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Research ArticleReceived: 18 May 2012 Revised: 23 August 2012 Accepted article published: 24 October 2012 Published online in Wiley Online Library:

(wileyonlinelibrary.com) DOI 10.1002/jsfa.5920

Screening of agro-industrial wastes for citricacid bioproduction by Aspergillus niger NRRL2001 through solid state fermentationGurpreet S Dhillon,a Satinder K Brar,a Surinder Kaura,b and MausamVermac

Abstract

BACKGROUND: The citric acid (CA) industry is currently struggling to develop a sustainable and economical process owingto high substrate and energy costs. Increasing interest in the replacement of costly synthetic substrates by renewable wastebiomass has fostered research on agro-industrial wastes and screening of raw materials for economical CA production.The food-processing industry generates substantial quantities of waste biomass that could be used as a valuable low-costfermentation substrate. The present study evaluated the potential of different agro-industrial wastes, namely apple pomace(AP), brewers spent grain, citrus waste and sphagnum peat moss, as substrates for solid state CA production using Aspergillusniger NRRL 2001.

RESULTS: Among the four substrates, AP resulted in highest CA production of 61.06 1.9 g kg1 dry substrate (DS) after a72 h incubation period. Based on the screening studies, AP was selected for optimisation studies through response surfacemethodology (RSM). Maximum CA production of 312.32 g kg1 DS was achieved at 75% (v/w) moisture and 3% (v/w) methanolafter a 144 h incubation period. The validation of RSM-optimised parameters in plastic trays resulted in maximum CA productionof 364.4 4.50 g kg1 DS after a 120 h incubation period.CONCLUSION: The study demonstrated the potential of AP as a cheap substrate for higher CA production. This study contributesto knowledge about the future application of carbon rich agro-industrial wastes for their value addition to CA. It also offerseconomic and environmental benefits over traditional ways used to dispose off agro-industrial wastes.c 2012 Society of Chemical Industry

Keywords: agro-industrial wastes; Aspergillus niger; citric acid; inducer; response surface methodology; solid state tray fermentation

INTRODUCTIONCitric acid (CA) fermentation by Aspergillus niger is one of theworlds largest industrial microbial processes. CA, being non-toxic, biodegradable and generally recognised as safe (GRAS), isextensively used in the food, pharmaceutical, biomedicine, biore-mediation and agricultural industries, among others.1,2 Recently,an array of advanced applications has come to light, such as inhealth-related consumer products, in biomedicine for synthesis-ing biopolymers for drug delivery and for culturing a wide varietyof human cell lines, and in nanotechnology for bioremediation ofheavy metals from soils and metal ore mines and for making water-

based wood preservatives.1,35 Approximately, 70% of total CAproduction is used in the food and beverage industry, 12% in phar-maceuticals and the remaining 18% in other industries.1 The CAmarket has been under tremendous pressure in the past few years,as continually decreasing CA prices over the last few decades havesqueezed this profitable market. High energy and raw materialcosts have led to CA production becoming almost unprofitable.However, the size of the CA market is huge and continues toexpand owing to an increase in applications, which mandatesfinding pragmatic methods to meet the increasing needs.

On an industrial scale, CA is produced solely by A. niger insubmerged fermentation (SmF) using beet or cane molasses,sucrose or glucose syrup. A high initial sugar concentration isrequired and maximum production rates are usually achieved atinitial sugar concentrations of 140220 kg m3.6 The yield ofCA from Aspergillus strains often exceeds 70% of the theoreticalyield on the synthetic carbon source.7 The effect of carbohydrateconcentration on CA yield in SmF has been studied.8 The sugarsmaltose, sucrose, glucose, mannose and fructose were utilisedand highest yields were observed at a sugar concentration of100 kg m3, except for glucose, where 75 kg m3 gave the bestresults. In general, the carbon source is required in relatively

Corresponding author Universite du Quebec, 490 Rue de la Couronne, Quebec,G1K 9A9, Canada. E-mail: [email protected]

a INRS-ETE, Universite du Quebec, 490 Rue de la Couronne, Quebec, G1K 9A9,Canada

b DepartmentofMycologyandPlantPathology, InstituteofAgriculturalSciences,Banaras Hindu University (BHU), Varanasi, 221005, India

c Institut de Recherche et de Developpement en Agroenvironnement Inc. (IRDA),2700 Rue Einstein, Quebec, G1P 3W8, Canada

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www.soci.org GS Dhillon et al.

higher concentration compared with other medium componentsand thus contributes a higher share of the raw material cost.

