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Hindawi Publishing CorporationBiotechnology Research InternationalVolume 2013 Article ID 137851 5 pageshttpdxdoiorg1011552013137851
Research ArticleStatistical Analysis of Metal Chelating Activity ofCentella asiatica and Erythroxylum cuneatum UsingResponse Surface Methodology
R J Mohd Salim12 M I Adenan12 A Amid3 M H Jauri1 and A S Sued1
1 Natural Product Division Drug Discovery Centre (DDC) Forest Research Institute Malaysia (FRIM) Jalan KepongSelangor Darul Ehsan 52109 Kepong Malaysia
2Malaysian Institute of Pharmaceuticals and Nutraceuticals Ministry of Science Technology and InnovationUSM 10 Persiaran Bukit Jambul 11900 Bukit Jambul Malaysia
3 Department of Biotechnology Engineering International Islamic University Malaysia GombakPO Box 10 50728 Kuala Lumpur Malaysia
Correspondence should be addressed to R J Mohd Salim roshanjahnfrimgovmy
Received 24 October 2012 Accepted 20 December 2012
Academic Editor Triantafyllos Roukas
Copyright copy 2013 R J Mohd Salim et al This is an open access article distributed under the Creative Commons AttributionLicense which permits unrestricted use distribution and reproduction in any medium provided the original work is properlycited
Thepurpose of the study is to evaluate the relationship between the extraction parameters and themetal chelating activity ofCentellaasiatica (CA) and Erythroxylum cuneatum (EC)The response surfacemethodology was used to optimize the extraction parametersof methanolic extract of CA and EC with respect to the metal chelating activity For CA Run 17 gave optimum chelating activitywith IC
50= 093mgmL at an extraction temperature of 25∘C speed of agitation at 200 rpm ratio of plant material to solvent at
1 g 45mL and extraction time at 15 hour As for EC Run 13 with 60∘C 200 rpm 1 g 35mL and 1 hour had metal chelating activityat IC50= 03817mgmL Both optimized extracts were further partitioned using a solvent system to evaluate the fraction responsible
for the chelating activity of the plants The hexane fraction of CA showed potential activity with chelating activity at IC50= 0090
and the ethyl acetate fraction of EC had IC50= 0120mgmL The study showed that the response surface methodology helped to
reduce the extraction time temperature and agitation and subsequently improve the chelating activity of the plants in comparisonto the conventional method
1 IntroductionThe knowledge and practice of traditional medicine areuniversal amongst the respected ethnic groups in each coun-try In Malaysia the benefits of herbal medicine are beingconveyed down from one generation to another Latif et al[1] state that there are four sources of traditional Malaysianmedicine namely Malay village medicine (including OrangAsli medicine) Chinese medicine (introduced from China)Indian medicine (introduced from India) and other formsof traditional medicine (including those introduced by theJavanese Sumatrans Arabs Persians Europeans etc)
Centella asiatica (CA) also locally known as pegaga isa crawling plant usually growing wildly in a humid climatearound the globe Its wide medicinal benefits include wound
Erythroxylum cuneatum forma cuneatum (Miq) Kurz(EC) is a genus of tropical flowering plants in the familyof Erythroxylaceae [7] While CAs are being well studiedfor their various medicinal fortunes Erythroxylum cuneatum(EC) on the other hand has a very limited report on itsmedicinal value In Terengganu the leaves are pounded andapplied on the forehead of women after miscarriage InBunguran Indonesia leaves are reported to be used in Sajur(vegetable soup) [8] It is used in Thai traditional medicinefor antifever purposes as well as an anti-inflammatory agent[9]
2 Biotechnology Research International
Table 1 Parameters to be optimized using response surfacemethod-ology for CA and EC
Neurodegenerative disease (ND) results from the deteri-oration of neurons which functionalize the intellectual andcognition ability of a human body [10] Zecca et al [11]reported that iron may engage in a mechanism involvingmany neurodegenerative disorders It was deduced that asthe brain ages iron accumulates in regions that are affectedby Alzheimer and Parkinson diseases diseases categorizedunder ND Thus it is the interest of the research to study theability of CA and EC to chelate the metal iron and furtheroptimize the extraction process of the plants with respect totheir chelating activity
