Selection of Microorganisms for Ethanol Production from ...

Chiang Mai J. Sci. 2019; 46(3) : 469-480http://epg.science.cmu.ac.th/ejournal/Contributed Paper

Selection of Microorganisms for Ethanol Production from Cashew Apple JuiceTrakul Prommajak [a], Noppol Leksawasdi [b] and Nithiya Rattanapanone* [c,d][a] Division of Food Safety in Agribusiness, School of Agriculture and Natural Resources,University of

Phayao, Phayao 56000, Thailand.[b] Bioprocess Research Cluster, School of Agro-Industry, Faculty of Agro-Industry, Chiang Mai University, Chiang Mai 50100, Thailand.[c] Postharvest Technology Research Center, Faculty of Agriculture, Chiang Mai University, Chiang Mai 50200, Thailand.[d] Faculty of Agro-Industry, Chiang Mai University, Chiang Mai 50100, Thailand.*Author for correspondence; e-mail: [email protected]

Received: 4 April 2018Revised: 24 October 2018

Accepted: 25 October 2018

ABSTRACT Cashew apple is an agricultural waste generated by cashew nut industry at approximately

13 million tons per year worldwide. Toal sugars content of cashew apple juice was about 10% (w/v) which comprised 45.87 ± 0.94 g L-1 glucose and 45.22 ± 0.48 g L-1 fructose. These sugars could be used as substrates for ethanol fermentation. The presence of tannin in the juice limited the growth of some microorganisms. Thus, selection of suitable microbial strains which can produce ethanol in cashew apple juice medium is required. In this study, 50 microbial strains in the genera of Saccharomyces spp., Candida spp., Klebsiella spp., Zymomonas spp., Kluyveromyces spp. and Escherichia spp. were inoculated in cashew apple juice without tannin precipitation. The sample was taken out after fermentation for 24 and 48 h. Four microbial strains, including S. ellipsoideus TISTR 5194, S. ellipsoideus TISTR 5199, C. krusei TISTR 5624 and S. cerevisiae UNSW 706900, could produce ethanol at the level higher than 30 g L-1 after 48 h of fermentation period. Among these potential strains, C. krusei TISTR 5624 had the highest ethanol productivity (1.31±0.04 g L-1h-1) and ethanol yield of 35.14±0.1.21 g L-1 which were appropriate for ethanol production from cashew apple juice.

Keywords: cashew apple juice, ethanol, Candida, Saccharomyces

1. INTRODUCTIONCashew (Anacardium occidentale) is a tropical

tree which is mainly cultivated in Latin America, East Africa, India, Southeast Asia, and Australia [1]. Typically, cashew is grown for its kidney-shaped nut which is considered real fruit botanically. The global production of cashew

nut was 1.6 million tons in 2000. The main producers were India, Vietnam and Brazil [2]. In Thailand, cultivation area of cashew tree was 23,847 ha in 2010 with 37,857 tons of cashew nut production [3].

During harvesting of cashew nut, its

Chiang Mai J. Sci. 2019; 46(3)470

peduncle or cashew apple of 0.30 tons was left in the field in 2010 and became significant agricultural waste as cashew apple was heavier than cashew nut by 8 times. The cashew apple juice contains about 10% of reducing sugar which can be used for the production of ethanol. Cashew apple juice contains tannins which can inhibit the growth of some microorganisms such as yeasts or bacteria. Tannins are secondary metabolites that plant used to expel insect and animal herbivores, as well as microbial infection. They have inhibitory effects on microorganisms by complexation with membrane proteins and extracellular enzymes, which in turn inhibit nutrient transport and microbial growth [4]. Tannins can be found in most plant tissues, including leaf, bud, seed, root, stem and fruit [5]. Many fruits were reported for containing tannins, e.g. berries, pomegranate, apple, grape, apricot, peach, guava and mango [6]. Tannins in fruit juice could be decreased by addition of earths, proteins, polysaccharides and enzymes [7]. Tannin precipitation by addition of gelatin was usually performed before fermentation [8-12]. However, this tannin removal step might increase production cost during industrial fermentation. It was reported that some strains of bacteria, mould and yeast could produce tannin hydrolyzing enzyme, tannase, or extracellular enzymes with tannin-resistance property [13-14]. Moreover, it was also reported that Saccharomyces cerevisiae could proliferate in cashew apple juice without significant inhibitory effect [10]. The raw material pretreatment step and production cost could be eliminated and minimized once the tannin precipitation step could be removed. The aim of this study was to evaluate and select the microorganisms that could produce ethanol with the relatively high yield in the presence of tannins.

2. MATERIALS AND METHODS2.1 Cashew Apple Juice Preparation

Cashew apple was harvested from a local

farm in Uttaradit province, Thailand during March to May 2011. The juice was extracted by a hydraulic press machine (Sakaya II, Sakaya Automate, Thailand). Potassium metabisulfite was added to the juice at 100 mg L-1 (100 ppm) to decrease microbial deterioration. The juice was clarified by Universal 320 R centrifuge (Hettich, Germany) at 2,000 x g for 10 min. Clarified cashew apple juice contained 91.09 ± 1.37 g L-1 reducing sugar.

