Optimization of Aeration and Agitation Rate for Lipid and Gamma Linolenic Acid Production by ...

13 Pages·2015·3.15 MB·English

Hindawi Publishing Corporation
e Scientific World Journal
Volume 2014, Article ID 280146, 12 pages
http://dx.doi.org/10.1155/2014/280146
Research Article
Optimization of Aeration and Agitation Rate for
Lipid and Gamma Linolenic Acid Production by
Cunninghamella bainieri 2A1 in Submerged Fermentation
Using Response Surface Methodology
NormahSaad,1PeymanAbdeshahian,2MohdSahaidKalil,2
WanMohtarWanYusoff,1andAidilAbdulHamid1
1SchoolofBiosciencesandBiotechnology,FacultyofScienceandTechnology,UniversitiKebangsaanMalaysia,
43600Bangi,Selangor,Malaysia
2DepartmentofChemicalandProcessEngineering,FacultyofEngineeringandBuiltEnvironment,UniversitiKebangsaanMalaysia,
43600Bangi,Selangor,Malaysia
CorrespondenceshouldbeaddressedtoAidilAbdulHamid;[email protected]
Received9February2014;Revised29September2014;Accepted29September2014;Published31December2014
AcademicEditor:SamiAifa
Copyright©2014NormahSaadetal.ThisisanopenaccessarticledistributedundertheCreativeCommonsAttributionLicense,
whichpermitsunrestricteduse,distribution,andreproductioninanymedium,providedtheoriginalworkisproperlycited.
ThelocallyisolatedfilamentousfungusCunninghamellabainieri2A1wascultivatedina5Lbioreactortoproducelipidandgamma-
linolenicacid(GLA).Theoptimizationwascarriedoutusingresponsesurfacemethodologybasedonacentralcompositedesign.
Astatisticalmodel,second-orderpolynomialmodel,wasadjustedtotheexperimentaldatatoevaluatetheeffectofkeyoperating
variables,includingaerationrateandagitationspeedonlipidproduction.Processanalysisshowedthatlinearandquadraticeffect
ofagitationintensitysignificantlyinfluencedlipidproductionprocess(𝑃 < 0.01).Thequadraticmodelalsoindicatedthatthe
interactionbetweenaerationrateandagitationspeedhadahighlysignificanteffectonlipidproduction(𝑃<0.01).Experimental
results showed that a lipid content of 38.71% was produced in optimum conditions using an airflow rate and agitation speed
of0.32vvmand599rpm,respectively.Similarresultsrevealedthat0.058(g/g)gamma-linolenicacidwasproducedinoptimum
conditionswhere1.0vvmaerationrateand441.45rpmagitationratewereused.Theregressionmodelconfirmedthataerationand
agitationwereofprimeimportanceforoptimumproductionoflipidinthebioreactor.
1.Introduction beaccumulatedbythefungalstrainsofCunninghamellaspp.
depending on the fermentation methods and culture con-
Theextensiveresearchstudieshavebeencarriedoutoverthe ditions [5, 6]. Similar studies have shown that a high lipid
lastdecadestodeveloplipidproduction.Theseattemptshave accumulationisattainedbyCunninghamellabainieri2A1in
aimed at improving the economic production of microbial shake flask culture [7]. It is worth noticing that microbial
lipidsratherthanplantandanimalderivedoils.Lipidshave lipids are mostly comprised of triacylglycerols (TAGs) with
been used as transesterified form for biofuel production. thelowerquantitiesoffreefattyacids,naturallipids(monoa-
Microbialoil(singlecelloil)offersfavourableadvantagesover cylglycerols, diacylglycerols, and steryl-esters), sterols, and
plantoilsandanimalfatssuchasreducedlifecirculation,low polarfractionssuchasphospholipids,sphingolipids,andgly-
labour efforts, higher possibility for large scale production, colipids.Ithasbeenfoundthatwhenoleaginousmicroorgan-
andlowaffectionbyclimatechanges[1,2]. ismsgrowonthesubstrateswithahydrophobiccharacteristic
Among microorganisms, Zygomycetes have shown to thelipidaccumulatedcontainsalowquantityofTAGs.The
producemicrobialoilfromorganicsubstances[3,4].Previ- processoflipidproductionfromhydrophobicsubstrateshas
ousstudieshaverevealedthatahighamountoflipidcould beenknownasdenovolipidaccumulation.Onthecontrary,
2 TheScientificWorldJournal
Table1:Processvariablesanddeterminedlevelsforcentralcompositedesign.
Variable Symbol Actuallevel Codedvalue
Aerationrate(vvm) 𝑋1 0.16 0.30 0.65 1 1.14 −1.41 −1 0 1 1.41
Agitationrate(rpm) 𝑋2 117.16 200 400 600 682.84 −1.41 −1 0 1 1.41
lipids produced from sugar-based substances show a high 2.2. Culture Medium and Fermentation in Bioreactor. The
amountofTAGsintheircompositions.Lipidsynthesisfrom Kendrick medium [22] was used in this study. The com-
sugar-richsubstrateshasbeencalledexnovolipidaccumu- position of this medium was as follows (in g/L): glu-
lation [8]. A number of hydrophobic substrates have been cose, 30; (NH4)2C4H4O6, 1.0; KH2PO4, 7.0; Na2HPO4, 2.0;
usedinthesinglecelloil(SCO)productionbyZygomycetes MgSO4⋅7H2O, 1.5; CaCl2⋅2H2O, 0.1; FeCl3⋅6H2O, 0.008;
includingvariedoilsderivedfromvegetables(oliveoils,corn ZnSO4⋅7H2O,0.001;CuSO4⋅5H2O,0.001;Co(NO3)2⋅6H2O,
oils,andsunfloweroils),purefatty-freeacids,fattyesters,and 0.0001; MnSO4⋅5H2O, 0.0001; and yeast extract, 1.5. The
fattywastessuchascrudfishoils[8,9].Awiderangeofsugar- Kendrickmediumwasthentransferredintoa5Lsubmerged
based substances and agricultural wastes have already been bioreactorwithaworkingvolume4L.TheinitialpHofthe
utilizedindenovolipidaccumulationincludingglycerol[10], culturemediumwasadjustedto6.0using1.0MHClor1.0M
∘
sweet sorghum [11], rice hulls hydrolysate [12], xylose [5], NaOH.Thebioreactorwassterilizedat121 Cfor30min.Seed
orange peel [6], tomato waste hydrolysate [13], pectin [14], culture with an inoculums size of 10% (v/v) was aseptically
and corn steep [15]. Moreover, oleaginous microorganisms added to the medium. The batch fermentation was carried
∘
arecapableofproducinglipidfromsugars-basedsubstratein outat30 Cfor120hindifferentaerationandagitationrates
differentwaysofthatfromfats[16]. accordingtotheexperimentaldesign.