With a view to feasible and sustainable production of CA,there is an increasing trend towards the utilisation of less costlysubstrates to reduce material costs. In this context, agro-industrialwastes and their by-products represent ideal substrates for thebioproduction of CA. This also offers a lucrative alternative forthe fruit-processing industry, which incurs losses due to thetreatment and transportation costs of dumping wastes in landfillsites. Moreover, it eliminates the environmental risks associatedwith their disposal.

In the context of economical and sustainable CA bioproduction,the exploitation of cheap renewable carbon sources, such as agro-industrial wastes and their by-products have been gaining muchattention in recent years. Various industrial wastes, such as fruitpomace wastes, sugar industry wastes and starchy substrates,among others, have been utilised as raw materials for CA

production.1,915 One example is brewers spent grain, a wasteproduct resulting from barley malting and wort separation in thebrewing industry. Being rich in carbon and other vital nutrientsrequired for culturing fungi, such industrial wastes represent cheapsubstrates for CA fermentation. With recent biotechnologicalinnovations, mainly in the area of fermentation technology, manynew avenues have opened for their utilisation.

In Canada, waste residues and by-products derived from fruitprocessing and beer production represent abundant sources ofless expensive organic matter. Some of these were utilised in thepresent study to investigate their suitability for CA bioproduction.The study was conducted with the following objectives: (a)screening of four agro-industrial wastes, apple pomace (AP), citruswaste (CW), brewers spent grain (BSG) and sphagnum peat moss(SPM), to evaluate their potential for CA bioproduction throughsolid state fermentation (SSF) (also known as koji fermentation)by A. niger NRRL 2001; (b) optimisation of initial moisture leveland inducer (methanol, MeOH) concentration for higher CAbioproduction using the substrate with highest potential for CAproduction based on screening studies through response surfacemethodology (RSM); (c) scale-up of koji fermentation in plastictrays with RSM-optimised parameters.

MATERIALS AND METHODSMicroorganism, inoculum preparation and chemicalsAspergillus niger NRRL 2001 from the Agricultural Research Service(ARS) (Peoria, IL, USA) culture collection was used for solid state CAfermentation. The culture conditions, maintenance and inoculumpreparation have been described previously.14 All chemicals usedin the experiments were purchased from Fisher Scientific Company(Whitby, ON, Canada) or Sigma-Aldrich Canada Ltd (Oakville, ON,Canada) and were of analytical grade.

Solid substrate procurement and pretreatmentSSF was carried out using AP (A. Lassonde Inc., Rougemont, QC,Canada), BSG, CW and SPM as solid substrates to evaluate theirsuitability for the production of CA. BSG of barley (Hordeumvulgare)variety 2 Tau Newdale was collected from a small-scale brewery(La Barberie, Quebec, QC, Canada) after malting (solid/water ratio1:3, 65 1 C, 2 h) and wort separation.

AP and CW were dried at 45 1 C in a hot air oven to constantweight, grounded and passed through sieves to get the desiredparticle size of 1.72 mm for use in the study. The moisturecontent of substrates was determined with a moisture analyser

(HR-83 Halogen, Mettler Toledo, Greifensee, Switzerland). BSG wasused as such without drying. SPM supplemented with a nutrientsolution of 250 g glucose, 15.4 g (NH4)2SO4, 43.9 g KH2PO4 and4 g NaCl kg1 dry peat moss was used as a solid support andas control. The most suitable solid substrate was used for furtherparameter optimisation through RSM for hyperproduction of CA.

The proximate composition of the solid substrates is givenin Table 1. AP was already supplemented with 10 kg m3 ricehusk, this being general practice to improve processing duringjuice extraction in the apple industry. Variation in the proximatecomposition of BSG collected from different breweries can beexpected owing to differences in barley cultivars and maltingpractices. The substrates were stored at 20 1 C for a maximumof 4 weeks to avoid any microbial contamination.