2.2 Inoculum PreparationThirty-three strains of Candida spp., 9 strains

of Saccharomyces spp., 3 strains of Zymomonas spp., 2 strains of Kluyveromyces spp., 2 strains of Escherichia spp. and a stain of Klebsiella were provided by the Thailand Institute of Scientific and Technological Research (TISTR) and the University of New South Wales (UNSW), Australia (Table 1). All yeast strains were cultivated in yeast-malt medium (10 g glucose, 5 g peptone, 5 g malt extract, and 3 g yeast extract in 1 L distilled water). Zymomonas spp. were cultivated in Zymomonas medium (20 g glucose, 10 g yeast extract, and 10 g peptone in 1 L distilled water). Other bacteria were cultivated in nutrient broth (5 g peptone and 3 g beef extract in 1 L distilled water). After 24 h cultivation, the media were centrifuged with removal of some supernatant portion and the final optical density was adjusted to 20 at 600 nm. The microorganisms were kept in 20% (v/v) glycerol stock and stored in refrigerator at -22°C [15].

2.3 FermentationThe microbial strains were activated

by addition of 1 mL glycerol stock into 10 mL seed medium that was suitable for each microorganism as described above for 24 h. Then, 2 mL inoculum was transferred in to 500-mL centrifuge tube containing 20 mL clarified cashew apple juice and incubated at 30°C under shaking condition at 200 rpm.

Chiang Mai J. Sci. 2019; 46(3) 471

Table 1. Microorganism strains used for ethanol fermentation from cashew apple juice.

Code Strain SourceA

S.Fali Saccharomyces cerevisiae Fali® baker’s yeast

S.Angel Saccharomyces cerevisiae Angel® baker’s yeast

S.5020 Saccharomyces cerevisiae TISTR 5020




S.709800 Saccharomyces cureanes UNSW 709800

S.5194 Saccharomyces ellipsoideus TISTR 5194

S.5199 Saccharomyces ellipsoideus TISTR 5199

C.5681 Candida fennica TISTR 5681

C.5259 Candida krusei TISTR 5259


C.5156 Candida lusitaniae TISTR 5156

C.5165 Candida maltose TISTR 5165

C.5810 Candida pulcherrima TISTR 5810

C.5843 Candida shahatae TISTR 5843

C.5285 Candida sp. TISTR 5285

C.5306 Candida tropicalis TISTR 5306

C.5809 Candida pelliculosa TISTR 5809


C.5098 Candida famata TISTR 5098

C.5808 Candida guilliermondii TISTR 5098






C.5664 Candida magnoliae TISTR 5664

C.5687 Candida oleophila TISTR 5687

C.5008 Candida parapsilosis TISTR 5008

C.5069 Candida pseudointermedia TISTR 5069

C.5336 Candida pseudotropicalis TISTR 5336

C.5120 Candida pulcherrima TISTR 5120




C.5001 Candida utilis TISTR 5001




Chiang Mai J. Sci. 2019; 46(3)472

After 24 and 48 h cultivation time, 8 mL of the media were taken out for biomass, pH, total soluble solids (TSS), sugar, and ethanol analyses. Fermentation was conducted in quintuplicate [15].

2.4 Analytical DeterminationDried biomass was analyzed by the method

described by Tangtua et al. [15]. The pH of supernatant was measured by pH meter (pH 510, Eutech Instruments, Singapore). Total soluble solids (TSS) was measured by PAL-1 pocket refractometer (Atago, Japan).

Glucose, fructose and ethanol were analysed by High Performance Liquid Chromatography (HPLC, 1200 Series, Agilent Technologies, USA) using Aminex® HPX-87H column. The 20 µl of diluted samples were eluted by 5 mM sulfuric acid at the flow rate of 0.75 mL min-

1. The column and refractive index detector temperatures were set at 40°C. Propanol was used as internal standard [15].

Ethanol productivity and yield were calculated by the following equations:

Ethanol productivity (g L-1h-1)

mL min-1. The column and refractive index detector temperatures were set at 40°C. Propanol was

used as internal standard [15].


Ethanol productivity (g L-1h-1) = produced ethanol concentration (g L-1)

fermentation time (h)

Ethanol yield (%) = produced ethanol concentration (g L-1)

reducing sugars consumed (g L-1)×100

2.5 Experimental Design and Statistical Analysis

Completely randomized design was used to investigate substrates (glucose, fructose and

total soluble solids) consumption and products (ethanol, biomass and pH) formation of 50

microbial strains. Statistical analysis was performed by R version 2.15.2 (http://cran.r-

project.org/). Analysis of variance and Duncan’s new multiple range test was used for mean

comparison. Cluster analysis was used to classify the different growth patterns of microorganisms.

Optimal number of clusters was determined by ‘NbClust’ package. Hierarchy cluster analysis was

a statistical technique that grouped the samples from large data set by their similarity. The samples

were put in m-dimensional space and Euclidean distance was used to determine the distance

between every samples. Then, similar samples were iteratively grouped into the same cluster. This

method was suitable when grouping was not previously known [16].

3. RESULTS AND DISCUSSION

Ethanol yield (%)

mL min-1. The column and refractive index detector temperatures were set at 40°C. Propanol was

used as internal standard [15].