Itiswellknownthatsubmergedfermentationprocessis
2.3.AnalyticalMethods. Thefungalmyceliumwasharvested
affected by varied operating parameters such as incubation
bythefiltrationof100mLofculturesuspensionusingfilter
temperature, pH, aeration, and agitation. The scale-up of
paper(WhatmanNo. 1).Thefilteredmyceliumwaswashed
microbial product formation from a shake flask to a bio-
with200mLofdistilledwater,storedat−20∘Cfor24h,and
reactor involves optimization of culture conditions in fer-
then put under freeze-dried conditions (Shell Freeze Dry,
mentationprocesses[17].Amongoperatingfactors,agitation
Labconco Lyph. Lock 6) for 24h to obtain the dry weight.
andaerationarepivotalinaerobicfermentationbioreactors
Thedryweightoffungalcellswasdeterminedusingabalance
sincetheyareofprimeimportancein industrialbioprocess
and it was used to determine the biomass, lipid, and GLA.
and scale-up of aerobic biosynthesis systems [18]. A large
A mixture of chloroform and methanol with a ratio of 2:1
number of fermentation processes have already been per-
(v/v)wasthenaddedtodriedmycelia,mixed,andfollowed
formedtoproducemicrobiallipidbyawiderangeoffungi
bythefiltrationofmixturetoextractlipid.Extractedlipidwas
inashakeflaskscaleduringthelastdecades[1,3–5,19–21].
transesterifiedwith5%sodiummethoxidemethanolsolution
However, much less work has been carried out to produce
[23].Thechloroformlayerwasobtainedandevaporatedusing
fungal lipid in scale-up bioreactors using Cunninghamella
a rotary evaporator. The resultant colorless or pale-yellow
sp. The current research work aimed to study fungal lipid
transparent methyl esters were analyzed by a Shimadzu
production by Cunninghamella bainieri 2A1 in an aerated
GC-14A gas chromatography equipped with a 3.1m glass
submergedbioreactor.Theeffectofairflowrateandagitation
column of 3.2mm bore packed (Shimadzu Shinchrom E71
intensity on lipid and GLA production was investigated by
5%/Shimalite 80–100). The instrument was fitted with a
response surface methodology (RSM) based on a central
flame ionization detector. Identification of peaks was based
compositedesign(CCD).
on standards from Orbiting Scientific Technology Sdn Bhd.
Fungal biomass was expressed as gram per liter of culture
2.MaterialsandMethods medium (g/L). Lipid content was defined as the percentage
of gram of lipid produced per gram of biomass (%). GLA
2.1. Microorganism and Inoculum Preparation. Locally iso- contentwasdefinedasthegramofGLAproducedpergram
lated Cunninghamella bainieri 2A1 was obtained from oflipid-freebiomass(g/g).
SchoolofBiosciencesandBiotechnology,FacultyofScience
andTechnology,UniversityKebangsaanMalaysia.Cunning- 2.4. Experimental Design. A series of experiments was de-
hamellabainieri2A1wasmaintainedonpotatodextroseagar signed based on a central composite design (CCD) for two
∘
(PDA) at 4 C. Inoculum preparation was carried out using independentvariables,andeachvariablevariedatfivelevels.
5
sporesuspensionincluding10 spores/mLharvestedfrom7- Theexperimentalvariablesstudiedwereagitationrate(rpm)
day-old PDA plates. Seed culture was prepared by transfer- and aeration rate (vvm). Each variable was coded at five
ring 20mL of spore suspension into a 500mL conical flask levels of −1.41, −1, 0, +1, and +1.41. The coded values and
containing 180mL of nitrogen-limited medium (Kendrick the actual levels of the variables are given in Table1. The
∘
medium). Seed culture was then incubated at 30 C with an designmatrixoftheperformedexperimentalrunsisshown
agitationrateof250rpmfor48handkeptfortheinoculation in Table2 representing thirteen treatment combinations of
ofculturemedium. batchculture.
TheScientificWorldJournal 3
Table2:ExperimentaldesignandtestresultsoflipidandGLAproducedbyCunninghamellabainieri2A1inthesubmergedbioreactor.
Run 𝑋1 𝑋2 Lipidconcentration(g/L) GLAconcentration(mg/L)
1 −1 1 4.23 567.5
2 0 0 4.53 573.12
3 0 −1.41 1.23 144.0
4 0 0 4.60 602.82
5 1 −1 2.35 322.65
6 0 0 4.18 539.09
7 1.41 0 4.74 747.72
8 0 0 4.62 591.0
9 1 1 3.95 621.0
10 −1.41 0 3.01 433.5
11 0 0 4.31 573.12
12 0 1.41 4.34 567.36
13 −1 −1 1.125 145.0
𝑋1:aerationrate(vvm);𝑋2:agitationrate(rpm).
