highest value yielding sequence.

89 90

2. Material and methods

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2.1 Microalgae strain, growth conditions and biomass generation

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Scenedesmus obliquus (genbank accession number: FR751179.1)was isolated and purified

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from a pond located at Kwa-Zulu Natal province of Durban, South Africa. This alga was

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cultivated in domestic wastewater (Table 1) in a 25 L reactor with 16:8 h of light-dark cycle

95

using Gro-Lux lamps (80 µmol m-2 s-1) and a temperature of 25±2 °C. Biomass was harvested

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by gravitational settling and then centrifuged to obtain a thick algal slurry. Thickened slurry


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was then sun dried. Dried algal biomass was pulverised using mortar and pestle, and stored in

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desiccator for further use.

99 100

2.2 Sequential extraction of major metabolites

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Dried biomass of S. obliquus was utilized for sequential extraction of lipids, proteins, and

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carbohydrates. The extraction processes were chosen on the basis of their wide applicability

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and acceptability as the most effective method for each of these metabolites respectively. 3–5

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g of dried biomass was utilized for initial extraction of each selected metabolite. Residual

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biomass after each extraction was collected by vacuum filtration and then air dried. Table 2

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provides the details of various sequential extraction schemes investigated. The percentage

107

yields of the extracted metabolites along with the percentage recovery of processed biomass

108

in each individual step were quantified.

109 110

2.3 Lipid extraction from biomass

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Total lipids were extracted using 2:1 (v/v) mixture of chloroform and methanol as per the

112

method of Folch et al., 195717with microwave assisted cell disruption. 20 mL solvent was

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added to 1g dried biomass and digested in a microwave digester (Milestone S.R.L., Italy;

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1200 W of output power) at 1000 W and 100 °C for 10 minutes. Solvent containing extracted

115

lipids was centrifuged and vacuum filtered. Solvent was evaporated to dryness in an oven at

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60 °C. Total lipids were quantified gravimetrically and expressed as percentage dry cell

117

weight.

118 119

2.4 Protein extraction from biomass

120

2.4.1 Protein extraction



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Protein extraction was done according to Barbarino& Lourenco (2005) 18. In brief 50 mg of

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dried microalgal biomass was mixed with 4 mL of ultrapure water and incubated for 12 hours

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at 4 oC. Before an hour of incubation period the algal mixture was grinded for 5 mins by

124

mortar and pestle. To recover all grinded sample 4 mL of ultra-pure added to rinse the mortar

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pestle. After grinding the mixture was centrifuged (at 4 oC, 15000 g) for 20 min. Supernatant

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was collected and microalgal pellets were re-extracted with 1 mL 0.1N NaOH with 0.5 % β-

127

mercaptoethanol (v/v). The mixture of algal extract and NaOH solution kept at room

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temperature for 1 hour with occasional manual shaking. Then mixture was centrifuged for 20

129

min at 21 oC, 15000 g. The supernatant was pooled with first one.

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2.4.2 Protein analysis

131

In a tube 25% of trichloroacetic acid (TCA) was mixed with extract in 2.5:1 ratio. The

132

mixture were kept on ice bath for 30 min and then centrifuged for 20 min at 4 oC, 15000 g.

133

Supernatant were discarded and pellets were washed with cold (4 oC) 10% TCA and

134

centrifuged. Pellets formed after second centrifugation were suspended in 5% TCA at room

135

temperature in proportion of 5:1 (5% TCA: precipitate v/v) and centrifuged again at 21 oC

136

(15000 g) for 20 min. Supernatant were discarded and pellets were used for protein

137

estimation Lowry’s et al., 195119. Calibration curve was made using bovine albumin serum

138

(BSA) to calculate the percentage protein.20

139

2.5 Carbohydrate extraction from biomass

140

2.5.1 Carbohydrates extraction

141

Carbohydrates extraction was done by a slightly modified method of Karemore& Sen

142

(2016)21. In brief 50 mg of microalgal biomass was placed in 100 mL flask and mixed with

143

50 mL of 2% H2SO4 (v/v). The mixture was autoclaved at 121 oC temperature, 15 psi

144

pressure for 30 min. Autoclave mixture pH was maintained to 7 by using 1M NaOH or


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H2SO4. To separate to microalgal pellets mixture was centrifuged at 4 oC, 1509 g for 10 min.

146

Supernatant were used for carbohydrates analysis.

147

2.5.2 Carbohydrates analysis

148

Total carbohydrates were quantified using the phenol-sulfuric acid method.22In brief, 0.1 mL

149

of supernatant was diluted to 1 mL, and then mixed with 1 mL of phenol (5% w/v) and 5 ml

150

of 96 % H2SO4. After cooling to 25-30 °C, the absorbance of this solution was measured at

151

490 nm using a spectrophotometer (SpectroquantPharo 300, Merck). Total carbohydrates

152

were quantified referring to a calibration curve prepared using glucose as a standard.23

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2.6 Chemicals and reagents

154

Ultrapure water (Aqua MAX Ultra 370, Younglin, Korea) was used to prepare all solutions.

155

Most of the chemicals and reagents (analytical/HPLC grade) were purchased from Sigma

156

Aldrich, Germany

157 158

2.7 Statistical analysis

159

All the experimental analysis were carried out in triplicates. Statistical analysis was

160

conducted using one-way ANOVA at 95% confidence level. Post-hoc analysis of results was

161

performed with Tukey’s honestly significant differences (HSD) test.

162 163

3. Results and discussion

164

3.1 Effects of sequential extraction on extraction yields of metabolites

165

The yields obtained for lipids, proteins, and carbohydrates after primary extraction from

166

biomass were found to be 26.25 ± 0.64 %, 27.79 ± 1.41 % and 16.53 ± 0.20 %, respectively

167

(Fig. 1). This also correspond to the maximum content of these metabolites in the biomass for



168

the current study. Further, when these metabolites were extracted individually as a secondary

169

or tertiary extraction product, their yields were significantly lower.

170

35% Extraction Yield (% w/w)

Lipid a

30%

a

a

Protein

Carbohydrate

a

a

a

a

a

25%

b c

c

20%

a b a

15% b

10%

d

b

a

a

b

b

5%

C LP EA

C PL EA

PC LE A

PL C EA

LC PE A

LP C EA

ho l

e

Al

ga e

0%

171 172 173 174 175 176 177

W

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Fig. 1. Effects of extraction sequence on the extraction yields of primary metabolites from algae. Different letters on bars for each metabolite indicate significant difference among them (ANOVA, Tukey’s test, P< 0.05).

178

In case of lipids yield, when the lipids were extracted followed by the extraction of

179

proteins (PLCEA) and carbohydrates (CLPEA), the yields were significantly lower and noted

180

to be 23.00 ± 0.78 % and 20.00 ± 0.89 %, respectively. When compared to the maximum

181

lipid yields of 26.25 ± 0.64 % (obtained by primary extraction), lipid yields of secondary

182

extraction have decreased by ~12 % for PLCEA and ~24 % for CLPEA. Interestingly, no

183

considerable difference was observed between the lipid yields of these two secondary

184

extractions with CLPEA resulting in ~9 % lesser lipid yield than PLCEA extraction scheme.

185

Nonetheless, tertiary extraction further resulted in the significant reduction in lipid yields by

186

28-43 % lipid content (for PCLEA; 19.00 ± 0.92 %, and for CPLEA; 15.00 ± 0.95 %).


