J Sci Food Agric 1998, 76, 129È134

Optimization of Enzymatic Degradation ofCoconut ResidueN K Rastogi,1* G Rajesh2 and T R Shamala31 Department of Food Engineering, Central Food Technological Research Institute, Mysore-570 013, India2 Department of Technology Transfer and Marketing, Central Food Technological Research Institute,Mysore 570 013, India3 Department of Microbiology, Central Food Technological Research Institute, Mysore 570 013, India

(Received 9 April 1996 ; revised version received 10 December 1996 ; accepted 6 May 1997)

Abstract : Enzymatic degradation of coconut residue has been optimised byresponse surface methodology to increase the yield of coconut milk. The study ofcomplex interaction among pH, enzyme concentration, temperature, water ratioand time and identiÐcation of the optimal combination have been carried outusing central composite rotatable design (CCRD) experiments. The result showedabout 93% degradation of degradable solids could be obtained at pH 4É12,enzyme concentration 4É87%, temperature 65¡C, water ratio 1É68 and time9É98 h. Experimental results are in good agreement with the predicted values.

1998 SCI.(

J Sci Food Agric 76, 129È134 (1998)

Key words : enzymatic degradation ; coconut ; response surface methodology ;optimisation ; CCRD

INTRODUCTION

Coconut milk is an important dietary ingredient incoconut-producing countries. It is a white milkyproduct extracted from endosperm of coconut. It isfurther converted into various products such as dehy-drated coconut milk (Rastogi and Raghavarao 1994),dehydrated skim coconut milk (Hagenmaier et al 1974 ;Hagenmaier 1977), canned coconut cream (Timminsand Kramer 1977) etc. The residue obtained afterextraction of coconut milk contains protein, fat, carbo-hydrate, mineral and lignin. Enzymatic degradation ofthe residue can increase the total yield of coconut milk.Niwa et al (1965), Chandrashekaran and King (1967)and Rama Rao (1969) reported that cellulase and hemi-cellulase can be used for the degradation of coconutresidue. Protein, fat and carbohydrate constitute about70% of the weight of the residue and it is easy todegrade them with a multienzyme mixture. As the plantcell biomass contains lignin, pectin, starch and proteinsin addition to cellulose and hemicellulose, Beldman et al(1984) recommended the use of a multienzyme insteadof a single enzyme system for such hydrolysis. The

* To whom correspondence should be addressed.

optimum extraction of oil and protein from coconutresidue depends on e†ective splitting of the complexproteinÈpolysaccharideÈlipid, which are referred to asdegradable solids. The rest of the solids (hemicellulose,mineral, Ðbre and lignin D 30%) are considered non-degradable. The cellulose and starch after degradationyield glucose which would improve the sweetness ofcoconut milk.

Enzymatic degradation of the coconut residue wasfound to be inÑuenced by pH, enzyme concentration,water to coconut residue ratio, temperature and time.Optimisation could be used to maximise the degrada-tion of degradable solids.

Response surface methodology (RSM) is an e†ectivetool for optimisation wherein many factors and theirinteractions a†ect the desired response (Hunter 1959).RSM uses an experimental design such as central com-posite rotatable design (CCRD) to Ðt a model by a leastsquares technique. Adequacy of the model is assessed byan analysis of variance (ANOVA). Three-dimensionalplots can be usefully employed to visualise the overalle†ect of di†erent variables on the response surfaces andpermit optimum conditions to be obtained.

The purpose of the present work is to optimise and tostudy the e†ect of pH, enzyme concentration, water

1291998 SCI. J Sci Food Agric 0022-5142/98/$17.50. Printed in Great Britain(

130 N K Rastogi, G Rajesh, T R Shamala

Fig 1. Flow diagram for estimation of degradable solids.

ratio, temperature and time on the enzymatic degrada-tion of coconut residue to release more reducing sugars,fat and protein.

MATERIALS AND METHODS

Materials

Coconut (Cocos nucifera) was procured from a localmarket. The coconut kernel contained (%, w/w) mois-ture 53, fat 32, Ðbre 1É2, protein 3É5, mineral 0É8 andcarbohydrate 9É13.

Liquid enzyme (Trizyme P50, Triton Chemicals,Mysore India) contained pectinases 8000 U ml~1, cellu-lases 575 U ml~1, a-amylases 4650 U ml~1, amyloglu-cosidase 1095 U ml~1 protease, 800 U ml~1 andhemicellulases 3400 U ml~1.