Ethanol productivity (g L-1h-1) = produced ethanol concentration (g L-1)

fermentation time (h)

Ethanol yield (%) = produced ethanol concentration (g L-1)

reducing sugars consumed (g L-1)×100


Completely randomized design was used to investigate substrates (glucose, fructose and

total soluble solids) consumption and products (ethanol, biomass and pH) formation of 50

microbial strains. Statistical analysis was performed by R version 2.15.2 (http://cran.r-

project.org/). Analysis of variance and Duncan’s new multiple range test was used for mean

comparison. Cluster analysis was used to classify the different growth patterns of microorganisms.

Optimal number of clusters was determined by ‘NbClust’ package. Hierarchy cluster analysis was

a statistical technique that grouped the samples from large data set by their similarity. The samples

were put in m-dimensional space and Euclidean distance was used to determine the distance

between every samples. Then, similar samples were iteratively grouped into the same cluster. This

method was suitable when grouping was not previously known [16].

3. RESULTS AND DISCUSSION


Completely randomized design was used to investigate substrates (glucose, fructose and total soluble solids) consumption and products (ethanol, biomass and pH) formation of 50 microbial strains. Statistical analysis was performed by R version 2.15.2 (http://cran.r-project.org/). Analysis of variance and Duncan’s new multiple range test was used for mean comparison. Cluster analysis was used to classify the different growth patterns of microorganisms. Optimal number of clusters was determined by ‘NbClust’ package. Hierarchy cluster analysis was a statistical technique that grouped the samples from large data set by their similarity. The samples were put in m-dimensional space and Euclidean distance

Table 1. Microorganism strains used for ethanol fermentation from cashew apple juice.(Continued)

Code Strain SourceA

C.709400 Candida utilis UNSW 709400

C.709700 Candida utilis UNSW 709700

K.5695 Kluyveromyces marxianus TISTR 5695

K.709700 Kluyveromyces marxianus UNSW 709700

E.361 Escherichia coli TISTR 361

E.1261 Escherichia coli TISTR 1261

K.1383 Klebsiella sp. TISTR 1383

Z.405 Zymomonas mobilis TISTR 405



A TISTR: Thailand Institute of Scientific and Technological Research, Thailand UNSW: University of New South Wales, Australia

Chiang Mai J. Sci. 2019; 46(3) 473

was used to determine the distance between every samples. Then, similar samples were iteratively grouped into the same cluster. This method was suitable when grouping was not previously known [16].

3. RESULTS AND DISCUSSIONFifty strains of microorganisms were

used for ethanol fermentation from cashew apple juice. These strains were selected based on their description on ethanol production from the culture collection institutes. Initial concentration levels on glucose and fructose in fresh cashew apple juice were 45.87±0.94 g L-1 and 45.22±0.48 g L-1, respectively. Concentration of condensed tannins in fresh cashew apple juice used in this study was 2.28± 0.18 mg cyanidin/100 ml. Glucose, fructose and ethanol concentration of cashew apple juice at 24 and 48 h were presented in Figure 1. Maximum ethanol concentration and corresponding ethanol yield were obtained by S. ellipsoideus TISTR 5194 (31.98±1.07 g L-1 and 35.13±1.18%) followed by S. ellipsoideus 5199 (31.54±0.75 g L-1 and 34.64±0.82%), C. krusei 5624 (30.91±0.55 g L-1 and 35.14±1.21%), and S. cerevisiae UNSW 706900 (30.89±0.65 g L-1 and 33.95±0.70%, respectively). However, the maximum ethanol concentration obtained in this study was still lower than those obtained by Pinheiro et al. [17]. In that study, 44.0 g L-1 ethanol and 84.3% ethanol yield were obtained from 103.1 g L-1

initial sugar in cashew apple juice using baker yeast and all reducing sugars were consumed during fermentation. Although this study also used baker’s yeast, the corresponding ethanol concentration and yield were 28.49±0.48 g L-1

and 31.17±0.06% after 48 h of fermentation time. Neelakandan et al. reported that 65 g L-1 ethanol concentration was obtained from fermentation of cashew apple juice with 265 g L-1 initial reducing sugar using S. cerevisiae [18]. Deenanath et al. reported that maximum ethanol concentration of 65 g L-1 could be

obtained from fermentation of cashew apple juice with 100 g L-1 initial sugar content and 2.5 g L-1 supplemented ammonium sulphate by S. cerevisiae NRRL Y2084 for 5 days [19]. The difference might be due to the different in yeast strain, variation in tannin content, clarification, nutrient supplementation and disinfection of cashew apple juice.