2.5. Statistical Model. Experimental data from the mixture treatments2,4,6,8,and11includedcenterpointsofdesign
design(Table2)wereusedtofitasecond-orderpolynomial inwhichsameaerationrateandagitationintensitywereused
regression model (1) on experimental results (Table2) to for the estimation of test error. As can be found, treatment
representproductformationasafunctionofvariablestested: 7 showed high levels of lipid and GLA concentrations with
valuesashighas4.74g/Land747.72mg/L,respectively,where
𝑌=a0+∑a𝑖𝑋𝑖+∑a𝑖𝑖𝑋𝑖2+∑a𝑖𝑗𝑋𝑖𝑋𝑗, (1) aeration and agitation rates were set at 1.14vvm (1.41 as a
codedvalue)and400rpm(0asacodedvalue).Lipidaccumu-
where𝑌isthemeasuredresponse,𝑋𝑖 and𝑋𝑗 aretheinde- lationbydifferentfungisuchasCunninghamellaechinulata,
pendent variables, a0 represents the intercept, and a𝑖, a𝑖𝑖, Mortierellaisabellina,andMucorrouxiihavepreviouslybeen
and a𝑖𝑗 are the regression coefficients of the model [24]. studied[5,6,10–12].Inlinewiththisstudy,Fakasetal.[13]
The behavior of the generated model for two independent showed that flask culture of Cunninghamella echinulata on
variablesisexpressedmathematicallyas a tomato waste hydrolysate medium could produce a lipid
concentration of 7.8g/L. The production of SCO by Cun-
𝑌=a0+a1𝑋1+a2𝑋2+a11𝑋12+a22𝑋22+a12𝑋1𝑋2, (2) ninghamella echinulata and Mortierella isabellina was stud-
ied in shake-flask culture using sugar-based medium. The
where𝑌isthemeasuredresponse,a1anda2arelinearcoef- experimental results revealed that C. echinulata produced
ficients, a11 and a22 are squared coefficients, and a12 is an 3.9g/L lipid and 760.5mg/L GLA; however, M. isabellina
interactioncoefficient.𝑋1 and𝑋2 representcodedvaluesof triggered 9.9g/L lipid and 346.5mg/L GLA [25]. The same
aerationrate(vvm)andagitationspeed(rpm),respectively.
study revealed that lipid biosynthesis by M. isabellina from
Three responses were studied including biomass concen-
glucose in a 3-L bioreactor increased up to 12.7g/L. In this
tration (g/L), lipid content (%), and GLA content (g/g).
regard, studies fulfilled by Papanikolaou et al. [26] showed
StatisticalanalysisofthedatawasperformedusingDesign-
thatThamnidiumelegansCCF-1465producedalipidconcen-
Expert software (version 6.0.6 Stat-Ease, Inc.). The same
trationof9g/Linshakeflaskusingculturemediumcontain-
softwarewasusedforoptimizationofthevariables.
ingamixtureofglucose,fructose,andsucrose.Ontheother
hand,thebiosynthesisoflipidbyThamnidiumelegansfrom
2.6.VerificationoftheQuadraticModel. Inordertovalidate glucoseina3-Lsubmergedbioreactorexhibitedalipidand
optimumconditionspredictedbytheempiricalmodel,aset GLA concentration of 13.9g/L and 742mg/L, respectively,
ofbatchfermentationwasperformedinthebioreactorunder after200hfermentation[27].ItwasnotedthatM.isabellina
optimumconditions. could produce 301mg/L GLA from lactose supplemented
whey[28].
3.ResultsandDiscussion
3.2. Biomass Concentration in the Bioreactor. The biomass
3.1.LipidandGLAProductionintheBioreactor. Experimen- production by Cunninghamella bainieri 2A1 with different
tal results of lipid and GLA production by Cunninghamella combinationsofaerationandagitationratestudiedisshown
bainieri2A1accordingtoCCDareshowninTable2.Ascan in Table3. As can be seen, treatments 2, 4, 6, 8, and 11
be observed, two independent variables (aeration rate and included center pointsin the experimentaldesign in which
agitationintensity)werecontrolledatthelevelsdetermined the same aeration rate and agitation speed were used. As
byexperimentaldesign,whichwererepresentedas−1.41,−1, showninTable3,thehighestvalueofbiomassconcentration
0,1,and1.41(Table1).AscanbeseenfromresultsinTable2, was obtained in the treatment 9 with the value as high as
4 TheScientificWorldJournal
Table3:Experimentaldesignandtestresultsofbiomassconcentration,lipidyield,andGLAyieldproducedbyCunninghamellabainieri2A1
inthesubmergedbioreactor.
Run 𝑋1 𝑋2 Biomassconcentration(g/L) Lipidyield(%) GLAyield(g/g)
Actualvalue Predictedvalue Actualvalue Predictedvalue Actualvalue Predictedvalue
1 −1 1 11.35 11.35 37.28 39.06 0.05 0.052
2 0 0 11.94 11.82 38 37.75 0.048 0.049
3 0 −1.41 4.80 4.61 25.79 24.65 0.03 0.028
4 0 0 11.82 11.82 39 37.75 0.051 0.049
5 1 −1 7.17 7.38 32.79 34.27 0.045 0.047
6 0 0 11.47 11.82 36.48 37.75 0.047 0.049
7 1.41 0 12.06 11.82 39.33 37.43 0.062 0.060
8 0 0 11.82 11.82 39.17 37.75 0.05 0.049
9 1 1 12.42 12.55 31.85 34.01 0.05 0.052
10 −1.41 0 8.67 8.49 34.81 33.45 0.05 0.048
11 0 0 11.94 11.82 36.12 37.75 0.048 0.049
12 0 1.41 11.82 11.70 37.52 35.41 0.048 0.046
13 −1 −1 5.0 5.15 22.5 23.59 0.029 0.031
𝑋1:aerationrate(vvm);𝑋2:agitationrate(rpm).