CA fermentation in flasks and traysFermentation was carried out by placing 40 g of substrate in 500mL Erlenmeyer flasks and autoclaving at 121 1 C for 30 min.The initial moisture level of 70% (v/w) was maintained by theaddition of distilled water during screening studies. The pH ofthe substrates was taken as such without any adjustment. Aftersterilisation of the medium, filter-sterilised inducer was addedas required, then the flask contents were inoculated with sporesuspension (1 107 spores g1 substrate) and incubated in anenvironmental chamber (Percival Scientific, Perry, IA, USA) undercontrolled relative humidity (RH) conditions at 30 1 C for 7 days.

Taking into account RSM-optimised parameters, koji fermenta-tion was carried out in 5 L capacity plastic tray bioreactors withapproximate dimensions of 40 cm (length) 25 cm (breadth) 12 cm (height). About 400 g of AP with optimal parameters of75% (v/w) initial moisture level and 1.72 mm particle size wassterilised and transferred to the sterilised tray under aseptic condi-tions. Fermentation was carried out in an environmental chambermaintained at 75% RH for 7 days.

Experimental design and optimisation through RSMIn the RSM, the initial moisture level of AP (X1) and theconcentration of inducer (methanol, MeOH) (X2) were consideredas independent variables and CA bioproduction as the dependentvariable. The influence of the independent variables on CAproduction by A. niger NRRL 2001 was tested by performinga 22 statistical experimental design using RSM. A set of 13experiments, comprising five centre points (0, 0, 0), four staraxial points ( = 1.414) and four points corresponding to a matrixof 22 incorporating four experiments including three variables (+1,1, 0), was carried out. Each variable was studied at two differentlevels (1, +1) and the centre point (0), which is the midpoint ofeach factor range. The distance of the axial points was 1.414,calculated from the equation

= (2n)1/4 (1)

where is the distance of the axial points and n is the number ofindependent variables. The ranges of the variables investigatedand the full experimental plan with respect to their actual andcoded values are given in Tables 2 and 3 respectively. Multipleregression analysis of the data was carried out by STATISTICA6 (StatSoft Inc., Tulsa, OK, USA) using RSM and a second-orderpolynomial equation that defines predicted responses (Yi) interms of the independent variables (X1 and X2):

Yi = b0i + b1iX1 + b2iX2 + b11iX1 + b22iX2 + b12iX1X2 (2)

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Table 1. Proximate composition of different solid substrates

Composition (dry weight basis)

Constituent APa BSGa,b CWc SPMd

Moisture (% wetbasis)

70.5 0.7 65.4 1.2a 77.9 1.1

Total carbon (gkg1)

127.9 106.9 4.8a 53.1

Protein (g kg1) 2957 239 36a 122 7 Lipid (g kg1) 79 14a Cellulose (g kg1) 72 03 138 24a 88 7 Hemicellulose (g

kg1) 30 4.2a 44 5

Lignin (g kg1) 23.5 2.1 12.4 1.3a 3.7 0.1 Total

carbohydrate(g kg1)

480620

Fibre (%) 4.751.1

Ash (%) 0.56.1 2.6 0.3a 5.9 0.3 2.8Reducing sugars

(RS) (g kg1)108150 -

Glucose (% of RS) 22.7 21.5b 4.60.1 Fructose (% of RS) 23.6 9.80.2 Sucrose (% of RS) 1.8 3.90.2 Xylose (% of RS) 0.1 17.6b 1.00.1 Galactose (% of

RS) 1.3b 1.40.1

Arabinose (% ofRS)

8.5b

Minerals

Phosphorus (%) 0.070.076 0.01

Potassium (%) 0.41.0 0.15

Calcium (%) 0.060.1

Sodium (%) 0.2

Magnesium (%) 0.020.36

Copper (mg kg1) 1.1 Zinc (mg kg1) 15.0 Manganese (mg

kg1)3.969.0

Iron (mg kg1) 31.838.3

Data are mean standard deviation (n = 3). Source of data:a this studyb Ref 28.c Ref. 29.d Ref. 25.AP, apple pomace; BSG, brewers spent grain; CW, citrus waste; SPM,sphagnum peat moss.

where Yi is the predicted response, b0i is the intercept term, b1i andb2i are linear coefficients, b11i and b22i are squared coefficients andb12i is the interaction coefficient. The response (CA production)is a function of the level of factors. The response surface graphsindicate the effects of variables individually and in combination todetermine their optimal levels for maximum CA production.