Maximum ethanol productivity was achieved by C. krusei TISTR 5624 (1.31±0.04 g L-1h-1), followed by C. oleophila 5687 (1.13±0.10 g L-1h-

1), C. magnoliae 5664 (1.03±0.03 g L-1h-1) and C. tropicalis 5615 (1.02±0.08 g L-1h-1). These yeasts could produce ethanol at a level higher than 24 g L-1 within 24 h. They were then considered as potential microorganisms for rapid ethanol production from cashew apple juice. Ethanol productivity obtained in this study was higher than that obtained by fermenting cashew apple juice using S. cerevisiae SCT at 0.6 g L-1h-1 [20], but lower than 6.37 L-1h-1 using baker’s yeast [17].Ethanol concentration decreased after 48 h of fermentation by some strains, for example, C. krusei 5301 (7.19±0.16 g L-1 less), C. krusei 5288 (7.02±2.31 g L-1 less) and C. magnoliae 5664 (6.27±1.23 g L-1 less). When the juice had low sugar content, these strains might use ethanol as a carbon source and incorporated into yeast cell, resulting in lower ethanol concentration and higher dried biomass concentration [21].Maximum dried biomass (10.10±0.45 g L-1) was obtained by Z. mobilis 548 at 48 h of fermentation. This value was lower than 13.68±0.09 g L-1 which was obtained from fermenting cashew apple juice using S. cerevisiae NCYC 125. The difference may due to supplementation of KH2PO4, urea and yeast extract in the latter publication. In case of non-supplemented juice, this strain produced 8.74±0.08 g L-1 biomass [22].

Overall, correlations were found between all measured variables, except pH-biomass and pH-fructose, as shown in Figure 2. When substrates (glucose and fructose) were consumed, pH

Chiang Mai J. Sci. 2019; 46(3)474

of the medium decreased, while the products (ethanol and biomass) increased.

Clustering is a statistical technique that arrange samples with high similarity into the same group and the samples with less similarity into different group. Many clustering algorithms require input of the cluster number for analysis. Deciding cluster number from the plot may

be a subjective determination. Therefore, ‘NbClust’ package in R software was used to calculate 30 statistical indices which determine a cluster number that fit the data [23]. For this data, most cluster analysis methods suggested that the optimal number of clusters was three.

Hierarchy cluster analysis reveals groups of microbial strains based on all measured

Figure 1. Glucose (A), fructose (B) and ethanol (C) concentrations of fermented cashew apple juice at 24 and 48 h. The graph shown the values of the first 20 strains ranked by ethanol concentration at 48 h. Initial concentration of glucose and fructose were 45.87±0.94 and 45.22±0.48 g L-1, respectively.Small and capital letters indicate statistical difference (P<0.05) between strains at 24 and 48 h, respectively.

opq

nopq

bf

hijk

klm

nopq

cpq q

ghi g

q

cde

ijkde

cdb

ijkf

ee

ag

ijkgh gh

hiij

lmno

pqlm

nopq

dee

hijk

delm

nopq

mno

pqlm

nopq

jkl

a agh

lmno

pq

lmno

pqm

nopq

opq jk

lmno

jklm jk

lmn

qjk

lmno

pjk

lmno

F FC

DF F F

AF F F F F

BF F

CB

F F FD

EA

F F F F F F FD

E

F F F F F F F EF EF F FF F F F F

EFF F F

05

101520253035404550

S.Fa

liS.

Ang

elS.

5020

S.53

39S.

5606

S.70

6900

S.70

9800

S.51

94S.

5199

C.5

681

C.5

259

C.5

624

C.5

156

C.5

165

C.5

810

C.5

843

C.5

285

C.5

306

C.5

809

C.5

303

C.5

098

C.5

808

C.5

264

C.5

271

C.5

288

C.5

296

C.5

301

C.5

664

C.5

687

C.5

008

C.5

069

C.5

336

C.5

120

C.5

144

C.5

350

C.5

615

C.5

001

C.5

032

C.5

043

C.5

046

C.7

0940

0C

.709

700

K.5

695

K.7

0970

0E.

361

E.12

61K

.138

3Z.

405

Z.54

8Z.

550

Glu

cose

(g/L

)

24 h

48 h

opq

cdij

nop

pqde

fg

rrs

tkl kl

mt

def

lmn

efgh

bc cdno

phi

efgh

abab

ijkl

ghi fg

hilm

prs

tbc

abc

jkcd

em

nop

nolm

ab aef

ghst st st st

rst rs rs

strs

t rst

J JD

GH

IJJ J

AJ J

FGH

J JC

IJE

BB

J IJF

B

AG

HIJ

HIJ GH

IJG

HIJ

GH

IJIJ IJ

CD

FGG

HIJ

GH

IJG

HIJ

HIJ

HIJ GH

IJE

EG

HIJ

J J J J IJ IJ GH

IJ J IJ

0

10

20

30

40

50

60

S.Fa

liS.

Ang

elS.

5020

S.53

39S.

5606

S.70

6900

S.70

9800

S.51

94S.

5199

C.5

681

C.5

259

C.5

624

C.5

156

C.5

165

C.5

810

C.5

843

C.5

285

C.5

306

C.5

809

C.5

303

C.5

098

C.5

808

C.5

264

C.5

271

C.5

288

C.5

296

C.5

301

C.5

664

C.5

687

C.5

008

C.5

069

C.5

336

C.5

120

C.5

144

C.5

350

C.5

615

C.5

001

C.5

032

C.5

043

C.5

046

C.7

0940

0C

.709

700

K.5

695

K.7

0970

0E.

361

E.12

61K

.138

3Z.

405

Z.54

8Z.