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187

188

In the case of proteins, the highest yield of 27.79 ± 1.41 % was obtained with primary

189

extraction. Secondary extraction of proteins following carbohydrate extraction (CPLEA,

190

24.98 ± 1.57 %) or lipid extraction (LPCEA, 23.21 ± 4.77 %) provided similar yields to

191

primary extraction. Tertiary protein extraction resulted in the yields of 12.29 ± 2.01 % for

192

LCPEA and 8.00 ± 0.83 % for CLPEA. No significant difference was observed in protein

193

yield for LCPEA and CLPEA scheme of extraction. It is also reported that the protein yield

194

decreases significantly when performed after lipid extraction. In a previously reported study,

195

protein yields of Navicula sp. when extracted after lipids were found to decrease from 19.4 %

196

to 13.3 % which corresponds to ~ 31.5% decreased yield.24

197

198 199

Table 1. Characteristics of wastewater (screened raw sewage) used for algal cultivation Parameter

Unit

Value

pH

6.93 ± 0.28 mg L

-1

240.00 ± 2.35

Total Dissolved Solids

mg L

-1

523.02 ± 45.75

Total Suspended Solids

mg L-1

Alkalinity

1034 ± 93.82

mg L

-1

136.60 ± 3.64

COD

mg L

-1

320.07 ± 3.78

N-NH3

mg L-1

BOD5

N-NO2

-

N-NO3

-

P-PO43-

52.23 ± 1.21

mg L

-1

0.00 ± 0.00

mg L

-1

0.40 ± 0.13

mg L-1

8.47 ± 0.23

200 201

202

In case of carbohydrates, the maximum yield (16.53 ± 0.20 %) was obtained by

203

primary extraction. Secondary extractions of carbohydrates followed by lipids (LCPEA, yield

204

of 13.53 ± 0.35 %) did not result in significant reduction in yield, while extraction after



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205

proteins (PCLEA, yield of 10.41 ± 3.69 %) significantly decreased it. The most significant

206

loss in carbohydrates was as a resultof tertiary extractions (LPCEA and PLCEA) and was

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noted to be 45- 53% lower when compared to the yields of primary extraction. Moreover,

208

carbohydrates yield as a result of tertiary extraction (PLCEA) and secondary extraction

209

following protein (PCLEA) were comparable.

210 211

Table 2. Extracted metabolites during different steps of sequential extraction from algae Legend

Metabolite Extracted First Step

Second Step

Third Step

LPCEA

Lipid

Protein

Carbohydrate

LCPEA

Lipid

Carbohydrate

Protein

PLCEA

Protein

Lipid

Carbohydrate

PCLEA

Protein

Carbohydrate

Lipid

CPLEA

Carbohydrate

Protein

Lipid

CLPEA

Carbohydrate

Lipid

Protein

212 213 214

215

216

The yields of proteins, lipids and carbohydrates obtained in this study were

217

comparable to the earlier reports. Our results are similar to the previous findings of Toyubet

218

al., 200825 who reported 28.30 ± 1.17 % protein content in S. obliquus. For the same

219

microalga, Ji et al., 201525and Ryckebos et al.,201227 have reported the lipid content in the

220

range of 21.9- 24.3 % and 29.7 %, respectively. Recently, Trzcinski et al., 201228 have also

221

reported the carbohydrate content of various microalgae strains in the range of 12-15 %.

222

Therefore, these results also confirm the appropriateness of the extraction methods used in

223

this study.

224


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225

In addition, the extraction sequence- PLCEA, where proteins were extracted first,

226

lipids in subsequent step and finally carbohydrates was found be the best for value extraction

227

(detailed in next sections) corroborates with the earlier studies. Gottel et al., 201329 have

228

observed with A. prothothecoides that the lipid droplets remained intact inside the cell even

229

after the extraction of soluble proteins. Therefore, the study opined that the proteins must be

230

extracted prior to lipids in an extraction sequence. In support to this principal, Gerde et al.,

231

201330 has observed that some proteins could be lost during lipid extraction from biomass

232

which cross-confirms the PLCEA schemes superiority. Interestingly, Munoz et al., 20157

233

have observed that carbohydrates were concentrated in a spent biomass (of B.braunii) after

234

the extraction of proteins and lipids. These recent findings from various researchers confirms

235

that the protein extraction must be prioritised in a biorefinery process.

236 237

3.2 Effects of sequential extraction on residual biomass

238

The extraction processes for all three metabolites viz proteins, carbohydrates and lipids

239

concomitantly causes some loss of algal biomass. We have also observed subsequent

240

reduction in residual biomass after each extraction step which can be attributed to the lower

241

extraction yields of next metabolite. These results are summarized in Fig. 2 as a residual

242

biomass yield (% of initial algal mass).

243


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Residual Biomass (% of initial wt.)


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100% a

a

80%

ab

ab

b

b

b

60%

cd

40%

d

ace ae

e

20% 0% LPCEA st

LCPEA

Before 1 extraction

PLCEA

PCLEA

CPLEA

nd

Before 2 extraction

CLPEA

Before 3rd extraction

244 245 246 247 248 249

Fig. 2. Effects of extraction sequence on the residual algal biomass in each step of metabolite extraction. Different letters on bars for each step indicate significant difference among them (ANOVA, Tukey’s test, P < 0.05). When proteins were extracted primarily from the whole cell algae biomass (PCLEA

250

and PLCEA extraction sequence), it resulted in the highest residual mass available for second

251

step (80.00 ± 3.00 % for PLCEA and 80.00 ± 6.00 % for PCLEA) with the concomitant

252

biomass loss of only 20 %. The next best primary extraction was found to be of lipids

253

wherein it had resulted in 30 % loss of initial biomass and 70 % residual biomass was

254

available for the next extraction. However, when carbohydrates were extracted first, the

255

residual biomass was reduced to 64.00 ± 7.00 % for CPLEA and 64.00 ± 2.00 % for CLPEA.

256

These residual biomass values were significantly lower (ANOVA, P < 0.05) when compared

257

with biomass left after initial protein extraction.

258


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a

a

a

Whole Algae a

b

b

LPCEA c

a

c

LCPEA b

b

a

PLCEA c

a

c

PCLEA d

b

a

Lipid Protein Carbohydrate

CPLEA e

c

a

CLPEA 0.0

0.2

0.4

0.6

0.8

-1

259 260 261 262 263 264 265

Extracted Primary Metabolites (kg kg of dried algae)

Fig. 3. Effects of extraction sequence on total extraction of primary metabolites. Different letters on bars for each metabolite indicate significant difference among them (ANOVA, Tukey’s test, P < 0.05).

266 267

After secondary extraction, the highest residual biomass was 60.80 ± 2.00 % for

268

PLCEA whilst the lowest residual biomass was 18.88 ± 5.00 % for CPLEA (Fig.3). Despite

269

the relatively low carbohydrate content (Fig. 1), extraction of carbohydrates resulted in the

270

highest loss of biomass when compared to the other two extraction processes irrespective of

271

the extraction sequence (Fig. 3). On the contrary, the loss of biomass during protein or lipid

272

extractions depended on the extraction sequence. Though the primary extraction of proteins

273

and lipids had comparable losses of biomass, secondary extraction of lipids resulted in

274

significantly lower loss of biomass when compared to secondary extraction of proteins.

275

PLCEA had a 24 % loss of primary extracted biomass (initial biomass for secondary

276

extraction) whereas CLPEA had lost 31 % of it. However, when proteins were extracted in

277

secondary extraction after lipids, the loss in biomass was noted to be 57.5 % and when it was

278

followed by carbohydrates extraction the biomass loss was maximum and was found to be



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279

70.5%. This suggested that proteins have a higher tendency than lipids to be affected by the

280

preceding extraction process. Proteins are likely to become progressively unbound from the

281

algal biomass after each extraction process and increased loss of biomass. Furthermore, these

282

insights help us in determining an extraction sequence where loss of biomass in each step

283

could be minimised so that the comprehensive extraction sequence is developed for

284

extraction of a reasonable yields of the target metabolites. Therefore, in order to minimise the

285

loss of the biomass, the extraction of the metabolites can follow a sequence as: proteins-

286

lipids-carbohydrate. This further requires an investigation into the total extraction of all three

287

metabolites while accounting for the effects of extraction sequence on both extraction yields

288

and biomass recovery.

289 290

3.3 Comparison of metabolites extracts based on market value using various extraction

291

sequences

292

A primary objective in achieving sustainable algal biorefinery is to obtain sufficient

293

extractions of major metabolites from harvested algal biomass. However, extraction

294

processes for individual metabolites and the sequence of extraction not only affects their

295

extraction yield from biomass but also lowers the residual biomass available for next

296

extraction. In the current study, the extracted metabolites were quantified for each extraction

297

sequence and summarized in Fig. 4. Their individual yields per kg of algae biomass (in

298

primary extraction) were found to be 0.27 ± 0.01 kg for lipids, 0.28 ± 0.02 kg for proteins,

299

0.16 ± 0.01 kg for carbohydrates. The secondary extraction of these metabolites has greatly

300

reduced their yields which was a function of the type of metabolite extracted and the % loss

301

of biomass in preceding extraction. When lipids were extracted after protein (PLCEA), it

302

resulted in yield of 0.18 ± 0.02 kg kg-1 algae, while after carbohydrates extraction, lipids

303

yield were significantly lowered to 0.13 ± 0.01 kg kg-1 algae.