Table4:AnalysisofvarianceforthequadraticpolynomialmodelofbiomassproductionbyCunninghamellabainieri2A1.
Source Polynomialcoefficients Sumofsquares DF Meansquare 𝐹value Prob>𝐹
Model 1.39 5 0.28 335.28 <0.0001∗
Intercept 2.47 1
𝑋1 0.11 0.11 1 0.11 127.19 <0.0001∗
𝑋2 0.33 0.87 1 0.87 1045.02 <0.0001∗
𝑋2 −0.081 0.046 1 0.046 54.94 0.0001∗
1
𝑋2 −0.23 0.38 1 0.38 461.40 <0.0001∗
2
𝑋1𝑋2 −0.066 0.018 1 0.018 21.18 0.0025∗
Residual 5.805𝐸−003 7 8.293𝐸−004
LackofFit 4.397𝐸−003 3 1.466𝐸−003 4.16 0.1010
PureError 1.408𝐸−003 4 3.521𝐸−004
∗
Statisticallysignificantat99%probabilitylevel.
𝑋1:aerationrate(vvm);𝑋2:agitationrate(rpm);𝑋12and𝑋22:thequadraticterms;𝑋1𝑋2:theinteractionterm.
𝑅2=0.9958.
12.42g/L where an aeration rate of 1.0vvm (1 as a coded of the fitted model was evaluated using the statistical test
value)andanagitationrateof600rpm(1asacodedvalue) for analysis of variance (ANOVA) (Table4). As the results
wereappliedinthebioreactor.However,thelowestbiomass shown in Table4, calculated model’s 𝐹 value of 335.28 with
concentration (4.80g/L) was measured when fermentation a probability value (𝑃 > 𝐹) less than 0.0001 suggested that
processwascarriedoutatanaerationrateandagitationspeed theselectedquadraticmodelwassignificantandfittedwellto
of 0.65vvm (0 as a coded value) and 117.16rpm (−1.41 as a theexperimentaldata(𝑃 < 0.01).Ascanbeobservedfrom
codedvalue),respectively(treatment3).Onthebasisofthe Table3,thevaluespredictedbytheexperimentalmodelwere
quadraticpolynomialequationofresponsesurfacemodel(2), also close to the actual values obtained in the experimental
thepresentmodelanddataanalysiswasdefinedforcombined results.Thelackoffitisameasureofthefailureofamodel
effectsoftheindependentvariablesunderstudyintermsof torepresentdataintheexperimentaldomainatwhichdata
codedfactors(3).Themodelwasalsotransformedtofitthe pointswerenotincludedintheregressionmodelorvariations
modelontheexperimentaldata: inthemodelscannotbeaccountedbyrandomerror.Ifthere
𝑌=2.47+0.11𝑋 +0.33𝑋 −0.081𝑋2 is a significant lack of fit, the response is not fitted. The 𝐹
1 2 1 valueforlackoffitwithavalueof4.16impliedthatthelack
(3)
−0.23𝑋2−0.066𝑋 𝑋 , of fit was insignificant and hence the model was valid for
2 1 2 furtherstudies.Table4alsoshowsthesignificanceoflinear,
where 𝑌 is the biomass concentration value (g/L) and 𝑋1 interaction, and quadratic effects of the variables based on
and 𝑋2 are the coded values of aeration rate (vvm) and theirprobabilityvalues.Ascanbeseen,linearterms(𝑋1and
agitationrate(rpm),respectively.Thestatisticalsignificance 𝑋2),interactioneffect(𝑋1𝑋2),andquadraticterms(𝑋12 and
TheScientificWorldJournal 5
12.55
)L 13.42
on (g/L) 9.77 /g( noita 108..5362
ntrati rtnec 6.55
ce no 4.98
n c
omass co 7.61 ssamoiB
bi
d
dicte 5.92 600
e 1
Pr 500
4.61 4.61 5.92 7.61 9.77 12.55 Agitation rate (4rp0m0)300 200 0.30 0.48 Aer0a.t6i5on rat0e .8(v2v m)
Actual biomass concentration (g/L)
Figure2:Responsesurfaceplotshowingthesimultaneouseffects
Figure 1: Experimental biomass concentration produced versus of aeration rate and agitation rate on biomass production by
thepredictedbiomassconcentrationbythestatisticalmodelunder Cunninghamellabainieri2A1.
operatingconditions.
𝑋2)ofaerationrateandagitationspeedhadhighlysignificant
2 99
effects on biomass concentration (𝑃 < 0.01). The multiple
coefficientofdetermination(𝑅2)representsthemeasurement 95
ofthedegreeofthereductionintheresponsevariability.Itis 90
postulatedthat𝑅2valuehigherthan0.9representsastronger %)
rthegisrecsassieo,n𝑅m2woditehltfhoerathcceepprteadbilcetvioanluoefotfh0e.9r9es5p8oinmspel[ie2d9]t.hIant bability ( 758000
99.58%ofthevariabilityintheresponsecouldbeattributed pro
totheindependentparametersstudiedandonly0.42%ofthe al 30
m 20
totalvariationcouldnotbeexplainedbythequadraticmodel. or
N 10
A regression model can be used to predict observations
5
on the response (biomass concentration) corresponding to
particular values of the regressor variables. Figure1 shows
1
observed biomass production versus those predicted from
themodel(3).Thisfigureconfirmsthatthepredicteddataof
theresponsesurfacebythestatisticalmodelwereconsistent
withtheobservedonesintherangeoftheoperatingvariables. −1.95 −0.94 0.07 1.07 2.08
Inordertofindouttheinteractioneffectsbetweenvariables Studentized residuals
on the response, three-dimensional response surface graph
wasconstructedbyplottingtheresponseonthe𝑧-axisagainst Figure3:Normalprobabilityofstudentizedresidualsforbiomass
concentration
twoindependentvariables(Figure2).