The data fitted well to the second-order polynomial function(Eqn (2)) and a correlation was drawn between experimental andmodel-predicted data (Figs 1A and 1B). The quality of the modelfit was evaluated through the coefficient R2, and its statisticalsignificance was determined by an F test. R2 represents theproportion of variation in the response data that can be explainedby the fitted model. High R2 was considered as evidence for theapplicability of the model in the range of variables included. R2

Table 2. Experimental range of two variables studied using centralcomposite design in terms of actual and coded factors

Coded levels

Variable Symbol

1.414()

Low

(1)Mid

(0)

High

(+1)

+1.414

(+)

Moisture (% v/w) X 1 67.92 70 75 80 82.07

Methanol (% v/w) X 2 1.59 2 3 4 4.41

(A)

(B)

Figure 1. Parity plots showing distribution of experimentalversuspredictedvalues of (A) citric acid production and (B) Aspergillus niger NRRL2001 viability obtained by varying moisture level (X1) and methanolconcentration (X2).

values range from 0 to 1, with values higher than 0.75 indicatinghigh consistency between predicted and observed values. The R2

value also provides a measure of the fraction of total variability inthe data explained by the regression.

CA extraction and analysisSamples were withdrawn in triplicate every 24 h after mixingthe fermentation medium. CA extraction was carried out byincubating the samples (solid/liquid ratio 1:15) in a wrist actionshaker (Burrell Scientific, Pittsburgh, PA, USA) for 20 min. Afterincubation the samples were centrifuged (Sorvall RC 5C Plus, Equi-Lab Inc., Quebec, QC, Canada) at 9000 g for 20 min and thesupernatants were retained for CA estimation.15

Viability assayA viability assay was performed as an indicator/measure of theamount of living fungal biomass in SSF. Fungal mycelium viability

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Table 3. Matrix of experimental 22 factorial design for optimisation and corresponding results for citric acid (CA) production and fungal viabilityafter 144 h of fermentation

CA concentration (g kg1 dry substrate)

Treatment X1 X2 Observed Predicted RD (%) Fungal viability (CFU g1)

Fractional 22 factorial design

1 1 1 264.52 259.83 1.80 7.81E+062 1 +1 189.51 197.74 +4.16 3.14E+063 +1 1 259.67 264.95 +1.99 8.56E+064 +1 +1 187.82 205.28 +8.86 5.25E+07

Star axial points

5 0 252.72 253.01 +0.12 4.13E+066 + 0 275.76 261.96 5.27 5.25E+067 0 247.08 249.46 +0.95 2.24E+078 0 + 179.25 163.36 9.73 6.52E+07Central points

9 (C) 0 0 311.45 310.11 0.71 1.87E+0610 (C) 0 0 312.32 310.11 0.43 1.85E+0611(C) 0 0 309.34 310.11 +0.25 1.94E+06

12 (C) 0 0 306.98 310.11 +1.01 2.25E+06

13 (C) 0 0 310.44 310.11 0.11 1.94E+06X 1, moisture level (% v/w); X2, inducer (methanol) concentration (% v/w); RD, relative deviation.

was assayed using the most probable number (MPN) method.16

Briefly, 1 g of fungus-colonised fermented substrate was diluted inan appropriate amount of sterile distilled water and homogenisedin an Ultra-Turrax for 30 s. Further dilutions of these sampleswere made. In total, five dilutions per sample were used for theviability assay. Each dilution was aseptically poured onto an agarplate at six different spots (10 L per spot); for each sample, twoplates were prepared. The plates were incubated for 24 h and thegrowing spots were quantified. In calculating the results, the firstdilution where all pipetted spots did not grow, the last dilutionwhere at least one pipetted spot showed a positive result and allsamples in between were considered. The results were calculatedin colony-forming units (CFU) according to the equation

MPN = P/ (NnNk)1/2 (3)

where P is the number of positives in all counted series, Nn isthe amount of sample in the negative parallels (g) and Nk is theamount of sample in all counted series (g).