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a

a

a

a

Whole Algae a

b

b

LPCEA c

a

c

LCPEA b

b

a

PLCEA c

a

c

PCLEA d

b

a


CPLEA e

a

c

CLPEA 0.0

0.2

0.4

0.6

0.8

1.0

Value of extracted primary metabolites (€ kg-1 of dried algae)

b Whole Algae

a

LPCEA

b

LCPEA

Current production cost of algae

c

PLCEA

d

PCLEA

e

CPLEA

f

CLPEA

Potential production cost of algae

c

0.0

0.2

0.4

0.6

0.8

4.0 -1

304 305 306 307 308 309 310 311 312

Total value of extracted primary metabolites (€ kg of dried algae)

Fig. 4. Effects of extraction sequence on value of extracted primary metabolites from 1kg of dried algal biomass after accounting for extraction cost, (a) value of individual primary metabolites extracted, (b) cumulative value extraction. Values are compared with the production cost of 1kg of dried algal biomass for net gain or loss obtained in that extraction sequence. Different letters on bars for each metabolite indicate significant difference among them (ANOVA, Tukey’s test, P < 0.05).

313 314

The facts of individual metabolites yield and the concomitant loss of processed

315

biomass marks the importance of choosing an appropriate extraction sequence to enhance the

316

value extraction of products. In this study, we have attempted to convert the yields of



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Page 16 of 46

317

individual metabolites to their market cost so as to investigate the overall value of the

318

products from an extraction sequence.The values of these extracted metabolites for their unit

319

mass and the production cost of dried algae while accounting for the costs involved in

320

extraction of individual metabolite were referred from the available literature.31 These values

321

are summarized in Table 3.

322 323 324

Table 3. Value of different primary metabolites extracted from algae after accounting for extraction cost, and the production cost of dried algal biomass (Wijffels et al., 2010) Value of extracted primary metabolite Metabolite

Lipid Protein

Application As feedstock for chemical industry As transport fuel For food For feed

Fraction of Value extracted (€ kg-1 metabolite) quantity 0.25

2.00

0.75 0.20 0.80

0.50 5.00 0.75

Carbohydrate 1.00 1.00 (as polysaccharides) -1 Current production cost of dried algae biomass = 4.02 € kg dried algae Potential production cost of dried algae biomass = 0.40 € kg-1 dried algae 325 326

Based on these, the cost of extracted metabolites from 1 kg of dried biomass (Fig. 4)

327

was calculated. Fig. 4a presents the values of individual metabolites for each extraction

328

sequence. Significant effects of extraction sequences were observed on the value extraction

329

for each metabolite. As observed, none of the extraction sequence could recover the current

330

production cost of algae (~4 € kg-1 of dried algae). However, with the potential of lowering

331

these production costs to ~0.4 € kg-1 of dried algae with certain measures.31 following

332

observations could be made.

333 334

Maximum values of these metabolites (0.23 ± 0.01 € for lipids, 0.45 ± 0.03 € for

335

proteins, and 0.16 ± 0.01 € for carbohydrates) were obtained when extracted directly from

336

whole cell algae in first step. However, these extracted values lowered in their second or


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337

tertiary extractions in accordance to their lower extracted amounts. Cumulative value

338

extractions for all the extraction schemes were also calculated (Fig. 4b) which were found to

339

be significantly affected by the sequence of extraction.

340 341

Total value of these metabolites in 1 kg of dried algae available for extraction was

342

0.84 ± 0.04 €. However, subsequent losses in sequential extraction significantly reduced the

343

total value extraction in all cases. Such extracted values ranged from 0.65 ± 0.04 € for

344

PLCEA to 0.33 ± 0.03 € for CLPEA. These values were compared with the production cost

345

for 1 kg of dried algae to estimate net gain or loss for a particular extraction sequence.

346 347

In this study, proteins were categorised as the most costly commodity followed by

348

lipids and then carbohydrates (Table 3).Carbohydrates, though the cheapest of all three

349

metabolites have affected the cumulative value of the products extraction with its processing

350

sequence. The highest loss (57.64 %) in market cost of these products was observed when

351

carbohydrates were either extracted as primary (CLPEA) or secondary extraction (LCPEA)

352

product. This significant loss in value can be attributed to the maximum loss of biomass

353

along with the extraction sequence where the highest value commodity (Proteins) was

354

extracted in the end with its yields being drastically reduced. Therefore, the CLPEAand

355

LCPEA schemes could not meet even the potential input cost of biomass generation (0.40 €

356

kg-1 biomass) and can account for an additional dispensation cost of 10 %. It corresponds to a

357

classic case of net energy input being more than the net energy which can be harnessed from

358

the biomass.

359 360

In secondary extraction of proteins, the cases LPCEA and CPLEA, the cumulative

361

cost of products was marginally higher than the potential input costwhereby it resulted in



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

362

12.5 % cost benefit to the process. It was mainly because the proteins being the highest value

363

commodity were extracted second in the sequence which resulted in minimum loss of

364

product.

365 366

On the other hand, the maximum gain in value (49- 66.5 %) was achieved with

367

primary extraction of proteins. The scheme PLCEA resulted in 66.5 % benefit overcoming

368

the cost of biomass generation whereas PCLEA could result in 49 % net value gain.

369

Comparatively higher value gain in PCLEA than PLCEA was due to the fact that the

370

sequence of extraction had followed their descending order of market costs. This sequence

371

was also characterised by minimum loss of biomass prior to lipids and carbohydrates

372

extraction.

373

Overall upon accounting for the cost of biomass generation that can potentially be

374

achieved, the cumulative cost of individual metabolites extracted from the whole algae

375

biomass could theoretically result in 112.5 % net value gain. However, in comparison to this,

376

the best extraction sequence of PLCEA (which has net value of 66.5 %) exhibits ~41 %

377

reduction in value. The value of 112.5 % corresponds to the maximum recovery of an

378

individual metabolite in one step process without the loss of other metabolites. This value is

379

unrealistic in practice as when only lipids are targeted, the value of proteins and

380

carbohydrates is lost and when only proteins are targeted the value of lipids and

381

carbohydrates is lost. Therefore, in order to successfully develop an integrated biofuel, food

382

and feed industry from microalgal biomass as a feedstock; bio-refining of microalgae must be

383

applied to extract value from all the intracellular (primary and secondary) metabolites in

384

addition to lowering the production cost of algae.

385 386

4. Conclusion


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387

This study demonstrated the importance of selecting an extraction sequence to achieve

388

sufficient yields of major metabolites from microalgal biomass. Significant effects of these

389

extraction sequences were observed on the final yields of individual metabolites as well as

390

biomass recovery. The cumulative value extractions were calculated (by subtracting the input

391

cost of biomass production) for each extraction sequence and compared. Based on these

392

comparisons, an optimal sequence for maximum value extraction was determined as protein

393

in first step, lipid in second step, and carbohydrate in tertiary. This PLCEA scheme of

394

extraction could provide net value gain of 66.5 % with the potential production cost of algae.

395 396

Acknowledgements

397

The authors hereby acknowledge the National Research Foundation and Durban University

398

of Technology for providing financial assistance. Authors also declare no conflict of interest.

399



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References

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(1) Griffiths, M. J.; Harrison, T. L. Lipid productivity as a key characteristic for choosing

402

algal species for biodiesel production. J. Appl. Phycol. 2009, 21, 493–507.

403

(2) Chisti, Y. Biodiesel from microalgae. Biotechnol. Adv. 2007, 25, 294–306.

404

(3) Knothe, G. A technical evaluation of biodiesel from vegetable oil vs. algae. Will algae-

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derived biodiesel perform? Green chem. 2011, 13, 3048–3065. (4) Salade, R. &Bauen, A. Micro-algae cultivation for biofuels: Cost, energy balance, environmental impacts and future prospects. Biomass & Bioenergy. 2013, 53, 29–38.