Estimations

With reference to Fig 1, Coconut kernel after removalof paring was passed through a KraussÈMaffie cutter(sieve size 3 mm) and coconut milk was squeezedthrough a muslin cloth from the grated meat. For each

experiment 10 g of fresh coconut grating was taken andsubjected to the treatments given in Table 2 (see below).The pH of the samples was adjusted with 1 M NaOHand 1 M HCl. Samples were incubated in a temperature-controlled water bath and stirred periodically duringthe experiment. After each treatment, the mixture wasagain Ðltered, through muslin cloth to obtain undi-gested residue. The residual cake was dried at 70¡C toconstant weight. The Ðnal weight was recorded. From10 g of fresh coconut grating, 5É106 g of residue wasobtained which contained 3É754 g of degradable solids(70%) and 1É532 g of non-degradable solids (30%).Hence, degradable solids were estimated as total solidsless non-degradable solids. Each experiment was repeat-ed three times and the average value was recorded.

Residual Ðbre and fat were estimated by the AOACmethod (1985) and protein by the Lowry method(Lowry et al 1951).

Experimental design

A CCRD (Cochran and Cox 1957) with Ðve variableswas used to study the response pattern and determinethe optimum combination of variables for maximisationof the degradation of coconut residue solids. The vari-ables optimised were pH (4È9), enzyme concentration(0È5É0%), water-to-residue ratio (0È2), temperature (25È65¡C) and time (2È10 h), each at Ðve levels : [2, [1, 0,1, 2. Table 1 shows the symbols and levels.

The central composite rotatable design shown inTable 2, was arranged to allow for Ðtting a second-order model (Hunter 1959). The CCRD combines theverticies of the hypercube whose coordinates are givenby 2n factorial design (treatments 1È16) with star points(treatments 17È26). The star points were added to thefactorial design to provide for the estimation of curva-ture of the model (Joglekar and May 1987). Six repli-cates (treatments 27È32) at the centre of the design wereused to allow for the estimation of the pure error sum ofsquares. Experiments were randomised in order to max-imise the e†ects of unexplained variability in theobserved responses due to extraneous factors.

TABLE 1Variables and their levels for CCRD

V ariables Symbolsa Coded variable levels

[2 [1 0 1 2

pH X1 4É00 5É25 6É5 7É75 9É00Enz conc (EC) (%) X2 0 1É25 2É5 3É75 5É00Temperature (T ) (¡C) X3 25 35 45 55 65Water ratio (WR) X4 0 0É5 1 1É5 2É0Time (t) (h) X5 2 4 6 8 10

a X1\ (pH-6É5)/1É25 ; X2 \ (EC-2É5)/1É25 ; X3 \ (T-45)/10 ; X4\ (WR-1)/0É5 ; X5\(t-6)/2.

Optimisation of enzymatic degradation of coconut residue 131

TABLE 2Treatment schedule for a Ðve-factor CCRD and the response in terms of percentage degradable solids present in coconut residue

T reatment No pH (X1) Enz conc (X2) T emp (X3) W ater ratio (X4) T ime (X5) % Degradation

Observed Predicted Residual

1 [1 [1 [1 [1 1 38É040 40É041 [2É0012 1 [1 [1 [1 [1 40É890 40É041 0É8493 [1 1 [1 [1 [1 44É730 45É710 [0É9804 1 1 [1 [1 1 40É400 45É250 [4É8505 [1 [1 1 [1 [1 47É430 43É764 3É6666 1 [1 1 [1 1 39É600 43É764 [4É1647 [1 1 1 [1 1 49É330 49É828 [0É4988 1 1 1 [1 [1 52É720 49É828 2É8929 [1 [1 [1 1 [1 41É850 40É041 1É809

10 1 [1 [1 1 1 37É480 40É041 [2É56111 [1 1 [1 1 1 51É097 46É105 4É99212 1 1 [1 1 [1 44É460 46É105 [1É64513 [1 [1 1 1 1 44É280 43É764 0É51614 1 [1 1 1 [1 43É540 43É764 [0É22415 [1 1 1 1 [1 48É890 49É828 [0É93816 1 1 1 1 1 52É590 49É828 2É76217 [2 0 0 0 0 49É920 48É756 1É16418 2 0 0 0 0 48É000 48É756 [0É75619 0 [2 0 0 0 43É730 42É878 0É85220 0 2 0 0 0 54É560 55É005 [0É44521 0 0 [2 0 0 39É620 37É004 2É61622 0 0 2 0 0 42É240 44É449 [2É20923 0 0 0 [2 0 28É850 30É621 [1É77124 0 0 0 2 0 32É800 30É621 2É17925 0 0 0 0 [2 42É810 46É818 [4É00826 0 0 0 0 2 51É233 46É818 4É41527 0 0 0 0 0 35É640 36É125 [0É48428 0 0 0 0 0 33É500 36É125 [2É62429 0 0 0 0 0 38É800 36É125 2É67530 0 0 0 0 0 34É100 36É125 [2É02431 0 0 0 0 0 34É500 36É125 [1É62432 0 0 0 0 0 39É800 36É125 3É675