550

Fruc

tose

(g/L

)

24 h

48 h

(A)

(B)

Figure 1. Glucose (A), fructose (B) and ethanol (C) concentrations of fermented cashew apple juice at 24 and 48 h. The graph shown the values of the first 20 strains ranked by ethanol concentration at 48 h. Initial concentration of glucose and fructose were 45.870.94 and 45.220.48 g L-

1, respectively. Small and capital letters indicate statistical difference (P<0.05) between strains at 24 and 48 h, respectively.

efg cd

s

jkij ij

pqrs

efg cd

kij

alm

n

ijm

nopq

nopq

qrs

ilm

mno

pq klm

noh

g fgef

g def

cb

mno

p lij

lmi

de cij

s rsk

opqr

sqr

sqr

sqr

sqr

sqr

sqr

s nopq

rpq

rsqr

s

GH

CD

MI

BC

D AB

PA A

JKD

AB

NO

JKN O

PO

PG

HG

HEF

GM

P

KL

LM MK

LLM

JK

FGG

HA

BC

D D HI GH

DEF DE

CD

DEF DEF

Q Q Q Q Q Q Q Q QQ

0

5

10

15

20

25

30

35

S.Fa

liS.

Ang

elS.

5020

S.53

39S.

5606

S.70

6900

S.70

9800

S.51

94S.

5199

C.5

681

C.5

259

C.5

624

C.5

156

C.5

165

C.5

810

C.5

843

C.5

285

C.5

306

C.5

809

C.5

303

C.5

098

C.5

808

C.5

264

C.5

271

C.5

288

C.5

296

C.5

301

C.5

664

C.5

687

C.5

008

C.5

069

C.5

336

C.5

120

C.5

144

C.5

350

C.5

615

C.5

001

C.5

032

C.5

043

C.5

046

C.7

0940

0C

.709

700

K.5

695

K.7

0970

0E.

361

E.12

61K

.138

3Z.

405

Z.54

8Z.

550

Eth

anol

(g/L

)

24 h

48 h

(C)

opq

nopq

bf

hijk

klm

nopq

cpq q

ghi g

q

cde

ijkde

cdb

ijkf

ee

ag

ijkgh gh

hiij

lmno

pqlm

nopq

dee

hijk

delm

nopq

mno

pqlm

nopq

jkl

a agh

lmno

pq

lmno

pqm

nopq

opq jk

lmno

jklm jk

lmn

qjk

lmno

pjk

lmno

F FC

DF F F

AF F F F F

BF F

CB

F F FD

EA

F F F F F F FD

E

F F F F F F F EF EF F FF F F F F

EFF F F

05

101520253035404550

S.Fa

liS.

Ang

elS.

5020

S.53

39S.

5606

S.70

6900

S.70

9800

S.51

94S.

5199

C.5

681

C.5

259

C.5

624

C.5

156

C.5

165

C.5

810

C.5

843

C.5

285

C.5

306

C.5

809

C.5

303

C.5

098

C.5

808

C.5

264

C.5

271

C.5

288

C.5

296

C.5

301

C.5

664

C.5

687

C.5

008

C.5

069

C.5

336

C.5

120

C.5

144

C.5

350

C.5

615

C.5

001

C.5

032

C.5

043

C.5

046

C.7

0940

0C

.709

700

K.5

695

K.7

0970

0E.

361

E.12

61K

.138

3Z.

405

Z.54

8Z.

550

Glu

cose

(g/L

)

24 h

48 h

opq

cdij

nop

pqde

fg

rrs

tkl kl

mt

def

lmn

efgh

bc cdno

phi

efgh

abab

ijkl

ghi fg

hilm

prs

tbc

abc

jkcd

em

nop

nolm

ab aef

ghst st st st

rst rs rs

strs

t rst

J JD

GH

IJJ J

AJ J

FGH

J JC

IJE

BB

J IJF

B

AG

HIJ

HIJ GH

IJG

HIJ

GH

IJIJ IJ

CD

FGG

HIJ

GH

IJG

HIJ

HIJ

HIJ GH

IJE

EG

HIJ

J J J J IJ IJ GH

IJ J IJ

0

10

20

30

40

50

60

S.Fa

liS.

Ang

elS.

5020

S.53

39S.

5606

S.70

6900

S.70

9800

S.51

94S.

5199

C.5

681

C.5

259

C.5

624

C.5

156

C.5

165

C.5

810

C.5

843

C.5

285

C.5

306

C.5

809

C.5

303

C.5

098

C.5

808

C.5

264

C.5

271

C.5

288

C.5

296

C.5

301

C.5

664

C.5

687

C.5

008

C.5

069

C.5

336

C.5

120

C.5

144

C.5

350

C.5

615

C.5

001

C.5

032

C.5

043

C.5

046

C.7

0940

0C

.709

700

K.5

695

K.7

0970

0E.

361

E.12

61K

.138

3Z.

405

Z.54

8Z.

550

Fruc

tose

(g/L

)

24 h

48 h

(A)

(B)

Chiang Mai J. Sci. 2019; 46(3) 475

variables, as shown in Figure 3. The results from principal component analysis indicated that cumulative variation of 61.8% and 90.3% could be explained by the first two and five eigenvalues, respectively. Therefore, five principal components should be enough to retain data variance. The most contributing variables for principal components were glucose at 24 h, fructose at 48 h and total soluble solids at 24 h. Microbial strains could be divided into 3 groups.