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(5) Bharatiraj, A.; Chakravarthy, M.; Kumar, R.R.; Yogendra, D.; Yuvaraj, D.;

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Jayamuthungai, J.; Kumar, P. R.; Palani, S. Aquatic biomass (algae) as a future feed

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stock for biorefineries: A revive on cultivation, processing and products. Renew.

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Sustain. Energy Rev. 2015, 47, 634–653.

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(6) Yen, H. W.; Hu, I. C.; Chen, C. Y.; Ho, S. H.; Lee, D. J.; Chang, J. S. Microalgae based

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biorefinery- From biofuels to natural products. Bioresour. Technol. 2013, 135, 166–

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174.

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(7) Munoz, R.; Navia, R.; Ciudad, G.; Tessini, C.; Jeison, D.; Mella, R.; Rabert, C.; Azócar,

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L. Preliminary biorefinery process proposal for protein and biofuels recovery from

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microalgae. Fuel, 2015, 150, 425–433.

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(8) Ursu, A.V.; Marcati, A.; Sayd, T.; Lhoutellier, S. V.; Djelveh, G.; Michaud, P. Extraction,

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fraction and functional properties of proteins from the microalgae chlorella vulgaris.

420

Bioresour. Technol. 2014, 157: 134–139.

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(9) Vanthoor-Koopmans, M.; Wijffels, R. H.; Barbosa, M. J.; Eppink, M. H. M. Biorefinery of microalgae for food and fuel. Bioresour. Technol. 2013, 135, 142–149. (10) Ansari, F. A.; Shriwastav, A.; Gupta, S. K.; Rawat, I.; Guldhe, A.; Bux, F. Lipid

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algae as a source for protein and reduced sugar: A step closer to the

425

biorefinery. Bioresour. Technol. 2015, 179, 559–564.

426

(11) Nobre, B.P.; Villalobos, F.; Barragán, B. E.; Oliveira, A. C.; Batista, A.P.; Marques, P.

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A. S. S.; Mendes, R. L.; Sovová, H.; Palavra, A. F.; Gouveia, L. A biorefinery from

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Nannochloropsis sp. microalga – Extraction of oils and pigments. Production of

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biohydrogen from the leftover biomass. Bioresour. Technol. 2013, 135,128–136.

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(12) Arnon, D.I. Copper enzymes in isolated chloroplasts. Polyphenoloxidase in Beta vulgaris. Plant physio. 1949, 24: 1.


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(13) Gouveia, L.; Neves, C.; Sebastião, D.; Nobre, B.P.; Matos, C. T. Effect of light on the

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production of bioelectricity and added-value microalgae biomass in a Photosynthetic

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Alga Microbial Fuel Cell. Bioresour. Technol. 2014a, 154, 171–177.

435

(14) Olguín, E. J. Dual Purpose Microalgae-Bacteria-Based Systems that Treat Wastewater

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and Produce Biodiesel and Chemical Products within a Biorefinery. Biotechnol Adv.

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2012, 30, 1031–1046.

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(15) CPCB. Environmental Management in Selected Industrial Sectors Status and Needs,

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Central Pollution Control Board. Ministry of Environment and Forest New Delhi,

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2003.

441 442 443 444

(16) Gouveia, L. From Tiny Microalgae to Huge Biorefineries. Oceanography. 2014b, 2, 120. (17) Folch, J.; Lees, M.; Stanley, G. H. S. A simple method for the isolation and purification of total lipides from animal tissues. J. Biol. Chem. 1957, 226, 497–509.

445

(18) Barbarino, E., &Lourenço, S. O. An evaluation of methods for extraction and

446

quantification of protein from marine macro- and microalgae. J. Appl. Phycol. 2005,

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17(5), 447–460.

448 449

(19) Lowry, O. H.; Rosebrough, N. J.; Farr, A. L.; Randall, R. J. Protein measurement with the folin phenol reagent. J. Biol. Chem. 1953, 193, 265–275.

450

(20) López, C. V. G., García, M. D. C. C., Fernández, F. G. A., Bustos, C. S., Chisti, Y.,

451

&Sevilla, J. M. F. (2010). Protein measurements of microalgal and cyanobacterial

452

biomass. Bioresour.Technol. 101, 19, 7587–91.

453

(21) Karemore, A., & Sen, R. Downstream processing of microalgal feedstock for lipid and

454

carbohydrate in a biorefinery concept: A holistic approach for biofuel applications.

455

RSC Adv. 2016, 6, 29486–29496.

456

(22) DuBois, M.; Gilles, K. A.; Hamilton, J. K.;Rebers, P. A.; Smith, F. Colorimetric Method

457

for Determination of Sugars and Related Substances. Anal. Chem. 1956, 28, 350–

458

356.

459

(23) Prajapati, S. K.; Malik, A.; Vijay, V. K. Comparative evaluation of biomass production

460

and bioenergy generation potential of Chlorella spp. through anaerobic digestion.

461

Appl. Energy. 2014, 14, 790–797.

462 463

(24) Patterson, D & Gatlin D. M. Evaluation of whole and lipid-extracted algae meals in the diets of juvenile red drum (Sciaenopsocellatus). Aquaculture. 2013, 416, 92–98.



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(25) Toyub, M. A.; Miah, M. I.; Habib, M. A. B.; Rahman, M. M. Growth performance and

465

nutritional value of Scenedesmus obliquus cultured in different concentrations of

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sweetmeat factory waste media. Bangladesh J. Anim. Sci. 2008, 37, 86–93.

467

(26) Ji, M. K.; Yun, H. S.; Park, S.; Lee, H.; Park, Y. T.; Bae, S.; Ham, J.; Choi, J. Effect of

468

food wastewater on biomass production by a green microalga Scendesmus obliquus

469

for bioenergy generation. Bioresour. Technol. 2015, 179, 624–62.

470

(27) Ryckebosh, E.;Muylaert, K.;Foubert, I. Optimization of an analytical procedure for

471

extraction of lipids from microalgae. J American Oil Chem’ Society. 2012, 89 (2),

472

189–198.

473

(28) Trzcinski, P.; Hernandez, E.; Webb, H. A novel process for enhancing oil producing in

474

algae biorefineries through bioconversion of soilid by-products. Bioresour. Technol.

475

2012, 11, 295–301.

476 477

(29) Gottel, M.; Eing, C.; Gusbeth, C.; Straessner, R.; Frey, W. Pulsed electric field assisted extraction of intracellular valuables from microalgae. Algal Res. 2013, 2, 401–408.

478

(30) Gerde, J. A.; Wang, T.; Yao, L.; Jung, S.; Johnson, L. A.; Lamsal, B. Optimizing protein

479

isolation from defatted and non- defatted Nannochloropsis microalga biomass. Algal

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Res. 2013, 2 (2), 145–153.

481 482

(31) Wijffels, R. H.; Barbosa, M. J.; Eppink, M. H. M. Microalgae for the production of bulk chemicals and biofuels. Biofuels, Bioprod.Biorefin. 2010, 4, 287–295.

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496 497 498 499

Exploration of microalgae biorefinery by optimizing sequential

500

extraction of major metabolites from Scenedesmus obliquus

501 502

Faiz Ahmad Ansari#, Amritanshu Shriwastav#$1, Sanjay Kumar Gupta$2, Ismail Rawat, Faizal

503

Bux*

504 505

Institute for Water and Wastewater Technology,

506

Durban University of Technology, P O Box1334, Durban, 4000, South Africa.

507 508

*Corresponding author

509

Prof.FaizalBux

510

Director

511

Institute for Water and Wastewater Technology

512

Durban University of Technology

513

P O Box1334, Durban, 4000, South Africa.

514

Tel:+27 31 373 2346

515

Fax: +27 31 373 2777

516

Email: [email protected]

517 518

#

519

$

520

1

521

Indian Institute of Technology Bombay, Mumbai - 400 076, INDIA

522

2

F.A.A. and A.S. contributed equally to this paper. Present Address: Centre for Environmental Science and Engineering

Environmental Engineering, Department of Civil Engineering



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523

Indian Institute of Technology Delhi, New Delhi-110016, INDIA

524


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525

Abstract

526

The effects of six different sequential extractions of proteins, lipids and carbohydrates on

527

their yields and subsequent biomass recoveries was investigated. The maximum yields of

528

lipids, proteins, and carbohydrates were 26.50 ± 1.32 %, 28.14 ± 1.97 % and 16.40 ± 0.43 %,

529

respectively in primary extraction of biomass. Compared to the primary extractions, lipid

530

yields were significantly lowered by 20-22 % in secondary extractions. The maximum loss of

531

proteins in secondary (post lipid extraction) and tertiary extractions was 34.79 % and 56 %,

532

respectively. The most significant loss (38- 44.5%)in carbohydrates recordedafter tertiary

533

extractions.Among all the extraction sequences, the sequence of proteins- lipids-

534

carbohydrates extracted algae(PLCEA) showed optimum recovery of individual metabolite.