Figure2showsthecombinedeffectofaerationrateand
agitation speed on the biomass concentration. As can be
seen, an increase in biomass concentration occurred when assumption (Figure3). As can be seen from Figure3, the
aerationratebegantoincreasewiththelowlevelofagitation normalassumptionwasacceptableasanormaldistribution
speed.Asubsequentriseinagitationrateresultedinasignif- of residuals was generated. A plot of residuals versus the
icant increase in biomass concentrationwhich indicated an predicted response values is given in Figure4. As shown,
interactionbetweenaerationandagitationaffectingbiomass there was a random scatter of residuals across the graph
production. which suggested that the variance of original observations
Generally, it is important to find out the adequacy of wasconstantforallvaluesof𝑦-axis.Bothoftheplots(Figures
the fitted model to make sure that it provides an adequate 3 and 4) confirmed that the empirical model was adequate
approximation to the real conditions. By plotting a normal to describe biomass production by the response surface.
probabilityofresiduals,acheckwasmadeforthenormality The empirical model was also used to determine optimum
6 TheScientificWorldJournal
Table5:AnalysisofvarianceforthequadraticpolynomialmodeloflipidcontentproducedbyCunninghamellabainieri2A1.
Source Polynomialcoefficients Sumofsquares DF Meansquare 𝐹value Prob>𝐹
∗
Model 300.21 5 60.4 13.84 0.0016
Intercept 37.75 1
𝑋1 1.41 15.83 1 15.83 3.65 0.0978
𝑋2 3.80 115.74 1 115.74 26.68 0.0013∗
𝑋2 −1.161 9.30 1 9.30 2.14 0.1865
1
𝑋2 −3.86 103.86 1 103.86 23.94 0.0018∗
2
𝑋1𝑋2 −3.93 61.78 1 61.78 14.24 0.0070∗
Residual 30.37 7 4.34
Lackoffit 22.46 3 7.49 3.79 0.1156
Pureerror 7.91 4 1.98
∗
Statisticallysignificantat99%probabilitylevel.
𝑋1:aerationrate(vvm);𝑋2:agitationrate(rpm);𝑋12and𝑋22:thequadraticterms;𝑋1𝑋2:theinteractionterm.
𝑅2=0.9081.
glucose-basedmedium.Inanattemptfortheproductionof
3.00
lipidinalaboratory-scalebioreactorwithaworkingvolume
of2.5L,thehighestbiomassproducedbyMortierellaraman-
niana,Mucorsp.,andZygorhynchusmoellerionglycerolwere
1.50 7.2g/L,1.6g/L,and0.7g/L,respectively[10].Thecultivation
of oleaginous strain Thamnidium elegans in the bioreactor
als containingglucoseandolivemillwastewaterresultedinthe
u
d
esi productionof2.5g/Loffat-freebiomass[30].
d r 0.00
e
z
nti 3.3.LipidContentintheBioreactor. Lipidcontent(lipidper-
de centage)producedbyCunninghamellabainieri2A1atvaried
u
St combinations of aeration rate and agitation speed is shown
−1.50
in Table3. As can be seen, the lipid content obtained in
the center points of experimental design is presented in
treatments 2, 4, 6, 8, and 11 using an aeration rate and
agitationspeedof0.65vvm(0asacodedvalue)and400rpm
−3.00
(0 as a coded value), respectively. As can be found, the
highest lipid content was obtained in the treatment 7 with
4.61 5.92 7.61 9.77 12.55
thevalueashighas39.33%whenfermentationprocesswas
Predicted biomass concentration (g/L)
carried out at an aeration rate of 1.14vvm (1.41 as a coded
Figure4:Plotofstudentizedresidualsversusthepredictedbiomass value)andanagitationrateof400rpm(0asacodedvalue).
concentration. However, minimum lipid content of 22.5% was measured
whenfermentationprocesswascarriedoutatanaerationrate
and agitation speed of 0.30vvm (−1 as a coded value) and
200rpm(−1asacodedvalue),respectively(treatment13).By
conditionsforthehighestproductionofbiomass.Theopti-
applying multiple regression analysis to the test results, the
mum conditions suggested by the quadratic model were an
followingsecondorderpolynomialequation(4)wasobtained
aeration rate of 0.63vvm and an agitation rate of 506rpm.
inordertorepresentlipidcontentasafunctionofairflowrate
The biomass production of 13.07g/L was predicted by the
andagitationspeed:
regressionmodelinoptimumconditionssuggested.Inorder
to verify optimum conditions, a fermentation experiment 𝑌=37.75+1.41𝑋 +3.80𝑋 −1.161𝑋2
1 2 1
was run by the cultivation of Cunninghamella bainieri 2A1 (4)
in the bioreactor under optimum conditions determined. −3.86𝑋2−3.93𝑋 𝑋 ,
2 1 2
Theexperimentalresultshowedthatabiomassconcentration
of 13.0g/L was obtained confirming a high fit between the where 𝑌 is the lipid content (%) and 𝑋1 and 𝑋2 are the
statistical model and experimental data. It was noted that coded values of aeration rate (vvm) and agitation rate
thecultivationofThamnidiumelegansinthebioreactorusing (rpm),respectively.Thestatisticaltestforanalysisofvariance
glucose-rich medium brought about 30.1g/L biomass [27]. (ANOVA)wasgeneratedtoevaluatestatisticalsignificanceof
Fakas et al. [5] found that Cunninghamella echinulata and theempiricalmodelselected(Table5).