Statistical analysis

A second order quadratic polynomial equation wasused to Ðt the experimental data given in Table 2. Themodel proposed for the response (Y ) was

Y \ a0] ;i/1

5aiX

i] ;

i/1

5aiiX

ii2

] ;i/1

4;

j/i`1

5aijX

iX

j

where Y is the predicted response, is the value of thea0Ðtted response at the centre point of the design, a

i, a

iiand are the linear, quadratic and cross producta

ijregression terms, respectively. Optimisation of the Ðttedpolynomial was determined using a simplex opti-misation technique contained in the program given byNelder and Mead (1965). After collecting the experimen-tal data and determining the optimum conditions usingRSM, the optimum response was veriÐed by experi-

ments under optimum conditions. Responses were mon-itored and results were compared with modelpredictions.

To visualise the relation between the response andthe experimental level of each factor, the Ðtted poly-nomial equation was expressed using the Surfer AccessSystem (Golden Software Inc. Golden, CO, USA) soft-ware on an IBM PC/AT.

RESULTS AND DISCUSSION

Diagnostic checking of Ðtted model

The percentage degradation under di†erent com-binations as deÐned in the design (Tables 1 and 2) wasanalysed using the ANOVA appropriate to the CCRD.The ANOVA for the data obtained using CCRD is pre-sented in Table 3. It is evident from the data presentedthat Ðrst-order and second-order (quadratic and cross-

132 N K Rastogi, G Rajesh, T R Shamala

TABLE 3Analysis of variance (ANOVA) for the Ðtted second order polynomial model and lack of Ðt for the percent-

age degradation of coconut residues as per CCRD

Source of variation df Sum of squares Mean sum of squares F-value

RegressionFirst-order terms 5 333É056 66É6112 5É327 686aSecond-order terms 15 914É117 60É9411 4É874 180aTotal 20 1247É173 62É358 65

ResidualLack of Ðt 6 62É5142 10É419 033 1É466 317Pure error 5 35É5279 7É105 580Total error 11 98É0421 8É912 918

Grand total 31 1345É2150CoefÐcient of determination (R2) 0É925

a SigniÐcant at 5% level.

product) terms were found to be signiÐcant and lack ofÐt was not signiÐcant. The lack of Ðt measures thefailure of the model to represent data in the experimen-tal domain at points which are not included in theregression. The coefficients along with their respectivedegrees of freedom, standard errors and t-values for theactual functional relationship for predicting the percent-age degradation of degradable solid in case of coconutresidue are presented in Table 4. SigniÐcant terms onlywere included in the model.

The predicted values are presented in Table 1 alongwith the observed values and residuals. The high valueof coefficient of determination (R2) suggests that themodel is a good Ðt. The R2 is proportion of variabilityin the response values explained or accounted for by themodel (Montgomery 1984). The goodness of Ðt is alsoreÑected by the closeness of predicted and observedvalues. The plots of residuals vs Ðts (Fig 2(a)) and fre-quency vs residual (Fig. 2(b)) indicate that the residualsare normally distributed.

Optimisation

Using the model, the combination of variables formaximum degradation of degradable solids was predict-ed by the Ñexible polyhedron simplex search method ofNelder and Mead (1965), as indicated earlier. Theoptimum feasible combination for maximum degrada-tion of degradable solids is presented in Table 5. A veri-Ðcation experiment at optimum condition consisting ofsix runs was performed using 100 g of fresh coconutgrating in each run. By using a hypothesis testing tech-nique (Montgomery 1984), this di†erence was shown tobe non-signiÐcant at the 5% level. Comparing thevalues of the response obtained in the veriÐcation

experiment to the one estimated by the model indicatedthe model was highly signiÐcant.

It was experimentally observed that at optimum con-ditions, the multienzyme system can release about 80%fat and 90% protein and degrade 95% Ðbre present inthe residue.