Statistical analysisAll CA analyses were performed in triplicate. Data were subjectedto analysis of variance (ANOVA) and Fishers least significantdifference test at P< 0.05 using Statgraphics Centurion XV 15.1.02(StatPoint Technologies, Inc., Warrenton, VA, USA) to determinethe significance of differences between substrates in their abilityto produce CA.

RESULTS AND DISCUSSIONCA production and fungal viability during screening studiesCA production and A. niger NRRL 2001 viability trends during thescreening of different agro-industrial wastes are shown in Figs 2Aand 2B respectively. CA concentrations of 61.06 1.9, 59.32 2.7, 11.8 0.7 and 24.6 1.3 g kg1 dry substrate (DS) wererecorded after a 72 h incubation period using AP, CW, BSG and

SPM respectively (Fig. 1A). The maximum viability observed for A.niger NRRL 2001 was 4.8E+06 CFU with AP as substrate, followedby viabilities of 1.64E+05, 3.4E+06 and 2.96E+06 CFU with BSG,CW and SPM, respectively as substrates (Fig. 1B).

Significant differences in CA production were observed betweenAP, BSG and CW (P < 0.05) (Fig. 2A). SPM can retain 1520 timesits own weight in water and has a low bulk density even when wet,allowing for high porosity and good air diffusion even under moistconditions. The variation in CA production with different substrateswas due to the difference in complexity of the substrates used forfermentation. As evident from Fig. 2B, fungal viability varied withthe cultivation medium. With AP the viability of A. niger NRRL 2001increased from the beginning of fermentation up to 144 h, mainlyowing to active fungal growth, and then decreased. Similarly, withBSG, CW and SPM, fungal viability increased up to 120 h and thenstarted to decrease. The viability assays correlated well with CAproduction during the course of fermentation.

Although, no significant difference in CA bioproduction wasfound between AP and CW (P < 0.05), AP was selected for furtheroptimisation studies of initial moisture level and inducer (MeOH)concentration using RSM for higher CA production byA.nigerNRRL2001, since AP is produced in large quantities in Canada. Owing tothe high total carbon content (128 g kg1) of AP, CA concentrationwas expected to increase with the optimised parameters andinducer supplementation.

CA bioproduction and fungal viability through RSMCA production exhibited different responses with varying initialmoisture level and MeOH concentration. The responses at differentcoded values of moisture level and MeOH concentration are shownin Table 3 for CA production and fungal growth after a 144 hfermentation period. The results of the second-order responsesurface model fitting are presented in ANOVA form in Table 4.Parity plots displaying the distribution of experimental versuspredicted values of CA bioproduction and A. niger NRRL 2001viability obtained by varying the moisture level (X1) and MeOH



0

10

20

30

40

50

60

70

0 24 48 72 96 120 144

CA c

onc.

(g kg

-1

DS)

Fermentation time (h)

APBSGCWSPM

(A)

0.0E+002.0E+044.0E+046.0E+048.0E+041.0E+051.2E+051.4E+051.6E+051.8E+052.0E+05

0.0E+00

1.0E+06

2.0E+06

3.0E+06

4.0E+06

5.0E+06

6.0E+06(B)

24 48 72 96 120 144 168 192

Viab

ility

(CFU

g-1 )

(BSG

)

Viab

ility

(C

FU g-

1 ) (A

P, C

W, S

PM)

Fermentation time (h)

APCWSPMBSG

Figure 2. Trends in (A) citric acid production and (B) Aspergillus niger NRRL2001 viability during screening of different agro-industrial wastes.

concentration (X2) are shown in Figs 1A and 1B respectively.Similarly, Figs 3A and 3B present the response surface profilesdepicting quantitative values for CA production and fungalviability respectively, with moisture level and MeOH concentrationrepresented as their coded values.