The extraction of plant material for example bioactivecompounds can be affected by more than one factor suchas particle size extraction solvent temperature and time[12] Response surface methodology is a software tool usedto study the interaction that may occur between variablefactors [13] This statistical experimental design is a powerfultool that enables the extraction process conducted effectivelyby verifying the effects of operational factors and theirinteractions [14] The traditional empirical methods onlystudy a single factor at a time and fail to acknowledge theinteraction that they have between each other [15]
2 Materials and Methods
21 Materials Centella asiatica (CA) was purchased fromlocalmarket Pasar Borong Selayang Selangor and Erythrox-ylum cuneatum (EC) was collected from FRIMrsquos compoundMethanol was purchased from Fisher Scientific ethanol fromJ Kollin Corporation Germany and hexane ethyl acetateand n-butanol from Merck USA All chemicals and solventsused were of analytical grade Iron (II) sulfate heptahy-drate (FeSO
4) was a product of Aldrich USA 441015840-[3-(2-
pyridinyl)-124-triazine-56-diyl]bis also known as ferrozinefrom Aldrich USA
22 Methods
221 Response Surface Methodology (RSM) RSM was usedto optimize the conditions for extraction of CA and EC togive the optimum metal chelating activity A face-centeredcube design (FCD) in RSM consisting of 30 experimentalruns including six replications at the center point was chosen
to evaluate the combined effect of the independent variablesThree levels were adopted and coded to low center andhigh levels The experiments were performed in randomorder to minimize the effects of unexplained variabilityin the observed responses due to systematic errors [15]The independent variables were temperature (∘C) speed ofrotation (rpm) ratio of raw material to solvent (g mL)and time of extraction (h) while the response is the metalchelating activity reported in 1IC
50 As the software was
meant to display the response at maximum the inverse IC50
(1IC50) was reported in this study so that the IC
50will be
displayed at its optimum activityThe total of 30 runs designed by Design Expert by com-
bining the parameters for extraction was shown in Table 1The figures for each parameter were deduced from prelimi-nary experiment Each run was performed in triplicate
222 Extraction Process A constant weight of 2 g plants wasused for all the 30 runs while adjusting accordingly to theratio of methanol solvent that was needed in each run asoutlined byDesignExpert softwareTheplantswere extractedin incubator shaker according to the combination parametersas given by each run The extracts were then separated fromthe filtrate and the methanol solvent was removed usingrotary evaporator at 40∘C and at a reduced pressure Theextracts from each run were then subjected to the metalchelating activity
223 Metal Chelating Activity The assay was initiated byadding 250120583L of 25mM FeSO
4to 500120583L sample solutions
CA and EC crude extracts were prepared in a series ofconcentrations This mixture was vortexed briefly for 10seconds before adding 250120583L of 6mM ferrozineThemixturewas vortexed again briefly for 10 seconds and allowed toequilibrate for 10min at room temperature The absorbanceof the mixture (formation of the ferrous iron-ferrozinecomplex) was measured at 562 nm [16] Sample solutionswith appropriate dilutions were used as blanks The abilityof extracts to chelate ferrous ion was calculated relative tothe control (consisting of iron and ferrozine only) using thefollowing formula [17]
Chelating effect
=(Absorbance of control minus Absorbance of sample)
Absorbance of control
times 100
(1)
224 Partitioning Process The crude methanolic extractswere weighed to be 50 g and were suspended in water andthen subjected to liquid-liquid partition by adding hexaneethyl acetate and n-butanol successively The residual partthat was suspended in water which was the water residuefraction [18] and the hexane ethyl acetate and n-butanolfraction were collected and subjected tometal chelating assayas described above
Biotechnology Research International 3
Table 2 Face centered central composite design setting with theindependent variables and their responses in CA
31 Optimization of Extraction with respect to Metal ChelatingActivity The optimum 1IC
50value for CA (referred to in
Table 2) was 10753mgmL (IC50= 0093mgmL) obtained
in the combined interaction of the independent parameter atRun 17 with 25∘C 200 rpm 1 g 45mL ratio and for durationof 15 hour
Table 3 summarized the experimental results for ECThe optimum 1IC
50value of 26196mgmL (IC
50=
03817mgmL) was obtained in Run 13 with temperatureof 60∘C agitation at 200 rpm and ratio of raw material tosolvent 1 g 35mL ratio for extraction duration of 1 hour