535

For this extraction sequence, the yields of proteins,lipids and carbohydrates were found to be

536

28.14 %, 22 %, 10.17 %, respectively. It was also characterised by the highest residual

537

biomass available for second (80 %) and third (61%) steps of extraction. Finally, the

538

cumulative yields of these metabolites were converted into net value gains. The extraction

539

sequence PLCEA could result in 66.5 % net value gain overcoming the cost of biomass

540

generation.

541 542

Key Words: Microalgae; Biorefinery; Proteins; Lipids; Carbohydrates; Scenedesmus

543

obliquus.

544



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

545

1. Introduction

546

Microalgae biofuels are a sustainable and renewable alternative to fossil fuels.1,2 Though the

547

microalgae biodiesel production remains the most sought after alternative for transport fuels,

548

several challenges remain.1,3 Generation of biodiesel (fatty acid alkyl esters) from microalgae

549

lipid is a multistep process which includes cultivation, biomass harvesting, lipid extraction,

550

transesterification and product purification. Many of these processes demand high input cost

551

which makes the process uneconomical which is the major challenge to commercial

552

microalgae biodiesel production.4

553 554

However, shifting the focus from a single product strategy (biodiesel production only)

555

to integrated biomass processing for the extraction of major metabolites alongside lipids may

556

help to develop a profitable microalgae biofuel and biorefinery in the near future.5

557 558

Since the conceptualization of microalgae biorefinery, various researchers have

559

investigated its feasibility for extracting different metabolites from many algal species.5-

560

10

561

biohydrogen from Nannochloropsis sp. Similarly, biorefineries for Chlorella vulgaris were

562

analyzed for different compounds, such as lipids and methane (Amon 1949)12, various

563

pigments and bioelectricity.13Olguín (2012)14 also investigated the production of hydrogen,

564

biodiesel, biogas, and other valuable products from Arthrospira. However, earlier studies

565

reported that the production of high value compounds rather than additional forms of energy

566

(biogas or hydrogen) will be more economically sustainable for biofuel production from

567

algae (Central Pollution Control Board (CPCB), New Delhi 2003)15.Therefore, major

568

metabolites targeted for extraction from algae are proteins, lipids, and carbohydrates based on

569

their relative abundance in algal cell and their market value.9, 10

Nobre et al., 201311 studied the extraction of lipids, carotenoids, fatty acids, and


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570

The microalgae biorefinery concept has continuously been explored in recent years,

571

however there are still several challenges. High efficiency extraction of proteins, lipids and

572

carbohydrates in a sequential extraction process remains one of the challenges yet to be

573

addressed. As the metabolite extraction processes differ in nature, their sequential use affects

574

the extraction potential of the successive metabolites differently.16 Efforts have been made to

575

use alternative low to medium energy consuming processes for extraction such as pulse

576

electric field, ionic liquids and surfactants.8,11However, these techniques are still under

577

development and subject to further research before they could be optimized for commercial

578

implementation for all target metabolites.16

579 580

Undeniably, reports on extraction of individual metabolites are available in

581

abundance. However, studies on identification and optimisation of an extraction sequence are

582

rare and scattered for individual metabolites. To overcome these inadequacies, we have

583

investigated the effect of various sequential extraction processes on the yields of proteins,

584

lipids and carbohydrates in detail. The metabolite yields, biomass recovery potentials and the

585

value extractions of individual extraction sequences were carried out and compared to each

586

other to ascertain the highest value yielding sequence.

587 588

2. Material and methods

589

2.1 Microalgae strain, growth conditions and biomass generation

590

Scenedesmus obliquus (genbank accession number: FR751179.1)was isolated and purified

591

from a pond located at Kwa-Zulu Natal province of Durban, South Africa. This alga was

592

cultivated in domestic wastewater (Table 1) in a 25 L reactor with 16:8 h of light-dark cycle

593

using Gro-Lux lamps (80 µmol m-2 s-1) and a temperature of 25±2 °C. Biomass was harvested

594

by gravitational settling and then centrifuged to obtain a thick algal slurry. Thickened slurry



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

595

was then sun dried. Dried algal biomass was pulverised using mortar and pestle, and stored in

596

desiccator for further use.

597 598

2.2 Sequential extraction of major metabolites

599

Dried biomass of S. obliquus was utilized for sequential extraction of lipids, proteins, and

600

carbohydrates. The extraction processes were chosen on the basis of their wide applicability

601

and acceptability as the most effective method for each of these metabolites respectively. 3–5

602

g of dried biomass was utilized for initial extraction of each selected metabolite. Residual

603

biomass after each extraction was collected by vacuum filtration and then air dried. Table 2

604

provides the details of various sequential extraction schemes investigated. The percentage

605

yields of the extracted metabolites along with the percentage recovery of processed biomass

606

in each individual step were quantified.

607 608

2.3 Lipid extraction from biomass

609

Total lipids were extracted using 2:1 (v/v) mixture of chloroform and methanol as per the

610

method of Folch et al., 195717with microwave assisted cell disruption. 20 mL solvent was

611

added to 1g dried biomass and digested in a microwave digester (Milestone S.R.L., Italy;

612

1200 W of output power) at 1000 W and 100 °C for 10 minutes. Solvent containing extracted

613

lipids was centrifuged and vacuum filtered. Solvent was evaporated to dryness in an oven at

614

60 °C. Total lipids were quantified gravimetrically and expressed as percentage dry cell

615

weight.

616 617

2.4 Protein extraction from biomass

618

2.4.1 Protein extraction


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619

Protein extraction was done according to Barbarino& Lourenco (2005) 18. In brief 50 mg of

620

dried microalgal biomass was mixed with 4 mL of ultrapure water and incubated for 12 hours

621

at 4 oC. Before an hour of incubation period the algal mixture was grinded for 5 mins by

622

mortar and pestle. To recover all grinded sample 4 mL of ultra-pure added to rinse the mortar

623

pestle. After grinding the mixture was centrifuged (at 4 oC, 15000 g) for 20 min. Supernatant

624

was collected and microalgal pellets were re-extracted with 1 mL 0.1N NaOH with 0.5 % β-

625

mercaptoethanol (v/v). The mixture of algal extract and NaOH solution kept at room

626

temperature for 1 hour with occasional manual shaking. Then mixture was centrifuged for 20

627

min at 21 oC, 15000 g. The supernatant was pooled with first one.

628

2.4.2 Protein analysis

629

In a tube 25% of trichloroacetic acid (TCA) was mixed with extract in 2.5:1 ratio. The

630

mixture were kept on ice bath for 30 min and then centrifuged for 20 min at 4 oC, 15000 g.

631

Supernatant were discarded and pellets were washed with cold (4 oC) 10% TCA and

632

centrifuged. Pellets formed after second centrifugation were suspended in 5% TCA at room

633

temperature in proportion of 5:1 (5% TCA: precipitate v/v) and centrifuged again at 21 oC

634

(15000 g) for 20 min. Supernatant were discarded and pellets were used for protein

635

estimation Lowry’s et al., 195119. Calibration curve was made using bovine albumin serum

636

(BSA) to calculate the percentage protein.20

637

2.5 Carbohydrate extraction from biomass

638

2.5.1 Carbohydrates extraction

639

Carbohydrates extraction was done by a slightly modified method of Karemore& Sen

640

(2016)21. In brief 50 mg of microalgal biomass was placed in 100 mL flask and mixed with

641

50 mL of 2% H2SO4 (v/v). The mixture was autoclaved at 121 oC temperature, 15 psi

642

pressure for 30 min. Autoclave mixture pH was maintained to 7 by using 1M NaOH or



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643

H2SO4. To separate to microalgal pellets mixture was centrifuged at 4 oC, 1509 g for 10 min.

644

Supernatant were used for carbohydrates analysis.