Statistical analysis was performed on the data with higher CAproduction at 144 h of fermentation, as no significant increase in CAproduction was observed after that time (P> 0.05). CA productionvaried from 179.25 g kg1 DS (trial 8 with 75% (v/w) moisture and4.41% (v/w) MeOH) to 312.32 g kg1 DS (trial 10 with 75% (v/w)moisture and 3% (v/v) MeOH). A decline in CA production wasobserved on increasing or decreasing the moisture level from 75%(v/w) or the MeOH concentration from 3% (v/w).

The data were best fitted by a second-order polynomial function(Eqn (1)), as can be inferred from the good agreement betweenthe experimental and model-predicted data (Table 3). A coefficientof determination higher than 0.75 (R2 = 0.969) indicated thatall models fitted the experimental results well, representingabout 97% variability in the response (Table 4). Numericaloptimisation was carried out to identify the optimal conditions forCA production by A. niger NRRL 2001. The following regressionequation was obtained after eliminating the non-significant termsat a confidence level of 95%:

Y (CA) = 310.1 60.9MeOH 52.60Mois 103.7MeOH (4)

Table 4. Model coefficients estimated by central composite designand best selected prediction models

Citric acid concentration

(g kg1 dry substrate)

Parameter Coefficient t value P value

Fungal viability

(CFU g1)

Constant 310.1 1.52 0.17 1.9E+06

Linear

X 1 6.3 2.86 0.024 5.9E+06

X 2 60.9 15.61 0.0001 3.2E+06Interaction

X 1 X2 1.2 3.51 0.01 1.02E+06Quadratic

X 1 52.60 1.42 0.2 3.1E+06X 2 103.7 19.22 0.0001 4.2E+06R 2 0.969 0.981

Significant (P < 0.05).

(A)

(B)

Figure 3. Response surface plots of (A) citric acid production and (B)Aspergillus niger NRRL 2001 viability obtained by varying moisture level(X1) and methanol concentration (X2).

The significance of each coefficient was evaluated via the Pvalues:17 the smaller the magnitude of P (P < 0.05), the moresignificant the corresponding coefficient. Based on the effectsof variables using Pareto charts (Figs 4A and 4B), both moistureand MeOH appear to be important parameters for CA productionand A. niger NRRL 2001 viability. The estimated coefficients andcorrespondingPvalues indicate that, among the test variables used

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(A)

(B)

Figure 4. Pareto charts showing effects of variables moisture level (X1) andmethanol concentration (X2) on responses (A) citric acid production and(B) Aspergillus niger NRRL 2001 viability in 22 factorial design.

in the present study, the variable X2 showed a significant negativeeffect (P< 0.0001), indicating that X2 can act as limiting factor andthat variation in its concentration will also have a negative effect onCA production. However, the variable X1 showed an insignificantpositive effect on CA production. The quadratic terms of both X1(moisture) and X2 (MeOH) showed a highly significant negativeeffect on CA production by A. niger NRRL 2001 (P < 0.0001). Theinteraction between X1 and X2 showed an insignificant positiveeffect on CA production (P < 0.01). It can be inferred that thevariables alone may have a significant positive effect on theresponse. Similarly, both X1 and X2 showed a significant positiveeffect on fungal viability (P < 0.05). The quadratic term of X1(moisture) showed an insignificant positive effect and that ofX2 (MeOH) a significant negative effect on A. niger NRRL 2001viability (P < 0.001). The interaction between X1 and X2 showed asignificant positive effect on fungal viability (P < 0.01).

A comparison of CA bioproduction by different Aspergillusstrains using various agro-industrial wastes is provided in Table 5.The present study demonstrated the potential of AP for highCA bioproduction of 312.32 g kg1 DS by A. niger NRRL 2001.Shojaosadati and Babaeipour13 reported a CA yield of 128 g kg1

dry AP at 78% moisture level in a packed bed bioreactor. In arecent study a low yield of CA of only 46 g kg1 AP was achieved.18

The moisture level in SSF affects the physical properties of thesolid particles and thus the final yield of CA. Higher moisturelevel results in clump formation and reduces the porosity of

the substrate, which hinders oxygen transfer, resulting in poorgrowth of the microorganism and hence a lower yield of CA.19

Low moisture level also results in low CA formation owing to adecrease in the diffusion of solutes, nutrients and gas to the micro-organism.20 The higher CA bioproduction obtained in the presentstudy can be attributed to the supplementation of AP with 10 kgm3 rice husk, the coarse particle nature of which helps to increasethe porosity of the substrate by preventing aggregation or clumpformation. Increased porosity will increase oxygen transfer andnutrient availability to the fungus. Moreover, the cellulose andhemicellulose present in rice husk and AP are known to induce

cellulase and hemicellulase production by the fungus,2023 whichin turn could help in efficient utilisation of the substrate.