32 Multiple Regression Analysis The statistical model wasdeveloped by applying multiple regression analysis methodson using the experimental data for themetal chelating activity
Table 3 Face centered central composite design setting with theindependent variables and their responses in EC
which is given in (2) for CA and in (3) for EC The responsefunction (119910) measured the 1IC
50value of themetal chelating
activity of the crude extracts CA and EC This value wasrelated to the variables (119860 119861 119862 119863) by a second-degreepolynomial using (2) and (3) which is displayed in termsof coded factors The coefficients of the polynomial wererepresented by a constant term 119860 119861 119862 and 119863 (lineareffects) 1198602 1198612 1198622 and 1198632 (quadratic effects) and 119860119861119860119862 119860119863 119861119862 119861119863 and 119862119863 (interaction effects) The analysisof variance (ANOVA) tables were generated and the effectand regression coefficients of individual linear quadraticand interaction terms were determined The significancesof all terms in the polynomial were judged statistically bycomputing the 119865-value at a probability (119875) of 0001 001 or005 In this case 119860 119861 1198602 1198612 1198622 119860119861 119860119862 119860119863 119861119862 119861119863and 119862119863 are significant model terms On the other handvalues greater than 01000 indicate that the model terms are
4 Biotechnology Research International
Table 4 Fit statistics for the response of 1IC50 value of CA
33 Fit Statistics for the Response Some characteristics of theconstructed model can be explained by details in Table 4 andTable 5 The statistical analysis indicates that the proposedmodel was adequate possessing no significant lack of fitand with satisfactory values of the 119877-squared The quality offit of the polynomial model equation was expressed by thecoefficient of determination (1198772 adjusted 1198772 and adequateprecision) 1198772 is a measure of the amount of variation aroundthe mean explained by the model and equal to 09569 (CA)and 09028 (EC) The closer the value of 119877-squared is tothe unity the better the empirical model fits the actual dataThe smaller the value of 119877-squared is the less relevant thedependent variables in themodel have to explain the behaviorvariation [18] and [19] The adjusted-1198772 is adjusted for thenumber of terms in the model It decreases as the numberof terms in the model increases if those additional terms donot add value to the model Adequate precision is a signal-to-noise ratio It compares the range of the predicted values
at the design points to the average prediction error Ratiosgreater than four indicate adequate model discriminationAs for CA it was 21064 whereas for EC it was 9404 Thestandard deviation of 066 (CA) and 026 (EC) indicatesthat the model designed was acceptable with a minimumdeviation Coefficient of variation (CV) is the standarddeviation expressed as a percentage of the mean which is2534 (CA) and 1612 (EC) CVdescribes the extent towhichthe data were dispersed The small values of CV give betterreproducibility In general a high CV indicates that variationin the mean value is high and does not satisfactorily developan adequate response model [20]
The predicted residual error sum of squares (PRESS) is ameasure of model fitness to each point in the design whichgave an amount of 4629 (CA) and 1612 (EC)
4 Conclusion
The metal chelating activity of CA and EC was optimizedusing statistical analysis to improve the chelating activity ofthe both plants by varying the parameters for the extractionIt shows that the extraction parameters had been optimized(IC50= 0093mgmL at extraction temperature of 25∘C
speed of agitation at 200 rpm ratio of plantmaterial to solventat 1 g 45mL and extraction time at 15 hour) As for EC Run13 with extraction temperature at 60∘C speed of agitationat 200 rpm ratio of plant material to solvent at 1 g 35mLand extraction time at 1 hour had metal chelating activity atIC50= 03817mgmL
Acknowledgment
The authors would like to thank FRIM and MOSTI forproviding fund for the research
References
[1] M Latiff ldquoGenetic resources of medicinal plants in Malaysiardquoin Genetic Resources of Under-Utilised Plants in MalaysiaProceedings of the NationalWorkshop on Plant Genetic ResourcesHeld in Subang Jaya Malaysia A H Zakri Ed MalaysianNational Committee on Plant Genetic Resources KualaLumpur Malaysia 1989
[2] M T Thomas R Kurup A J Johnson et al ldquoElite geno-typeschemotypes with high contents of madecassoside andasiaticoside from sixty accessions of Centella asiatica of southIndia and the Andaman Islands for cultivation and utility incosmetic and herbal drug applicationsrdquo Industrial Crops andProducts vol 32 no 3 pp 545ndash550 2010