Mortierella isabellina produced a biomass concentration of It is noteworthy that the actual values of lipid content
15g/L and 27g/L, respectively, in shake flask containing obtainedintheexperimentalresultswellmatchedthevalues
TheScientificWorldJournal 7
39.33
39.06
35.12
%) %) 35.19
d lipid content ( 30.91 Lipid content ( 322173..3.45269
e
ct
di
Pre 26.71 1
0.82 m)
600 v
22.50 22.50 26.71 30.91 35.12 39.33 A5gi0t0ation ra4t0e 0(rpm)300 200 0.300.480.A6era5tion rate (v
Actual lipid content (%)
Figure6:Responsesurfaceplotshowingthesimultaneouseffects
Figure5:Experimentallipidcontentproducedversusthepredicted of aeration rate and agitation rate on lipid content produced by
lipidcontentbythestatisticalmodelunderoperatingconditions. Cunninghamellabainieri2A1.
predicted by the experimental model (Table3). As can be
observed from Table5, computed model’s 𝐹 value of 13.84
99
withaprobabilityvalue(Prob> 𝐹)of0.0016indicatedthat
the quadratic regression model was significantly fitted for 95
experimental data (𝑃 < 0.01). The lack of fit related to 𝐹 90
value (3.79) implied that the lack of fit was not significant %)
raeclcaetpivteabtloeftohrefuprutrheererervoarl.uHateionnce.A, tshceanrebgeresesseinonfrommoTdaebllwea5s, bility ( 7800
thhaedlihniegahrlyansdigqnuifiadcarnatticeffteercmt oonfalgiiptiadtioynierldate(𝑃(𝑋2<and0.0𝑋122)). mal proba 532000
Theregressionmodelalsoshowedthattheinteractioneffect or
between aeration rate and agitation rate (𝑋1𝑋2) was highly N 10
significantat99%probabilitylevel(𝑃 < 0.01).Themultiple 5
coefficientofdetermination(𝑅2)withthesatisfactoryvalue
1
of 0.9081 implied that 90.81% of the variability in response
couldbeexplainedbytheempiricalmodelandonly9.19%of
thetotalvariationcouldnotbeexplainedbythemodel.
Figure5plotsexperimentalresultsoflipidcontentversus −1.70 −0.86 −0.02 0.82 1.66
thevaluesoflipidcontentpredictedbythequadraticmodel Studentized residuals
(4). Thisfigure confirmsthat thepredictedlipid contentby
Figure7:Normalprobabilityofstudentizedresidualsforlipidcon-
the regression model was in line with the obtained results
tent.
in the range of the variables studied. In order to describe
the effects of aeration and agitationon lipid content, three-
dimensionalresponsesurfacegraphofsimultaneouseffectof
thevariablesonlipidpercentagewasconstructedonthebasis across the graph. The plots illustrated in Figures 7 and 8
ofthemodel(Figure6).Asshown,thevariationsinaeration indicatedthatthepolynomialregressionmodelhadsatisfac-
rateinteractedwithagitationratesusedsothatanincrement toryadequacyforrepresentinglipidcontentobtainedbythe
in aeration rate and agitation speed concurrently increased responsesurface.Theanalysisofthestatisticalmodelshowed
lipidcontentuptoamaximumlevel. that the optimum conditions for the highest achievement
Thenormalplotofresidualsforlipidcontentisdepicted of lipid content was obtained when airflow and agitation
in Figure7. As can be seen, a satisfactory distribution of speed were used at the rates of 0.32vvm and 599rpm,
normal probability in relation to residuals was generated. respectively. The statistical model predicted that a lipid
Moreover, the plot of residuals versus the predicted lipid contentof39.04%couldbeproducedunderoptimumcondi-
yield (Figure8) showed an acceptable scatter of residuals tionssuggested.Averificationexperimentwasperformedin
8 TheScientificWorldJournal
Table6:AnalysisofvarianceforthequadraticpolynomialmodelofGLAcontentproducedbyCunninghamellabainieri2A1.
Source Polynomialcoefficients Sumofsquares DF Meansquare 𝐹value Prob>𝐹
Model 8.534𝐸−004 5 1.707𝐸−004 27.81 0.0002∗∗
Intercept 0.49
𝑋1 4.121𝐸−003 1.359𝐸−004 1 1.359𝐸−004 22.14 0.0022∗∗
𝑋2 6.432𝐸−003 3.310𝐸−004 1 3.310𝐸−004 53.93 0.0002∗∗
𝑋2 2.600𝐸−003 4.703𝐸−005 1 4.703𝐸−005 7.66 0.0278∗
1
𝑋2 −5.900𝐸−003 2.422𝐸−004 1 2.422𝐸−004 39.46 0.0004∗∗
2
𝑋1𝑋2 −4.000𝐸−003 6.400𝐸−005 1 6.400𝐸−005 10.43 0.0145∗
Residual 4.295𝐸−005 7 6.136𝐸−006
Lackoffit 3.215𝐸−005 3 1.072𝐸−005 3.97 0.1081
Pureerror 1.080𝐸−005 4 2.700𝐸−006
∗
Statisticallysignificantat95%probabilitylevel.
∗∗
Statisticallysignificantat99%probabilitylevel.
𝑋1:aerationrate(vvm);𝑋2:agitationrate(rpm);𝑋12and𝑋22:thequadraticterms;𝑋1𝑋2:theinteractionterm.
𝑅2=0.9521.