645

2.5.2 Carbohydrates analysis

646

Total carbohydrates were quantified using the phenol-sulfuric acid method.22In brief, 0.1 mL

647

of supernatant was diluted to 1 mL, and then mixed with 1 mL of phenol (5% w/v) and 5 ml

648

of 96 % H2SO4. After cooling to 25-30 °C, the absorbance of this solution was measured at

649

490 nm using a spectrophotometer (SpectroquantPharo 300, Merck). Total carbohydrates

650

were quantified referring to a calibration curve prepared using glucose as a standard.23

651

2.6 Chemicals and reagents

652

Ultrapure water (Aqua MAX Ultra 370, Younglin, Korea) was used to prepare all solutions.

653

Most of the chemicals and reagents (analytical/HPLC grade) were purchased from Sigma

654

Aldrich, Germany

655 656

2.7 Statistical analysis

657

All the experimental analysis were carried out in triplicates. Statistical analysis was

658

conducted using one-way ANOVA at 95% confidence level. Post-hoc analysis of results was

659

performed with Tukey’s honestly significant differences (HSD) test.

660 661

3. Results and discussion

662

3.1 Effects of sequential extraction on extraction yields of metabolites

663

The yields obtained for lipids, proteins, and carbohydrates after primary extraction from

664

biomass were found to be 26.25 ± 0.64 %, 27.79 ± 1.41 % and 16.53 ± 0.20 %, respectively

665

(Fig. 1). This also correspond to the maximum content of these metabolites in the biomass for


Page 30 of 46

Page 31 of 46

666

the current study. Further, when these metabolites were extracted individually as a secondary

667

or tertiary extraction product, their yields were significantly lower.

668

35% Extraction Yield (% w/w)

Lipid a

30%

a

a

Protein

Carbohydrate

a

a

a

a

a

25%

b c

c

20%

a b a

15% b

10%

d

b

a

a

b

b

5%

C LP EA

C PL EA

PC LE A

PL C EA

LC PE A

LP C EA

ho l

e

Al

ga e

0%

669 670 671 672 673 674 675

W

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Fig. 1. Effects of extraction sequence on the extraction yields of primary metabolites from algae. Different letters on bars for each metabolite indicate significant difference among them (ANOVA, Tukey’s test, P< 0.05).

676

In case of lipids yield, when the lipids were extracted followed by the extraction of

677

proteins (PLCEA) and carbohydrates (CLPEA), the yields were significantly lower and noted

678

to be 23.00 ± 0.78 % and 20.00 ± 0.89 %, respectively. When compared to the maximum

679

lipid yields of 26.25 ± 0.64 % (obtained by primary extraction), lipid yields of secondary

680

extraction have decreased by ~12 % for PLCEA and ~24 % for CLPEA. Interestingly, no

681

considerable difference was observed between the lipid yields of these two secondary

682

extractions with CLPEA resulting in ~9 % lesser lipid yield than PLCEA extraction scheme.

683

Nonetheless, tertiary extraction further resulted in the significant reduction in lipid yields by

684

28-43 % lipid content (for PCLEA; 19.00 ± 0.92 %, and for CPLEA; 15.00 ± 0.95 %).



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

685

686

In the case of proteins, the highest yield of 27.79 ± 1.41 % was obtained with primary

687

extraction. Secondary extraction of proteins following carbohydrate extraction (CPLEA,

688

24.98 ± 1.57 %) or lipid extraction (LPCEA, 23.21 ± 4.77 %) provided similar yields to

689

primary extraction. Tertiary protein extraction resulted in the yields of 12.29 ± 2.01 % for

690

LCPEA and 8.00 ± 0.83 % for CLPEA. No significant difference was observed in protein

691

yield for LCPEA and CLPEA scheme of extraction. It is also reported that the protein yield

692

decreases significantly when performed after lipid extraction. In a previously reported study,

693

protein yields of Navicula sp. when extracted after lipids were found to decrease from 19.4 %

694

to 13.3 % which corresponds to ~ 31.5% decreased yield.24

695

696 697

Table 1. Characteristics of wastewater (screened raw sewage) used for algal cultivation Parameter

Unit

Value

pH

6.93 ± 0.28 mg L

-1

240.00 ± 2.35

Total Dissolved Solids

mg L

-1

523.02 ± 45.75

Total Suspended Solids

mg L-1

Alkalinity

1034 ± 93.82

mg L

-1

136.60 ± 3.64

COD

mg L

-1

320.07 ± 3.78

N-NH3

mg L-1

BOD5

N-NO2

-

N-NO3

-

P-PO43-

52.23 ± 1.21

mg L

-1

0.00 ± 0.00

mg L

-1

0.40 ± 0.13

mg L-1

8.47 ± 0.23

698 699

700

In case of carbohydrates, the maximum yield (16.53 ± 0.20 %) was obtained by

701

primary extraction. Secondary extractions of carbohydrates followed by lipids (LCPEA, yield

702

of 13.53 ± 0.35 %) did not result in significant reduction in yield, while extraction after


Page 32 of 46

Page 33 of 46

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


703

proteins (PCLEA, yield of 10.41 ± 3.69 %) significantly decreased it. The most significant

704

loss in carbohydrates was as a resultof tertiary extractions (LPCEA and PLCEA) and was

705

noted to be 45- 53% lower when compared to the yields of primary extraction. Moreover,

706

carbohydrates yield as a result of tertiary extraction (PLCEA) and secondary extraction

707

following protein (PCLEA) were comparable.

708 709

Table 2. Extracted metabolites during different steps of sequential extraction from algae Legend

Metabolite Extracted First Step

Second Step

Third Step

LPCEA

Lipid

Protein

Carbohydrate

LCPEA

Lipid

Carbohydrate

Protein

PLCEA

Protein

Lipid

Carbohydrate

PCLEA

Protein

Carbohydrate

Lipid

CPLEA

Carbohydrate

Protein

Lipid

CLPEA

Carbohydrate

Lipid

Protein

710 711 712

713

714

The yields of proteins, lipids and carbohydrates obtained in this study were

715

comparable to the earlier reports. Our results are similar to the previous findings of Toyubet

716

al., 200825 who reported 28.30 ± 1.17 % protein content in S. obliquus. For the same

717

microalga, Ji et al., 201525and Ryckebos et al.,201227 have reported the lipid content in the

718

range of 21.9- 24.3 % and 29.7 %, respectively. Recently, Trzcinski et al., 201228 have also

719

reported the carbohydrate content of various microalgae strains in the range of 12-15 %.

720

Therefore, these results also confirm the appropriateness of the extraction methods used in

721

this study.

722



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

723

In addition, the extraction sequence- PLCEA, where proteins were extracted first,

724

lipids in subsequent step and finally carbohydrates was found be the best for value extraction

725

(detailed in next sections) corroborates with the earlier studies. Gottel et al., 201329 have

726

observed with A. prothothecoides that the lipid droplets remained intact inside the cell even

727

after the extraction of soluble proteins. Therefore, the study opined that the proteins must be

728

extracted prior to lipids in an extraction sequence. In support to this principal, Gerde et al.,

729

201330 has observed that some proteins could be lost during lipid extraction from biomass

730

which cross-confirms the PLCEA schemes superiority. Interestingly, Munoz et al., 20157

731

have observed that carbohydrates were concentrated in a spent biomass (of B.braunii) after

732

the extraction of proteins and lipids. These recent findings from various researchers confirms

733

that the protein extraction must be prioritised in a biorefinery process.

734 735

3.2 Effects of sequential extraction on residual biomass

736

The extraction processes for all three metabolites viz proteins, carbohydrates and lipids

737

concomitantly causes some loss of algal biomass. We have also observed subsequent

738

reduction in residual biomass after each extraction step which can be attributed to the lower

739

extraction yields of next metabolite. These results are summarized in Fig. 2 as a residual

740

biomass yield (% of initial algal mass).

741


Page 34 of 46

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Residual Biomass (% of initial wt.)