[3] L Suguna P Sivakumar and G Chandrakasan ldquoEffects ofCentella asiatica extract on dermal wound healing in ratsrdquoIndian Journal of Experimental Biology vol 34 no 12 pp 1208ndash1211 1996
[4] T D Babu G Kuttan and J Padikkala ldquoCytotoxic and anti-tumour properties of certain taxa of Umbelliferae with specialreference to Centella asiatica (L) Urbanrdquo Journal of Ethnophar-macology vol 48 no 1 pp 53ndash57 1995
[5] S S Katare and M S Ganachari ldquoEffect of Centella asiatica onhypoxia induced convulsions and lithium-pilocarpine induced
Biotechnology Research International 5
status epilepticus and antilipid peroxidation activityrdquo IndianJournal of Pharmacology vol 33 article 128 2001
[6] G Jayashree G K Muraleedhara S Sudarslal and V B JacobldquoAnti-oxidant activity ofCentella asiatica on lymphoma-bearingmicerdquo Fitoterapia vol 74 no 5 pp 431ndash434 2003
[7] R J M Salim Optimisation of extraction of Centella asiaticaand Erythroxylum cuneatum and their evaluation as a neuropro-tective agent [Masterrsquos thesis] International Islamic UniversityMalaysia Selangor Malaysia 2010
[8] A S Sued Kesan ekstrak Centella asiatica Linnaeus dan Ery-throxylum cuneatumForma Cuneatum (Miquel) Kurz ke atassymptom tarikan pada tikus ketagihan morfina dan proteinserummereka [Masterrsquos thesis] Universiti KebangsaanMalaysiaSelangor Malaysia 2009
[9] T Kanchanapoom A Sirikatitham H Otsuka and S Ruchi-rawat ldquoCuneatoside a new megastigmane diglycoside fromErythroxylum cuneatum Blumerdquo Journal of Asian Natural Prod-ucts Research vol 8 no 8 pp 747ndash751 2006
[10] J F Emard J P Thouez and D Gauvreau ldquoNeurodecgnerativediseases and risk factors a literature reviewrdquo Social Science andMedicine vol 40 no 6 pp 847ndash858 1995
[11] L Zecca M B H Youdim P Riederer J R Connor and R RCrichton ldquoIron brain ageing and neurodegenerative disordersrdquoNature Reviews Neuroscience vol 5 no 11 pp 863ndash873 2004
[12] B Yang X Liu and Y Gao ldquoExtraction optimization ofbioactive compounds (crocin geniposide and total phenoliccompounds) from Gardenia (Gardenia jasminoides Ellis) fruitswith response surface methodologyrdquo Innovative Food Scienceand Emerging Technologies vol 10 no 4 pp 610ndash615 2009
[13] J H Kwon J M R Belanger and J R J Pare ldquoOptimization ofmicrowave assisted extraction (MAP) for ginseng componentsby response surface methodologyrdquo Journal of Agricultural andFood Chemistry vol 51 pp 1807ndash1810 2003
[14] W Huang Z Li H Niu D Li and J Zhang ldquoOptimization ofoperating parameters for supercritical carbon dioxide extrac-tion of lycopene by response surface methodologyrdquo Journal ofFood Engineering vol 89 no 3 pp 298ndash302 2008
[15] C S Ku and S PMun ldquoOptimization of the extraction of antho-cyanin from Bokbunja (Rubus coreanus Miq) marc producedduring traditional wine processing and characterization of theextractsrdquo Bioresource Technology vol 99 no 17 pp 8325ndash83302008
[16] T C P Dinis V M C Madeira and L M Almeida ldquoActionof phenolic derivatives (acetaminophen salicylate and 5-aminosalicylate) as inhibitors of membrane lipid peroxidationand as peroxyl radical scavengersrdquo Archives of Biochemistry andBiophysics vol 315 no 1 pp 161ndash169 1994
[17] YWu SW Cui J Tang andXGu ldquoOptimization of extractionprocess of crude polysaccharides from boat-fruited sterculiaseeds by response surface methodologyrdquo Food Chemistry vol105 no 4 pp 1599ndash1605 2007
[18] T Satake K Kamiya Y An T Oishi and J Yamamoto ldquoTheanti-thrombotic active constituents from Centella asiaticardquoBiological and Pharmaceutical Bulletin vol 30 no 5 pp 935ndash940 2007
[19] T M Little and F J Hills Agricultural Experimentation Designand Analysis John Wiley New York NY USA 1978
[20] W W Daniel Biostatistics A Foundation for Analysis in theHealth Sciences vol 503 Wiley New York NY USA 5thedition 1991
Neurodegenerative disease (ND) results from the deteri-oration of neurons which functionalize the intellectual andcognition ability of a human body [10] Zecca et al [11]reported that iron may engage in a mechanism involvingmany neurodegenerative disorders It was deduced that asthe brain ages iron accumulates in regions that are affectedby Alzheimer and Parkinson diseases diseases categorizedunder ND Thus it is the interest of the research to study theability of CA and EC to chelate the metal iron and furtheroptimize the extraction process of the plants with respect totheir chelating activity