Group 1 represented microbial strains which rapidly consumed sugars and produced high concentration of ethanol at 24 and 48 h. Glucose and fructose were almost completely consumed at 48 h. Mean ethanol concentration was at 16.07±7.51 g L-1 at 24 h and increased to 22.79±6.91 g L-1 at 48 h (Table 2). Consequently, total soluble solids and pH were lower than other groups while dried biomass concentration

was higher than other groups at 24 h. Group 2 was slower than group 1 in

consumption of sugars and production of ethanol. Mean ethanol concentration was relatively low at 3.30±2.19 g L-1 at 24 h and later increased to 17.10±9.25 g L-1 at 48 h. At the end of fermentation, glucose and fructose were still remained in significant concentrations (7.51±7.49 and 18.76±9.38 g L-1, respectively), indicating that more fermentation time was required for this group.

For group 1 and 2, correlation between fructose and ethanol was higher than correlation between fructose and biomass (Table 3). The same trend was also found in the case of glucose (Table 4). This result indicated that fructose and glucose was used for production of ethanol rather than dried biomass in those groups. However, the correlation of sugar-biomass and

Figure 2. Correlation matrix of biomass, pH, total soluble solid (TSS), glucose, fructose and ethanol concentration after fermentation (the values at 24 and 48 h were combined).

Group 1Group 2Group 3

Biomass

0 10 25 0 20 40 4 8 12

26

10

010

25

Ethanol

Fructose 020

40

020

40

Glucose

pH 3.5

4.5

2 6 10

48

12

0 20 40 3.5 4.5

TSS

Figure 2. Correlation matrix of biomass, pH, total soluble solids (TSS), glucose, fructose and ethanol concentration after fermentation (the values at 24 and 48 h were combined).

Chiang Mai J. Sci. 2019; 46(3)476

Figure 3. Centroid plot (A) and cluster dendrogram (B) of microorganisms based on all measured variables.

Figure 3. Centroid plot (A) and cluster dendrogram (B) of microorganisms based on all measured variables. sugar-ethanol of group 1 was lower than

those of group 2. This might be the result of rapid glucose consumption. Correlation plot showed that there was a correlation between ethanol and biomass for groups 1 and 2, indicating that ethanol was produced in concurrence with cell growth (Figure 3). However, this trend was not found in group 3 which cell mass and other metabolic products might be produced instead of ethanol.

Group 3 produced low concentration of ethanol (3.27±0.20 g L-1 at 48 h). However,

glucose and fructose were almost completely consumed in similar manner as group 1. Glucose, fructose and ethanol concentration levels had minor effect, although significant (P<0.05), change between 24 and 48 h, resulting in the correlation plots of these variables that gathered around a zero intercept (Figure 3). However, the consumed sugars were not instantly metabolized into cell mass at 24 h. TSS value indicated that the sugars may be converted into other soluble intermediates before synthesizing cell mass later at 48 h (Table 2). This group consisted of all

Chiang Mai J. Sci. 2019; 46(3) 477

Table 3. Correlation coefficients between fructose and other variables.

Groups Biomass Ethanol Glucose pH TSS

1 -0.39 -0.53 0.87 -0.09 0.79

(n=27) (<0.01) (<0.01) (<0.01) (0.51) (<0.01)

2 -0.70 -0.89 0.93 0.76 0.89

(n=14) (<0.01) (<0.01) (<0.01) (<0.01) (<0.01)

3 -0.72 -0.74 0.97 0.80 0.97

(n=9) (<0.01) (<0.01) (<0.01) (<0.01) (<0.01)

Numbers in parentheses indicates adjusted P-value.

Table 2. Compositions of fermented medium from 3 groups of microbial strains.

GroupsDried biomass (mg/mL)a pHa TSS (% w/v)a

24 h 48 h 24 h 48 h 24 h 48 h

1 5.10±1.68A 6.36±1.74A 3.93±0.28B 3.90±0.28B 5.07±0.86B 3.81±0.78B

2 2.84±0.80B 5.24±1.86B 4.31±0.16A 4.01±0.17AB 9.08±1.23A 5.66±0.98A

3 2.72±1.47B 6.10±2.65AB 4.24±0.33A 4.04±0.26A 9.38±2.84A 5.72±2.14A

GroupsEthanol (g/L)a Fructose (g/L)a Glucose (g/L)a

24 h 48 h 24 h 48 h 24 h 48 h

1 16.07±7.51A 22.79±6.91A 19.47±10.18B 1.64±1.36B 5.55±4.76B 0.62±0.57B

2 3.30±2.19B 17.10±9.25B 40.37±3.73A 18.76±9.38A 34.55±6.71A 7.51±7.49A

3 1.79±0.24B 3.27±0.20C 3.89±1.35C 1.20±1.34B 2.88±1.55B 0.44±0.82B

a Mean ± standard deviation of samples in each group (Group 1, n=27; Group 2, n=14; Group 3, n=9) Means with different letters in each column indicate significant difference (P<0.05).

Table 4. Correlation coefficients between glucose and other variables.