Page 35 of 46

100% a

a

80%

ab

ab

b

b

b

60%

cd

40%

d

ace ae

e

20% 0% LPCEA st

LCPEA

Before 1 extraction

PLCEA

PCLEA

CPLEA

nd

Before 2 extraction

CLPEA

Before 3rd extraction

742 743 744 745 746 747

Fig. 2. Effects of extraction sequence on the residual algal biomass in each step of metabolite extraction. Different letters on bars for each step indicate significant difference among them (ANOVA, Tukey’s test, P < 0.05). When proteins were extracted primarily from the whole cell algae biomass (PCLEA

748

and PLCEA extraction sequence), it resulted in the highest residual mass available for second

749

step (80.00 ± 3.00 % for PLCEA and 80.00 ± 6.00 % for PCLEA) with the concomitant

750

biomass loss of only 20 %. The next best primary extraction was found to be of lipids

751

wherein it had resulted in 30 % loss of initial biomass and 70 % residual biomass was

752

available for the next extraction. However, when carbohydrates were extracted first, the

753

residual biomass was reduced to 64.00 ± 7.00 % for CPLEA and 64.00 ± 2.00 % for CLPEA.

754

These residual biomass values were significantly lower (ANOVA, P < 0.05) when compared

755

with biomass left after initial protein extraction.

756



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

a

a

Page 36 of 46

a

Whole Algae a

b

b

LPCEA c

a

c

LCPEA b

b

a

PLCEA c

a

c

PCLEA d

b

a


CPLEA e

c

a

CLPEA 0.0

0.2

0.4

0.6

0.8

-1

757 758 759 760 761 762 763

Extracted Primary Metabolites (kg kg of dried algae)

Fig. 3. Effects of extraction sequence on total extraction of primary metabolites. Different letters on bars for each metabolite indicate significant difference among them (ANOVA, Tukey’s test, P < 0.05).

764 765

After secondary extraction, the highest residual biomass was 60.80 ± 2.00 % for

766

PLCEA whilst the lowest residual biomass was 18.88 ± 5.00 % for CPLEA (Fig.3). Despite

767

the relatively low carbohydrate content (Fig. 1), extraction of carbohydrates resulted in the

768

highest loss of biomass when compared to the other two extraction processes irrespective of

769

the extraction sequence (Fig. 3). On the contrary, the loss of biomass during protein or lipid

770

extractions depended on the extraction sequence. Though the primary extraction of proteins

771

and lipids had comparable losses of biomass, secondary extraction of lipids resulted in

772

significantly lower loss of biomass when compared to secondary extraction of proteins.

773

PLCEA had a 24 % loss of primary extracted biomass (initial biomass for secondary

774

extraction) whereas CLPEA had lost 31 % of it. However, when proteins were extracted in

775

secondary extraction after lipids, the loss in biomass was noted to be 57.5 % and when it was

776

followed by carbohydrates extraction the biomass loss was maximum and was found to be


Page 37 of 46

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


777

70.5%. This suggested that proteins have a higher tendency than lipids to be affected by the

778

preceding extraction process. Proteins are likely to become progressively unbound from the

779

algal biomass after each extraction process and increased loss of biomass. Furthermore, these

780

insights help us in determining an extraction sequence where loss of biomass in each step

781

could be minimised so that the comprehensive extraction sequence is developed for

782

extraction of a reasonable yields of the target metabolites. Therefore, in order to minimise the

783

loss of the biomass, the extraction of the metabolites can follow a sequence as: proteins-

784

lipids-carbohydrate. This further requires an investigation into the total extraction of all three

785

metabolites while accounting for the effects of extraction sequence on both extraction yields

786

and biomass recovery.

787 788

3.3 Comparison of metabolites extracts based on market value using various extraction

789

sequences

790

A primary objective in achieving sustainable algal biorefinery is to obtain sufficient

791

extractions of major metabolites from harvested algal biomass. However, extraction

792

processes for individual metabolites and the sequence of extraction not only affects their

793

extraction yield from biomass but also lowers the residual biomass available for next

794

extraction. In the current study, the extracted metabolites were quantified for each extraction

795

sequence and summarized in Fig. 4. Their individual yields per kg of algae biomass (in

796

primary extraction) were found to be 0.27 ± 0.01 kg for lipids, 0.28 ± 0.02 kg for proteins,

797

0.16 ± 0.01 kg for carbohydrates. The secondary extraction of these metabolites has greatly

798

reduced their yields which was a function of the type of metabolite extracted and the % loss

799

of biomass in preceding extraction. When lipids were extracted after protein (PLCEA), it

800

resulted in yield of 0.18 ± 0.02 kg kg-1 algae, while after carbohydrates extraction, lipids

801

yield were significantly lowered to 0.13 ± 0.01 kg kg-1 algae.



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

a

a

Page 38 of 46

a

a

Whole Algae a

b

b

LPCEA c

a

c

LCPEA b

b

a

PLCEA c

a

c

PCLEA d

b

a


CPLEA e

a

c

CLPEA 0.0

0.2

0.4

0.6

0.8

1.0

Value of extracted primary metabolites (€ kg-1 of dried algae)

b Whole Algae

a

LPCEA

b

LCPEA

Current production cost of algae

c

PLCEA

d

PCLEA

e

CPLEA

f

CLPEA

Potential production cost of algae

c

0.0

0.2

0.4

0.6

0.8

4.0 -1

802 803 804 805 806 807 808 809 810

Total value of extracted primary metabolites (€ kg of dried algae)

Fig. 4. Effects of extraction sequence on value of extracted primary metabolites from 1kg of dried algal biomass after accounting for extraction cost, (a) value of individual primary metabolites extracted, (b) cumulative value extraction. Values are compared with the production cost of 1kg of dried algal biomass for net gain or loss obtained in that extraction sequence. Different letters on bars for each metabolite indicate significant difference among them (ANOVA, Tukey’s test, P < 0.05).

811 812

The facts of individual metabolites yield and the concomitant loss of processed

813

biomass marks the importance of choosing an appropriate extraction sequence to enhance the

814

value extraction of products. In this study, we have attempted to convert the yields of


Page 39 of 46

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


815

individual metabolites to their market cost so as to investigate the overall value of the

816

products from an extraction sequence.The values of these extracted metabolites for their unit

817

mass and the production cost of dried algae while accounting for the costs involved in

818

extraction of individual metabolite were referred from the available literature.31 These values

819

are summarized in Table 3.

820 821 822

Table 3. Value of different primary metabolites extracted from algae after accounting for extraction cost, and the production cost of dried algal biomass (Wijffels et al., 2010) Value of extracted primary metabolite Metabolite

Lipid Protein

Application As feedstock for chemical industry As transport fuel For food For feed

Fraction of Value extracted (€ kg-1 metabolite) quantity 0.25

2.00

0.75 0.20 0.80

0.50 5.00 0.75

Carbohydrate 1.00 1.00 (as polysaccharides) -1 Current production cost of dried algae biomass = 4.02 € kg dried algae Potential production cost of dried algae biomass = 0.40 € kg-1 dried algae 823 824

Based on these, the cost of extracted metabolites from 1 kg of dried biomass (Fig. 4)

825

was calculated. Fig. 4a presents the values of individual metabolites for each extraction

826

sequence. Significant effects of extraction sequences were observed on the value extraction

827

for each metabolite. As observed, none of the extraction sequence could recover the current

828

production cost of algae (~4 € kg-1 of dried algae). However, with the potential of lowering

829

these production costs to ~0.4 € kg-1 of dried algae with certain measures.31 following

830

observations could be made.

831 832

Maximum values of these metabolites (0.23 ± 0.01 € for lipids, 0.45 ± 0.03 € for

833

proteins, and 0.16 ± 0.01 € for carbohydrates) were obtained when extracted directly from

834

whole cell algae in first step. However, these extracted values lowered in their second or



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

835

tertiary extractions in accordance to their lower extracted amounts. Cumulative value

836

extractions for all the extraction schemes were also calculated (Fig. 4b) which were found to

837

be significantly affected by the sequence of extraction.

838 839

Total value of these metabolites in 1 kg of dried algae available for extraction was

840

0.84 ± 0.04 €. However, subsequent losses in sequential extraction significantly reduced the

841

total value extraction in all cases. Such extracted values ranged from 0.65 ± 0.04 € for

842

PLCEA to 0.33 ± 0.03 € for CLPEA. These values were compared with the production cost

843

for 1 kg of dried algae to estimate net gain or loss for a particular extraction sequence.