The extraction of plant material for example bioactivecompounds can be affected by more than one factor suchas particle size extraction solvent temperature and time[12] Response surface methodology is a software tool usedto study the interaction that may occur between variablefactors [13] This statistical experimental design is a powerfultool that enables the extraction process conducted effectivelyby verifying the effects of operational factors and theirinteractions [14] The traditional empirical methods onlystudy a single factor at a time and fail to acknowledge theinteraction that they have between each other [15]
2 Materials and Methods
21 Materials Centella asiatica (CA) was purchased fromlocalmarket Pasar Borong Selayang Selangor and Erythrox-ylum cuneatum (EC) was collected from FRIMrsquos compoundMethanol was purchased from Fisher Scientific ethanol fromJ Kollin Corporation Germany and hexane ethyl acetateand n-butanol from Merck USA All chemicals and solventsused were of analytical grade Iron (II) sulfate heptahy-drate (FeSO
4) was a product of Aldrich USA 441015840-[3-(2-
pyridinyl)-124-triazine-56-diyl]bis also known as ferrozinefrom Aldrich USA
22 Methods
221 Response Surface Methodology (RSM) RSM was usedto optimize the conditions for extraction of CA and EC togive the optimum metal chelating activity A face-centeredcube design (FCD) in RSM consisting of 30 experimentalruns including six replications at the center point was chosen
to evaluate the combined effect of the independent variablesThree levels were adopted and coded to low center andhigh levels The experiments were performed in randomorder to minimize the effects of unexplained variabilityin the observed responses due to systematic errors [15]The independent variables were temperature (∘C) speed ofrotation (rpm) ratio of raw material to solvent (g mL)and time of extraction (h) while the response is the metalchelating activity reported in 1IC
50 As the software was
meant to display the response at maximum the inverse IC50
(1IC50) was reported in this study so that the IC
50will be
displayed at its optimum activityThe total of 30 runs designed by Design Expert by com-
bining the parameters for extraction was shown in Table 1The figures for each parameter were deduced from prelimi-nary experiment Each run was performed in triplicate
222 Extraction Process A constant weight of 2 g plants wasused for all the 30 runs while adjusting accordingly to theratio of methanol solvent that was needed in each run asoutlined byDesignExpert softwareTheplantswere extractedin incubator shaker according to the combination parametersas given by each run The extracts were then separated fromthe filtrate and the methanol solvent was removed usingrotary evaporator at 40∘C and at a reduced pressure Theextracts from each run were then subjected to the metalchelating activity
223 Metal Chelating Activity The assay was initiated byadding 250120583L of 25mM FeSO
4to 500120583L sample solutions
CA and EC crude extracts were prepared in a series ofconcentrations This mixture was vortexed briefly for 10seconds before adding 250120583L of 6mM ferrozineThemixturewas vortexed again briefly for 10 seconds and allowed toequilibrate for 10min at room temperature The absorbanceof the mixture (formation of the ferrous iron-ferrozinecomplex) was measured at 562 nm [16] Sample solutionswith appropriate dilutions were used as blanks The abilityof extracts to chelate ferrous ion was calculated relative tothe control (consisting of iron and ferrozine only) using thefollowing formula [17]
Chelating effect
=(Absorbance of control minus Absorbance of sample)
Absorbance of control
times 100
(1)
224 Partitioning Process The crude methanolic extractswere weighed to be 50 g and were suspended in water andthen subjected to liquid-liquid partition by adding hexaneethyl acetate and n-butanol successively The residual partthat was suspended in water which was the water residuefraction [18] and the hexane ethyl acetate and n-butanolfraction were collected and subjected tometal chelating assayas described above
Biotechnology Research International 3
Table 2 Face centered central composite design setting with theindependent variables and their responses in CA
31 Optimization of Extraction with respect to Metal ChelatingActivity The optimum 1IC
50value for CA (referred to in
Table 2) was 10753mgmL (IC50= 0093mgmL) obtained
in the combined interaction of the independent parameter atRun 17 with 25∘C 200 rpm 1 g 45mL ratio and for durationof 15 hour
Table 3 summarized the experimental results for ECThe optimum 1IC
50value of 26196mgmL (IC
50=
03817mgmL) was obtained in Run 13 with temperatureof 60∘C agitation at 200 rpm and ratio of raw material tosolvent 1 g 35mL ratio for extraction duration of 1 hour