MeOH is routinely employed as an inducer in the microbial

production of CA by strains of A. niger.1,4,12,2426 Such strains arenot able to use MeOH as a metabolic substrate. However, MeOHis known to depress the synthesis of cell wall proteins in the earlystages of cultivation.1 Moreover, MeOH also induces activity of theenzyme citrate synthase, which ultimately leads to higher accumu-lation of CA.1,4 However, it should be noted that MeOH at higherconcentrations also produces negative effects on fungal growth.

The growth of the fungus correlated well with the CA productiontrend (Table 3). Treatments resulting in higher CA production alsoexhibited excellent fungal growth compared with treatmentsresulting in lower CA production. However, in treatments in whichthe fungus was unable to grow, sporulation was observed owingto the adaptation of the fungus to adverse conditions. Alcoholsare also known to inhibit sporulation and increase fermentationefficiency.27 Thus the present study established the suppressiverole of MeOH with respect to spore formation.

CA bioproduction through koji fermentation in traysTaking into consideration the optimised parameters, kojifermentation was conducted in plastic trays. The best parameters,i.e. an initial moisture content of 75% (v/w) and an MeOHconcentration of 3% (v/w), resulted in a CA yield of 364.4 4.50 g kg1 DS, which was achieved with A. niger NRRL 2001 usingAP after a 120 h incubation period.

Conventional tray fermentation technology (koji fermentation)is attracting more interest and is often used for the laboratoryscale-up of processes for producing enzymes and carboxylic acids,among others.14,21,22 Koji fermentation offers potential benefitsover bioreactors; for example, it is a simple technique, trays canbe stacked on top of one another on shelves, and it produceshigher yields. The advantages of using static tray bioreactorsinclude high substrate loading, large area for fungi to grow andeasier handling at laboratory scale compared with flasks. Fromthe present study it can be concluded that tray fermentationscould possibly be used to achieve higher yields of CA. Moreover,utilisation of agricultural waste substrates would have positiverepercussions on the overall economy of CA production, whichcould eventually lead to stabilisation of CA prices.

CONCLUSIONThis study established the potential of apple pomace (AP) forhigher citric acid (CA) production by A. niger NRRL 2001. HighCA production of 364.4 4.50 g kg1 DS was achieved by kojifermentation with A. niger NRRL 2001 using AP after a 120 hincubation period. An initial moisture level of 75% (v/w) and anMeOH concentration of 3% (v/w) were found to be effective



Table 5. Comparison of citric acid (CA) production by different Aspergillus niger strains

A. niger strain Substrate

CA production

(g kg1 DS) Optimal conditionsIncubation

temperature/time Reference

NRRL 2001 Corn husk 259 10 70% moisture 30 C/120 h 30BC-1 AP 124 78% (w/w) moisture,

0.61.18 mm particle size30 C/5 days 13

NRRL 567 SPM + glucose 354 Maximum yield with 19 gphytate, 49 g olive oil and37 g MeOH kg1 DS

32 C and 12 days 25

Van Tieghem MTCC AP 46 4% (v/w) MeOH 30 C/5 days 18MTCC 282 Banana peel 180 70% moisture 28 C/72 h 10NRRL 328 Pineapple waste 54.8% moisture 28 C/144 h 11NRRL 2001 AP + rice husk (10 kg m3) 364.4 4.5 75% (v/w) moisture, 3%

(v/w) MeOH30 C/120 h This study

DS, dry substrate; AP, apple pomace; SPM, sphagnum peat moss; MeOH, methanol.

for achieving significantly higher concentrations of CA (P

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