Groups Biomass Ethanol Fructose pH TSS

1 -0.44 -0.54 0.87 -0.09 0.74

(n=26) (0.02) (<0.01) (<0.01) (0.50) (<0.01)

2 -0.77 -0.85 0.93 0.82 0.94

(n=14) (<0.01) (<0.01) (<0.01) (<0.01) (<0.01)

3 -0.75 -0.74 0.97 0.80 0.97

(n=9) (<0.01) (<0.01) (<0.01) (<0.01) (<0.01)

Numbers in parentheses indicates adjusted P-value.

Chiang Mai J. Sci. 2019; 46(3)478

sugar-ethanol of group 1 was lower than those of group 2. This might be the result of rapid glucose consumption. Correlation plot showed that there was a correlation between ethanol and biomass for groups 1 and 2, indicating that ethanol was produced in concurrence with cell growth (Figure 3). However, this trend was not found in group 3 which cell mass and other metabolic products might be produced instead of ethanol.

Group 3 produced low concentration of ethanol (3.27±0.20 g L-1 at 48 h). However, glucose and fructose were almost completely consumed in similar manner as group 1. Glucose, fructose and ethanol concentration levels had minor effect, although significant (P<0.05), change between 24 and 48 h, resulting in the correlation plots of these variables that gathered around a zero intercept (Figure 3). However, the consumed sugars were not instantly metabolized into cell mass at 24 h. TSS value indicated that the sugars may be converted into other soluble intermediates before synthesizing cell mass later at 48 h (Table 2). This group consisted of all bacterial strains used in this study (Klebsiella, Zymomonas and Escherichia) and some strains of Candida. Although it was reported that Zymomonas mobilis MTCC 090 could be able to produce a maximum ethanol concentration of 12.64 g L-1 from 62% cashew apple juice (equivalent to 17.67% reducing sugar) using an optimal condition at the pH of 5.5, temperature of 32°C and fermentation time of 37 h [24]. The different Zymomonas strains used in the current study were not able to produce the same level of ethanol concentration. Low level of substrate concentration (glucose and fructose less than 3 g L-1 and 5 g L-1, respectively) at 48 h indicated that the microorganisms may synthesize the fermentation products other than ethanol. Zymomonas were fast sugar-consumption bacteria which produced ethanol, acetaldehyde and lactate [25]. Klebsiella produced higher alcohol, acetate and lactate along with ethanol [26].

Most strains showed glucophilic character with glucose-to-fructose consumption ratio of 1.02 to 5.78 after 24 of fermentation. An exceptional high glucose preference was found in C. famata 5098 which had glucose-to-fructose consumption ratio of 20.25. However, the discrepancy was decreased after 48 h of fermentation with glucose-to-fructose consumption ratio of 1.00 to 2.20 for all microorganisms. Correlation plot between glucose and ethanol also showed noticeable gathering of glucose value in low area compared to that of fructose and ethanol, indicating that glucose was consumed before fructose (Figure 2). Preference of microorganism on glucose or fructose consumption depended on the strains, nitrogen supplementation and temperature, which influence hexokinase activity [27]. In general, Saccharomyces had glucophilic character. However, the preference could shift toward fructose at low temperature for some strains [28]. Nitrogen supplementation could also stimulate fructose utilization [29].

4. CONCLUSIONFifty microbial strains were used to ferment

cashew apple juice without clarification and nutrient supplementation. These microorganisms could be categorized into 3 groups based on dried biomass, total soluble solids, pH, glucose, fructose and ethanol concentration of fermented juice at 24 and 48 h. The first group belonged to the fast ethanol producer. Among these strains, C. krusei 5624 was the most potent microorganism for ethanol production from cashew apple juice with the highest ethanol productivity (1.31±0.04 g L-1 h-1) and ethanol yield (35.14±0.1.21 g L-1). The second group was slow ethanol producers. The last group included all bacteria used in this study, which produced very low ethanol concentration (less than 3.6 g L-1).

ACKNOWLEDGEMENTSThe authors gratefully acknowledged the

Chiang Mai J. Sci. 2019; 46(3) 479

financial supports and / or in-kind assistance from National Research Council of Thailand (NRCT), Project Funding of National Research University-Chiang Mai University (NRU-CMU) and National Research University-Office of Higher Education Commission, Ministry of Education, Thailand (NRU-OHEC), Non-Food Agricultural Research Cluster, Faculty of Agro-Industry (FAI), Chiang Mai University (CMU), and Bioprocess Research Cluster (BRC).

REFERENCES[1] Mclaughlin J., Balerdi C. and Crane J.,

Cashew-apple fruit growing in the florida home landscape; Available at: http://edis.ifas.ufl.edu/hs377.

[2] Clay J.W. World Agriculture and the Environment: A Commodity-by-commodity Guide to Impacts and Practices, Island Press, Washington, D.C., 2004.

[3] Office of Agricultural Economics, Cashew Apple: Area, Production and Price during 1981-2012, Personal Communication, 2011.

[4] McSweeney C.S., Palmer B., McNeill D.M. and Krause D.O., Anim. Feed Sci. Technol., 2001; 91(1): 83-93. DOI 10.1016/S0377-8401(01)00232-2.

[5] Barbehenn R.V and Constabel C.P. Phytochem., 2011; 72(13): 1551-1565. DOI 10.1016/j.phytochem.2011.01.040.