32 Multiple Regression Analysis The statistical model wasdeveloped by applying multiple regression analysis methodson using the experimental data for themetal chelating activity
Table 3 Face centered central composite design setting with theindependent variables and their responses in EC
which is given in (2) for CA and in (3) for EC The responsefunction (119910) measured the 1IC
50value of themetal chelating
activity of the crude extracts CA and EC This value wasrelated to the variables (119860 119861 119862 119863) by a second-degreepolynomial using (2) and (3) which is displayed in termsof coded factors The coefficients of the polynomial wererepresented by a constant term 119860 119861 119862 and 119863 (lineareffects) 1198602 1198612 1198622 and 1198632 (quadratic effects) and 119860119861119860119862 119860119863 119861119862 119861119863 and 119862119863 (interaction effects) The analysisof variance (ANOVA) tables were generated and the effectand regression coefficients of individual linear quadraticand interaction terms were determined The significancesof all terms in the polynomial were judged statistically bycomputing the 119865-value at a probability (119875) of 0001 001 or005 In this case 119860 119861 1198602 1198612 1198622 119860119861 119860119862 119860119863 119861119862 119861119863and 119862119863 are significant model terms On the other handvalues greater than 01000 indicate that the model terms are
4 Biotechnology Research International
Table 4 Fit statistics for the response of 1IC50 value of CA
33 Fit Statistics for the Response Some characteristics of theconstructed model can be explained by details in Table 4 andTable 5 The statistical analysis indicates that the proposedmodel was adequate possessing no significant lack of fitand with satisfactory values of the 119877-squared The quality offit of the polynomial model equation was expressed by thecoefficient of determination (1198772 adjusted 1198772 and adequateprecision) 1198772 is a measure of the amount of variation aroundthe mean explained by the model and equal to 09569 (CA)and 09028 (EC) The closer the value of 119877-squared is tothe unity the better the empirical model fits the actual dataThe smaller the value of 119877-squared is the less relevant thedependent variables in themodel have to explain the behaviorvariation [18] and [19] The adjusted-1198772 is adjusted for thenumber of terms in the model It decreases as the numberof terms in the model increases if those additional terms donot add value to the model Adequate precision is a signal-to-noise ratio It compares the range of the predicted values
at the design points to the average prediction error Ratiosgreater than four indicate adequate model discriminationAs for CA it was 21064 whereas for EC it was 9404 Thestandard deviation of 066 (CA) and 026 (EC) indicatesthat the model designed was acceptable with a minimumdeviation Coefficient of variation (CV) is the standarddeviation expressed as a percentage of the mean which is2534 (CA) and 1612 (EC) CVdescribes the extent towhichthe data were dispersed The small values of CV give betterreproducibility In general a high CV indicates that variationin the mean value is high and does not satisfactorily developan adequate response model [20]
The predicted residual error sum of squares (PRESS) is ameasure of model fitness to each point in the design whichgave an amount of 4629 (CA) and 1612 (EC)
4 Conclusion
The metal chelating activity of CA and EC was optimizedusing statistical analysis to improve the chelating activity ofthe both plants by varying the parameters for the extractionIt shows that the extraction parameters had been optimized(IC50= 0093mgmL at extraction temperature of 25∘C
speed of agitation at 200 rpm ratio of plantmaterial to solventat 1 g 45mL and extraction time at 15 hour) As for EC Run13 with extraction temperature at 60∘C speed of agitationat 200 rpm ratio of plant material to solvent at 1 g 35mLand extraction time at 1 hour had metal chelating activity atIC50= 03817mgmL
Acknowledgment
The authors would like to thank FRIM and MOSTI forproviding fund for the research
References
[1] M Latiff ldquoGenetic resources of medicinal plants in Malaysiardquoin Genetic Resources of Under-Utilised Plants in MalaysiaProceedings of the NationalWorkshop on Plant Genetic ResourcesHeld in Subang Jaya Malaysia A H Zakri Ed MalaysianNational Committee on Plant Genetic Resources KualaLumpur Malaysia 1989