3.4.GLAContentintheBioreactor. Table3showstheexper-
3.00
imentalresultsofGLAcontentobtainedfromthecultivation
ofC.bainieri2A1indifferentlevelsofaerationandagitation
rateswhichweredeterminedbytheexperimentaldesign.It
1.50 isobviousthatthecenterpointsoftheexperimentaldesign
were in treatments 2, 4, 6, 8, and 11. As is evident, the
uals highestvalueofGLAcontentwasobtainedinthetreatment
d
esi 7 with the value as high as 0.062 (g/g) when fermentation
d r 0.00 process was carried out at an aeration rate of 1.14vvm (1.41
e
ntiz as a coded value) and an agitation rate of 400rpm (0 as
de a coded value). However, minimum GLA content of 0.029
u
St (g/g)wasmeasuredwhenfermentationprocesswascarried
−1.50
out at an aeration rate and agitation speed of 0.30vvm
(−1 as a coded value) and 200rpm (−1 as a coded value),
respectively(treatment13).Asecondordermodelequation
(5)wasfittedontheexperimentaldataobtainedaccordingto
−3.00
theexperimentaldesign:
23.59 27.46 31.33 35.19 39.06 𝑌=0.049+4.121𝐸−003𝑋 +6.432𝐸−003𝑋 +2.600𝐸
1 2
Predicted lipid content (%)
−003𝑋2−5.900𝐸−003𝑋2−4.000𝐸−003𝑋 𝑋 ,
1 2 1 2
Figure 8: Plot of studentized residuals versus the predicted lipid
(5)
content.
where𝑌istheGLAcontent(g/g)and𝑋1and𝑋2arethecoded
valuesofaerationrate(vvm)andagitationrate(rpm),respec-
optimumconditionstomeasurelipidpercentageinthebio- tively. The significance of the response surface quadratic
reactor. Experimental result showed that a lipid content of model was evaluated using statistical analysis of variance
38.71%wasproducedwhichconfirmedthatthemodelused (ANOVA), which is essential for determining patterns of
was valid and the results could be reproducible. Scaling- interactionbetweenexperimentalvariables(Table6).Onthe
up of lipid production by M. isabellina on glucose in a other hand, GLA content obtained from the experimental
submerged-bioreactor showed that a lipid content of 72.5% resultswasappropriatelyconsistentwiththevaluespredicted
wasproduced[25].SubmergedfermentationofThamnidium bytheexperimentalmodel(Table3).Obviously,themodel𝐹
elegans CCF-1465 in shake-flask culture revealed that this valueof27.81withaprobabilityvalue(Prob> 𝐹)of0.0002
strainassimilatedsugarsinthemediumandproduced70% impliedthattheregressionmodelwassignificant(𝑃<0.01).
lipidindrybiomass[26].TheproductionoflipidbyMucor Furthermore, the lack of fit of the model with the value of
circinelloides from citric acid as sole carbon source was 3.97indicatedthatthelackoffitwasinsignificant.Therefore,
studied by Aggelis [31]. This study revealed that maximum the empirical model adequately fitted on the experimental
15.9%lipidcontentwasobtainedwhere10g/Lcitricacidwas results.AsshowninTable6,thelineareffectofaerationrate
supplementedtotheculturemedium. andagitationrate(𝑋1and𝑋2)hadahighlysignificanteffect
TheScientificWorldJournal 9
0.062
0.055
g)
nt (g/g) 0.053 ntent (g/ 00..004493
onte A co 0.037
A c 0.045 GL 0.030
L
G
d
e
ct
di
Pre 0.036 1
600 0.82
500 m)
0.028 Agitation ra4te0 (0rpm)300 200 0.30 0.48Aer0at.i6o5n rate (vv
0.028 0.036 0.045 0.053 0.062
Figure10:Responsesurfaceplotshowingthesimultaneouseffects
Actual GLA content (g/g) of aeration rate and agitation rate on GLA content produced by
Cunninghamellabainieri2A1.
Figure9:ExperimentalGLAcontentproducedversusthepredicted
GLAcontentbythestatisticalmodelunderoperatingconditions.
onGLAcontent(𝑃<0.01).Moreover,thequadraticeffectsof
aerationrateandagitationrate(𝑋2and𝑋2)onGLAcontent 99
1 2
weresignificantat95%probabilitylevel(𝑃 < 0.05)and99% 95
probability level (𝑃 < 0.01), respectively. The significance 90
studyofthemodeltermsalsorevealedthattheaerationrate %)
aonndGaLgiAtatcioonnterantte(h𝑃ad<as0i.g0n5i)fi.cThaneti𝑅nt-esrqaucatrioedneofffetchte(𝑋m1o𝑋d2e)l bility ( 7800
(𝑅2) with the value of 0.9521 suggested that 95.21% of the oba 50
variabilityintheresponse(GLAcontent)couldbeexplained mal pr 3200
bytheregressionmodelandonly4.79%ofthetotalvariation or
couldnotbeexplainedbythequadraticmodel. N 10
5
ThevaluesofGLAcontentmeasuredintheexperimental
results versus predicted values by the statistical model are
1
depicted in Figure9. As is evident, observed GLA content
valueswereinaccordancewiththosepredictedbythemodel
(Table3). The three-dimensional response surface graph of
simultaneouseffectofaerationandagitationwasconstructed −1.44 −0.72 −0.01 0.71 1.43
toillustratetheinteractioneffectofthesevariablesonGLA
Studentized residuals
content (Figure10). As can be observed, a quadratic rise
in the response was formed with increased aeration and Figure11:NormalprobabilityofstudentizedresidualsforGLAcon-
agitationratesintherangeoflevelstested.Hence,variations tent.
in response were affected by different rates of aeration and
agitationindicatingtheinteractionbetweentheseparameters
ontheGLAcontentproduced.Thenormalplotofresiduals 1.0vvmandanagitationspeedof441.45rpm.Thequadratic
for GLA content is illustrated in Figure11. As shown, an modelalsopredictedthataGLAcontentof0.055(g/g)could
appropriate distribution of normal probability in relation beattainedintheoptimumconditions.