[6] Smeriglio A., Barreca D., Bellocco E. and Trombetta D., Br. J. Pharmacol., 2017; 174(11): 1244-1262. DOI 10.1111/bph.13630.

[7] Dedehou E.S.C.A., Dossou J., Ahohuendo B., Saidou A., Ahanchede A. and Soumanou M.M., J. Appl. Biosci., 2015; 95: 8989-9002. DOI 10.4314/jab.v95i1.9.

[8] Attri B.L., Nat. Prod. Rad., 2009; 8(4): 374-379. DOI nopr.niscair.res.in/han-dle/123456789/5996.

[9] Damasceno L.F., Fernandes F.A.N., Magalhães M.M.A. and Brito E.S., Food Chem., 2008; 106(1): 172-179. DOI 10.1016/j.foodchem.2007.05.063.

[10] Joshi V.K., Sandhu D.K. and Thakur N.S., Fruit based Alcoholic Beverages; in Joshi V. K. and Pandey A., eds., Biotechnology: Food Fermentation (Vol. II), Educational Publishers & Distributors, New Delhi, 1999.

[11] Vergara C.M.A.C., Honorato T.L., Maia G.A. and S. Rodrigues, LWT-Food Sci. Tech-nol., 2010; 43(1): 141-145. DOI 10.1016/j.lwt.2009.06.009.

[12] Prommajak T., Leksawasdi N. and Rattana-panone N., CMU J. Nat. Sci., 2018; 17(1): 13-23. DOI 10.12982/CMUJNS.2018.0002.

[13] Jana A., Maity C., Halder S.K., Pati B.R., Mondal K.C. and Mohapatra P.K.D., Braz. J. Microbiol., 2012; 43: 1080-1083. DOI 10.1590/S1517-83822012000300034.

[14] Strumeyer D.H. and Malin M.J., Biochem. J., 1970; 118(5): 899-900. DOI 10.1042/bj1180899.

[15] Tangtua J., Techapun C., Pratanaphon R., Kuntiya A., Chaiyaso T., Hanmuangjai P., Seesuriyachan P. and Leksawasdi N., Chiang Mai J. Sci., 2013; 40(2): 299-304.

[16] Crawley M.J. The R Book., John Wiley & Sons, Chichester, 2007.

[17] Pinheiro Á., Rocha M., Macedo G. and Gonçalves L., Appl. Biochem. Biotechnol., 2008; 148: 227-234. DOI 10.1007/s12010-007-8118-7.

[18] Neelakandan T., Usharani G. and Sekar C., Int. J. Curr. Res., 2010; 11: 151-153. DOI 10.24941/ijcr.2017.

[19] Deenanath E.D., Rumbold K. and Iyuke S., ISRN Renew. Energ., 2013; Article ID 107851: 1-11. DOI 10.1155/2013/107851.

Chiang Mai J. Sci. 2019; 46(3)480

[20] Araújo S.M., Silva C.F., Moreira J.J.S., Narain N. and Souza R.R., J. Ind. Microbiol. Biotechnol., 2011; 38(9): 1161-1169. DOI 10.1007/s10295-010-0891-6.

[21] Gygi S.P., Rist B., Gerber S.A., Turecek F., Gelb M.H. and Aebersold R. Nat. Biotechnol., 1999; 17(10): 994-999. DOI 10.1038/13690.

[22] Osho A., Mol. Nutr. Food Res., 1995; 39(5‐6): 521-529. DOI 10.1002/food.19950390518.

[23] Charrad M., Ghazzali N., Boiteau V. and Niknafs A., J. Stat. Software, 2014; 61(6): 1-36. DOI 10.18637/jss.v061.i06.

[24] Karuppaiya M., Sasikumar E., Viruth-agiri T. and Vijayagopal V. Chem. Eng. Commun., 2009; 196(11): 1425-1435. DOI 10.1080/00986440902938972.

[25] Sahm H., Bringer-Meyer S. and Sprenger G. A., The Genus Zymomonas; in Dworkin M., Falkow S., Rosenberg E., Schleifer K.H. and Stackebrandt E., eds., The Pro-karyotes: Volume 5: Proteobacteria: Alpha and Beta Subclasses, Springer, New York, 2006: 201-221. DOI 10.1007/0-387-30745-1_10.

[26] Cheng K.K., Liu H.J. and Liu D.H., Biotechnol. Lett., 2005; 27(1): 19-22. DOI: 10.1007/s10529-004-6308-8.

[27] Berthels N.J., Cordero Otero R.R., Bauer F.F., Pretorius I.S. and Thevelein J.M., Appl. Microbiol. Biotechnol., 2008; 77(5): 1083-1091. DOI 10.1007/s00253-007-1231-2.

[28] Tronchoni J., Gamero A., Arroyo-López F.N., Barrio E. and Querol A., Int. J. Food Microbiol., 2009; 134(3): 237-243. DOI 10.1016/j.ijfoodmicro.2009.07.004.

[29] Berthels N.J., Cordero Otero R.R., Bauer F.F., Thevelein J.M. and Pretorius I.S., FEMS Yeast Res., 2004; 4(7): 683-689. DOI 10.1016/j.femsyr.2004.02.005.

Selection of Microorganisms for Ethanol Production from ...

Documents

Transcript of Selection of Microorganisms for Ethanol Production from ...