[2] M T Thomas R Kurup A J Johnson et al ldquoElite geno-typeschemotypes with high contents of madecassoside andasiaticoside from sixty accessions of Centella asiatica of southIndia and the Andaman Islands for cultivation and utility incosmetic and herbal drug applicationsrdquo Industrial Crops andProducts vol 32 no 3 pp 545ndash550 2010
[3] L Suguna P Sivakumar and G Chandrakasan ldquoEffects ofCentella asiatica extract on dermal wound healing in ratsrdquoIndian Journal of Experimental Biology vol 34 no 12 pp 1208ndash1211 1996
[4] T D Babu G Kuttan and J Padikkala ldquoCytotoxic and anti-tumour properties of certain taxa of Umbelliferae with specialreference to Centella asiatica (L) Urbanrdquo Journal of Ethnophar-macology vol 48 no 1 pp 53ndash57 1995
[5] S S Katare and M S Ganachari ldquoEffect of Centella asiatica onhypoxia induced convulsions and lithium-pilocarpine induced
Biotechnology Research International 5
status epilepticus and antilipid peroxidation activityrdquo IndianJournal of Pharmacology vol 33 article 128 2001
[6] G Jayashree G K Muraleedhara S Sudarslal and V B JacobldquoAnti-oxidant activity ofCentella asiatica on lymphoma-bearingmicerdquo Fitoterapia vol 74 no 5 pp 431ndash434 2003
[7] R J M Salim Optimisation of extraction of Centella asiaticaand Erythroxylum cuneatum and their evaluation as a neuropro-tective agent [Masterrsquos thesis] International Islamic UniversityMalaysia Selangor Malaysia 2010
[8] A S Sued Kesan ekstrak Centella asiatica Linnaeus dan Ery-throxylum cuneatumForma Cuneatum (Miquel) Kurz ke atassymptom tarikan pada tikus ketagihan morfina dan proteinserummereka [Masterrsquos thesis] Universiti KebangsaanMalaysiaSelangor Malaysia 2009
[9] T Kanchanapoom A Sirikatitham H Otsuka and S Ruchi-rawat ldquoCuneatoside a new megastigmane diglycoside fromErythroxylum cuneatum Blumerdquo Journal of Asian Natural Prod-ucts Research vol 8 no 8 pp 747ndash751 2006
[10] J F Emard J P Thouez and D Gauvreau ldquoNeurodecgnerativediseases and risk factors a literature reviewrdquo Social Science andMedicine vol 40 no 6 pp 847ndash858 1995
[11] L Zecca M B H Youdim P Riederer J R Connor and R RCrichton ldquoIron brain ageing and neurodegenerative disordersrdquoNature Reviews Neuroscience vol 5 no 11 pp 863ndash873 2004
[12] B Yang X Liu and Y Gao ldquoExtraction optimization ofbioactive compounds (crocin geniposide and total phenoliccompounds) from Gardenia (Gardenia jasminoides Ellis) fruitswith response surface methodologyrdquo Innovative Food Scienceand Emerging Technologies vol 10 no 4 pp 610ndash615 2009
[13] J H Kwon J M R Belanger and J R J Pare ldquoOptimization ofmicrowave assisted extraction (MAP) for ginseng componentsby response surface methodologyrdquo Journal of Agricultural andFood Chemistry vol 51 pp 1807ndash1810 2003
[14] W Huang Z Li H Niu D Li and J Zhang ldquoOptimization ofoperating parameters for supercritical carbon dioxide extrac-tion of lycopene by response surface methodologyrdquo Journal ofFood Engineering vol 89 no 3 pp 298ndash302 2008
[15] C S Ku and S PMun ldquoOptimization of the extraction of antho-cyanin from Bokbunja (Rubus coreanus Miq) marc producedduring traditional wine processing and characterization of theextractsrdquo Bioresource Technology vol 99 no 17 pp 8325ndash83302008
[16] T C P Dinis V M C Madeira and L M Almeida ldquoActionof phenolic derivatives (acetaminophen salicylate and 5-aminosalicylate) as inhibitors of membrane lipid peroxidationand as peroxyl radical scavengersrdquo Archives of Biochemistry andBiophysics vol 315 no 1 pp 161ndash169 1994
[17] YWu SW Cui J Tang andXGu ldquoOptimization of extractionprocess of crude polysaccharides from boat-fruited sterculiaseeds by response surface methodologyrdquo Food Chemistry vol105 no 4 pp 1599ndash1605 2007
[18] T Satake K Kamiya Y An T Oishi and J Yamamoto ldquoTheanti-thrombotic active constituents from Centella asiaticardquoBiological and Pharmaceutical Bulletin vol 30 no 5 pp 935ndash940 2007
[19] T M Little and F J Hills Agricultural Experimentation Designand Analysis John Wiley New York NY USA 1978
[20] W W Daniel Biostatistics A Foundation for Analysis in theHealth Sciences vol 503 Wiley New York NY USA 5thedition 1991
31 Optimization of Extraction with respect to Metal ChelatingActivity The optimum 1IC
50value for CA (referred to in
Table 2) was 10753mgmL (IC50= 0093mgmL) obtained
in the combined interaction of the independent parameter atRun 17 with 25∘C 200 rpm 1 g 45mL ratio and for durationof 15 hour
Table 3 summarized the experimental results for ECThe optimum 1IC
50value of 26196mgmL (IC
50=
03817mgmL) was obtained in Run 13 with temperatureof 60∘C agitation at 200 rpm and ratio of raw material tosolvent 1 g 35mL ratio for extraction duration of 1 hour