to residuals was formed. The plot of residuals versus the Inordertoverifyoptimumconditionssuggestedbythe
predicted GLA content is shown in Figure12. Obviously, model, a set of experiment was performed in the optimum
an acceptable scatter of residuals across the graph was conditions suggested by the cultivation of C. bainieri 2A1
generated.TheplotsshowninFigures11and12revealedthat in the bioreactor under optimum conditions. The result
the quadratic regression model had a suitable adequacy to obtained from verification experiment showed that a GLA
explainGLAcontentobtainedbytheresponsesurface.Fur- content of 0.058 (g/g) was produced. This finding validated
ther statistical analysis suggested that optimum conditions theaccuracyoftheempiricalmodelusedforattainingsuffi-
forthehighestproductionofGLAcontentwasaflowrateof cienthomogeneityandadequateaerationintheculture.Ithas
10 TheScientificWorldJournal
production.Ithasbeenknownthataerationandagitationare
3.00
ofgreatimportanceinaerobicculture.Ariseinaerationand
agitationratesleadstoanincreasedoxygensupply,improved
mixing and enhancement of mass transfer phenomenon
1.50 in a bioreactor of submerged fermentation, which in turn
increasesmicrobialcellgrowthandbiomassproduction[34].
uals Asreportedelsewhere,aerationratehadfavorableeffectson
d
esi the production of GLA by Mortierella isabellina using olive
d r 0.00 mill wastewater [30]. Studies fulfilled by Bajaj and Singhal
e
ntiz [35]alsorevealedthatairflowrateandagitationspeedwere
de criticalfactorsforbiomassproductionbymicrobialcells.
u
St Similar to biomass production, a reciprocal relation
−1.50
between agitation intensities and lipid yield was found
(Figure6). As can be seen, an increase in agitation rate
resultedinincreasedlipidyield.Thisfactcanbeattributedto
improvedhomogeneityofthecultureandtheenhancement
−3.00
ofheatandnutrienttransferintheculturewhichincreased
microbialgrowthandmetabolism-mediatedproductforma-
0.028 0.036 0.044 0.052 0.060
tion. In an attempt for the production of lipid by Yarrowia
Predicted GLA content (g/g)
lipolyticaithasbeenfoundthatincreasedagitationintensity
Figure12:PlotofstudentizedresidualsversusthepredictedGLA andaerationratecausedariseinbiomassproductioncom-
content. pared to shake flask, whereas a low lipid was accumulated
compared to that in flask scale [36]. On the other hand, an
incrementinaerationcausedabettermixingintensitywith
higherdissolvedoxygenmaintenanceintheculturemedium
been foundthatGLAsynthesisisvariedinrelationtolipid
[37].Consequently,airflowandagitationcausedaninterac-
accumulationbyZygomycetes.Inthisregard,theproduction
tioneffectonlipidproductionbyC.bainieri2A1(Figure6).
oflipidislowatearlylipidaccumulation,whileGLAcontent
The model trend in the response formed for GLA yield
is high at this time. As lipid synthesis proceeds, with the
revealed that aeration and agitation intensity had positive
increasing in the concentration of oil content the quantity
effects on GLA production (Figure10) because increased
of GLA starts dwindling. The process of SCO synthesis is
aerationandagitationratesmadebettermechanicalmixing
followedbyanincrementinGLAcontentatthelateprocess
and enhanced oxygen and nutrient transfer in the culture,
whereadecreaseinlipidproductionoccurs[32].
whichinturnariseinmyceliagrowthandGLAproduction
TheproductionofGLAbyThamnidiumelegansonglu-
occurred[38].
coseinalaboratorybioreactorwasinvestigated.Itwasnoted
thatfungalstrainusedcouldtriggerGLAcontentof0.031g/g
[27].TheproductionofGLAbyCunninghamellaechinulata 4.Conclusions
and Mortierella isabellina in shake flask revealed that 19.5%
GLAwasproducedbyC.echinulataand3.5%GLAwaspro- This study showed a successful process modeling in the
ducedbyM.isabellina[25].InanattemptforGLAsynthesis productionofbiomass,lipid,andGLAbyC.bainieri2A1in
by different strain of Zygomycetes, it was found that GLA a submerged bioreactor using RSM. The regression models
contentproducedbyZ.moelleri,Cunninghamellaechinulata, fittedwellwithexperimentalresultsobtained.Theanalysisof
and Mucor sp., from glycerol were 21.4%, 19.9%, and 19.8%, themodelsrevealedthatairflowratesandagitationintensities
respectively[10].TheproductionoflipidbyCunninghamella hadsignificanteffectsonbiomass,lipid,andGLAsynthesisby
echinulata was studied by Gema et al. [6] who used orange C. bainieri2A1intherangeofoperatinglevels determined.
peel as substrate for lipid synthesis. The authors found that The product values obtained in optimum conditions were
themaximumGLAcontentof1.2–15mg/gfermentedorange close to the values predicted by the models confirming the
peelwasobtainedwheresolid-statefermentationwasapplied. accuracyofthemodelsused.
3.5.TheEffectofAerationandAgitation. Aerationhasacru- ConflictofInterests
cial effect on SCO synthesis by oleaginous microorganisms
The authors declare that there is no conflict of interests
usingsugar-basedmediumsinceaerationpositivelyincrease
regardingthepublicationofthispaper.
consumption of fermentable sugars by lipid-producing
microorganisms resulting in increased yield of SCO pro-
ducedperunitofsugarconsumed[33].AsshowninFigure2 Acknowledgments
anincreaseinaerationratefrom200vvmto600rpmcaused
aquadraticincreaseinbiomassconcentration.Similartrend The authors wish to express their gratitude to Univer-
wasfoundforvariationsinagitationratessothatincreased siti Kebangsaan Malaysia for financing this research work
agitation intensities resulted in an increment in biomass throughGrant02-01-02-SF0138.