TABLE 4Estimated coefficients of the Ðtted second order polynomialrepresenting the relationship between the response and

process variables

CoefÐcient df Estimated Standard t-valuecoefÐcient error

a0 1 36É1245 1É1907 30É3388aa1 1 [0É7419 0É6094 [1É2174a2 1 3É0319 0É6094 4É9752aa3 1 1É8613 0É6094 3É0543aa4 1 0É7894 0É6094 1É2953a5 1 0É2147 0É6094 0É3523a11 1 3É1579 0É5512 5É7291aa22 1 3É2042 0É5512 5É8131aa33 1 1É1504 0É5512 2É0871a44 1 [1É3758 0É5512 [2É4961aa55 1 2É6733 0É5512 4É8498aa12 1 0É3883 0É7463 0É5202a13 1 0É6879 0É7463 0É9217a14 1 [0É1329 0É7463 [0É1781a15 1 [0.7116 0É7463 [0.9535a23 1 0É3908 0É7463 0É5236a24 1 0É5416 0É7463 0É7257a25 1 1É0579 0É7463 1É4174a34 1 [0É6629 0É7463 [0É8882a35 1 [0É1167 0É7463 [0É1563a45 1 1É5691 0É7463 2É1024

a SigniÐcant at 5% level.

Optimisation of enzymatic degradation of coconut residue 133

Fig 2. Plot of (a) residuals vs Ðts, (b) frequency vs residuals.

Response surface plotting

The response surfaces in Fig. 3 (a)È(c) are based on theabove model (after deleting the non-signiÐcant terms)with three variables kept constant at the optimum leveland varying the other two within the experimentalrange. In general, exploration of the response surfacesindicated a complex interaction between the variables.

TABLE 5Feasible optimum conditions for maximum percentage degra-

dation of coconut residues

pH 4É12Enz conc (EC) (%) 4É87Temp (T) (¡C) 65É00Water ratio (WR) 1É68Time (t) (h) 9É98Percentage degradation (Predicted) 91É95Percentage degradation (Experimental) 93É00

Fig 3. Response surfaces showing percentage degradation ofcoconut Ðbre with pH, enzyme concentration, temperature,water ratio and time : (a) enzyme concentration, water ratioand time kept at optimum condition ; (b) pH, temperature andtime kept at optimum condition ; (c) enzyme concentration,

water ratio and time kept at optimum condition.

At the optimum levels of temperature, enzyme con-centration and time (Fig 3(a)), maximum degradationwas observed at the lowest level of pH and at 1É68 levelof water ratio. At the lowest level of water ratio, the

134 N K Rastogi, G Rajesh, T R Shamala

response decreased in the lower range of pH. Beyondthe lower range a gradual increase was observed. At thehighest level of water ratio, the response followed thesame pattern. This may be because cellulase, hemi-cellulase, a-amylase and amyloglycosidase are active atlower pH range (about 4È6) and proteases are active inthe higher pH range (about 7È9). At the highest level ofpH, the degradation was found to increase with increas-ing water ratio and at the lower levels of pH the degra-dation was found to increase up to 1É68 of the level ofwater ratio. Beyond 1É68 a slight decrease was observed.

At the optimum levels of pH, temperature and time(Fig 3(b)), the maximum response was noted at highestlevel of enzyme concentration and water ratio level at1É68. At the lowest level of enzyme concentration, theresponse increased with an increase in water ratio,whereas at the highest level of enzyme concentration,the response increased with water ratio. At a later stageit decreased with water ratio within the experimentalrange.

At the optimum levels of enzyme concentration,water ratio and time (Fig 3(c)), the maximum responsewas observed at highest level of temperature and lowestlevel of pH, because high temperature and low pHappear to be favourable for degradation. At the lowestlevel of pH, the response decreased in lower range oftemperature and beyond which a relative increase wasobserved. At the highest level of pH, the response wasfound to trace a similar pattern for temperature. It canbe inferred from Table 5 that the experimental resultsare in good agreement with those predicted at optimumlevel.

CONCLUSION

It can be concluded that enzymatic degradation is acomplex function of pH, enzyme concentration, tem-perature, water ratio and time. Lower pH and highertemperature are found to be favourable for maximumdegradation of residue. The response surface method-ology provides an insight into the interaction and iden-tiÐes the optimum combination of variables (within thespeciÐed range) for maximum degradation of coconutresidue with the help of a relatively small number ofexperiments. The Ðve variable and Ðve level experimen-tal design amounts to (5 ] 5 ] 5 ] 5 ] 5) 3125 com-

binations, but only 32 combinations were sufficient topredict the optimum conditions using the RSM tech-nique, thus reducing the time and cost of study.

ACKNOWLEDGEMENTS

The authors thank Mr I J Regis for helping us in carry-ing out experiments. Thank also to Dr K S M S Ragha-varao for his helpful discussions and to thestatisticianÈreferee for his constructive criticism.

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