844 845

In this study, proteins were categorised as the most costly commodity followed by

846

lipids and then carbohydrates (Table 3).Carbohydrates, though the cheapest of all three

847

metabolites have affected the cumulative value of the products extraction with its processing

848

sequence. The highest loss (57.64 %) in market cost of these products was observed when

849

carbohydrates were either extracted as primary (CLPEA) or secondary extraction (LCPEA)

850

product. This significant loss in value can be attributed to the maximum loss of biomass

851

along with the extraction sequence where the highest value commodity (Proteins) was

852

extracted in the end with its yields being drastically reduced. Therefore, the CLPEAand

853

LCPEA schemes could not meet even the potential input cost of biomass generation (0.40 €

854

kg-1 biomass) and can account for an additional dispensation cost of 10 %. It corresponds to a

855

classic case of net energy input being more than the net energy which can be harnessed from

856

the biomass.

857 858

In secondary extraction of proteins, the cases LPCEA and CPLEA, the cumulative

859

cost of products was marginally higher than the potential input costwhereby it resulted in


Page 40 of 46

Page 41 of 46

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


860

12.5 % cost benefit to the process. It was mainly because the proteins being the highest value

861

commodity were extracted second in the sequence which resulted in minimum loss of

862

product.

863 864

On the other hand, the maximum gain in value (49- 66.5 %) was achieved with

865

primary extraction of proteins. The scheme PLCEA resulted in 66.5 % benefit overcoming

866

the cost of biomass generation whereas PCLEA could result in 49 % net value gain.

867

Comparatively higher value gain in PCLEA than PLCEA was due to the fact that the

868

sequence of extraction had followed their descending order of market costs. This sequence

869

was also characterised by minimum loss of biomass prior to lipids and carbohydrates

870

extraction.

871

Overall upon accounting for the cost of biomass generation that can potentially be

872

achieved, the cumulative cost of individual metabolites extracted from the whole algae

873

biomass could theoretically result in 112.5 % net value gain. However, in comparison to this,

874

the best extraction sequence of PLCEA (which has net value of 66.5 %) exhibits ~41 %

875

reduction in value. The value of 112.5 % corresponds to the maximum recovery of an

876

individual metabolite in one step process without the loss of other metabolites. This value is

877

unrealistic in practice as when only lipids are targeted, the value of proteins and

878

carbohydrates is lost and when only proteins are targeted the value of lipids and

879

carbohydrates is lost. Therefore, in order to successfully develop an integrated biofuel, food

880

and feed industry from microalgal biomass as a feedstock; bio-refining of microalgae must be

881

applied to extract value from all the intracellular (primary and secondary) metabolites in

882

addition to lowering the production cost of algae.

883 884

4. Conclusion



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

885

This study demonstrated the importance of selecting an extraction sequence to achieve

886

sufficient yields of major metabolites from microalgal biomass. Significant effects of these

887

extraction sequences were observed on the final yields of individual metabolites as well as

888

biomass recovery. The cumulative value extractions were calculated (by subtracting the input

889

cost of biomass production) for each extraction sequence and compared. Based on these

890

comparisons, an optimal sequence for maximum value extraction was determined as protein

891

in first step, lipid in second step, and carbohydrate in tertiary. This PLCEA scheme of

892

extraction could provide net value gain of 66.5 % with the potential production cost of algae.

893 894

Acknowledgements

895

The authors hereby acknowledge the National Research Foundation and Durban University

896

of Technology for providing financial assistance. Authors also declare no conflict of interest.

897


Page 42 of 46

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898

References

899

(1) Griffiths, M. J.; Harrison, T. L. Lipid productivity as a key characteristic for choosing

900

algal species for biodiesel production. J. Appl. Phycol. 2009, 21, 493–507.

901

(2) Chisti, Y. Biodiesel from microalgae. Biotechnol. Adv. 2007, 25, 294–306.

902

(3) Knothe, G. A technical evaluation of biodiesel from vegetable oil vs. algae. Will algae-

903 904 905

derived biodiesel perform? Green chem. 2011, 13, 3048–3065. (4) Salade, R. &Bauen, A. Micro-algae cultivation for biofuels: Cost, energy balance, environmental impacts and future prospects. Biomass & Bioenergy. 2013, 53, 29–38.

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(5) Bharatiraj, A.; Chakravarthy, M.; Kumar, R.R.; Yogendra, D.; Yuvaraj, D.;

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Jayamuthungai, J.; Kumar, P. R.; Palani, S. Aquatic biomass (algae) as a future feed

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stock for biorefineries: A revive on cultivation, processing and products. Renew.

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Sustain. Energy Rev. 2015, 47, 634–653.

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(6) Yen, H. W.; Hu, I. C.; Chen, C. Y.; Ho, S. H.; Lee, D. J.; Chang, J. S. Microalgae based

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biorefinery- From biofuels to natural products. Bioresour. Technol. 2013, 135, 166–

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174.

913

(7) Munoz, R.; Navia, R.; Ciudad, G.; Tessini, C.; Jeison, D.; Mella, R.; Rabert, C.; Azócar,

914

L. Preliminary biorefinery process proposal for protein and biofuels recovery from

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microalgae. Fuel, 2015, 150, 425–433.

916

(8) Ursu, A.V.; Marcati, A.; Sayd, T.; Lhoutellier, S. V.; Djelveh, G.; Michaud, P. Extraction,

917

fraction and functional properties of proteins from the microalgae chlorella vulgaris.

918

Bioresour. Technol. 2014, 157: 134–139.

919 920 921

(9) Vanthoor-Koopmans, M.; Wijffels, R. H.; Barbosa, M. J.; Eppink, M. H. M. Biorefinery of microalgae for food and fuel. Bioresour. Technol. 2013, 135, 142–149. (10) Ansari, F. A.; Shriwastav, A.; Gupta, S. K.; Rawat, I.; Guldhe, A.; Bux, F. Lipid

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algae as a source for protein and reduced sugar: A step closer to the

923

biorefinery. Bioresour. Technol. 2015, 179, 559–564.

924

(11) Nobre, B.P.; Villalobos, F.; Barragán, B. E.; Oliveira, A. C.; Batista, A.P.; Marques, P.

925

A. S. S.; Mendes, R. L.; Sovová, H.; Palavra, A. F.; Gouveia, L. A biorefinery from

926

Nannochloropsis sp. microalga – Extraction of oils and pigments. Production of

927

biohydrogen from the leftover biomass. Bioresour. Technol. 2013, 135,128–136.

928 929

(12) Arnon, D.I. Copper enzymes in isolated chloroplasts. Polyphenoloxidase in Beta vulgaris. Plant physio. 1949, 24: 1.



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(13) Gouveia, L.; Neves, C.; Sebastião, D.; Nobre, B.P.; Matos, C. T. Effect of light on the

931

production of bioelectricity and added-value microalgae biomass in a Photosynthetic

932

Alga Microbial Fuel Cell. Bioresour. Technol. 2014a, 154, 171–177.

933

(14) Olguín, E. J. Dual Purpose Microalgae-Bacteria-Based Systems that Treat Wastewater

934

and Produce Biodiesel and Chemical Products within a Biorefinery. Biotechnol Adv.

935

2012, 30, 1031–1046.

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(15) CPCB. Environmental Management in Selected Industrial Sectors Status and Needs,

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(16) Gouveia, L. From Tiny Microalgae to Huge Biorefineries. Oceanography. 2014b, 2, 120. (17) Folch, J.; Lees, M.; Stanley, G. H. S. A simple method for the isolation and purification of total lipides from animal tissues. J. Biol. Chem. 1957, 226, 497–509.

943

(18) Barbarino, E., &Lourenço, S. O. An evaluation of methods for extraction and

944

quantification of protein from marine macro- and microalgae. J. Appl. Phycol. 2005,

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946 947

(19) Lowry, O. H.; Rosebrough, N. J.; Farr, A. L.; Randall, R. J. Protein measurement with the folin phenol reagent. J. Biol. Chem. 1953, 193, 265–275.

948

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Overview of the sequential extraction of primary metabolites from oven dried S. obliquus grown on wastewater. Different primary metabolite (lipid, protein, and carbohydrate) was extracted in different step, and all possible combinations were investigated. Their individual yields, loss of biomass, and total economic value extractions were compared in all cases to find out optimal extraction sequence towards algal biorefinery concept.


Exploration of Microalgae Biorefinery by Optimizing Sequential

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