32 Multiple Regression Analysis The statistical model wasdeveloped by applying multiple regression analysis methodson using the experimental data for themetal chelating activity
Table 3 Face centered central composite design setting with theindependent variables and their responses in EC
which is given in (2) for CA and in (3) for EC The responsefunction (119910) measured the 1IC
50value of themetal chelating
activity of the crude extracts CA and EC This value wasrelated to the variables (119860 119861 119862 119863) by a second-degreepolynomial using (2) and (3) which is displayed in termsof coded factors The coefficients of the polynomial wererepresented by a constant term 119860 119861 119862 and 119863 (lineareffects) 1198602 1198612 1198622 and 1198632 (quadratic effects) and 119860119861119860119862 119860119863 119861119862 119861119863 and 119862119863 (interaction effects) The analysisof variance (ANOVA) tables were generated and the effectand regression coefficients of individual linear quadraticand interaction terms were determined The significancesof all terms in the polynomial were judged statistically bycomputing the 119865-value at a probability (119875) of 0001 001 or005 In this case 119860 119861 1198602 1198612 1198622 119860119861 119860119862 119860119863 119861119862 119861119863and 119862119863 are significant model terms On the other handvalues greater than 01000 indicate that the model terms are
4 Biotechnology Research International
Table 4 Fit statistics for the response of 1IC50 value of CA
33 Fit Statistics for the Response Some characteristics of theconstructed model can be explained by details in Table 4 andTable 5 The statistical analysis indicates that the proposedmodel was adequate possessing no significant lack of fitand with satisfactory values of the 119877-squared The quality offit of the polynomial model equation was expressed by thecoefficient of determination (1198772 adjusted 1198772 and adequateprecision) 1198772 is a measure of the amount of variation aroundthe mean explained by the model and equal to 09569 (CA)and 09028 (EC) The closer the value of 119877-squared is tothe unity the better the empirical model fits the actual dataThe smaller the value of 119877-squared is the less relevant thedependent variables in themodel have to explain the behaviorvariation [18] and [19] The adjusted-1198772 is adjusted for thenumber of terms in the model It decreases as the numberof terms in the model increases if those additional terms donot add value to the model Adequate precision is a signal-to-noise ratio It compares the range of the predicted values
at the design points to the average prediction error Ratiosgreater than four indicate adequate model discriminationAs for CA it was 21064 whereas for EC it was 9404 Thestandard deviation of 066 (CA) and 026 (EC) indicatesthat the model designed was acceptable with a minimumdeviation Coefficient of variation (CV) is the standarddeviation expressed as a percentage of the mean which is2534 (CA) and 1612 (EC) CVdescribes the extent towhichthe data were dispersed The small values of CV give betterreproducibility In general a high CV indicates that variationin the mean value is high and does not satisfactorily developan adequate response model [20]
The predicted residual error sum of squares (PRESS) is ameasure of model fitness to each point in the design whichgave an amount of 4629 (CA) and 1612 (EC)
4 Conclusion
The metal chelating activity of CA and EC was optimizedusing statistical analysis to improve the chelating activity ofthe both plants by varying the parameters for the extractionIt shows that the extraction parameters had been optimized(IC50= 0093mgmL at extraction temperature of 25∘C
speed of agitation at 200 rpm ratio of plantmaterial to solventat 1 g 45mL and extraction time at 15 hour) As for EC Run13 with extraction temperature at 60∘C speed of agitationat 200 rpm ratio of plant material to solvent at 1 g 35mLand extraction time at 1 hour had metal chelating activity atIC50= 03817mgmL
Acknowledgment
The authors would like to thank FRIM and MOSTI forproviding fund for the research
References
[1] M Latiff ldquoGenetic resources of medicinal plants in Malaysiardquoin Genetic Resources of Under-Utilised Plants in MalaysiaProceedings of the NationalWorkshop on Plant Genetic ResourcesHeld in Subang Jaya Malaysia A H Zakri Ed MalaysianNational Committee on Plant Genetic Resources KualaLumpur Malaysia 1989
[2] M T Thomas R Kurup A J Johnson et al ldquoElite geno-typeschemotypes with high contents of madecassoside andasiaticoside from sixty accessions of Centella asiatica of southIndia and the Andaman Islands for cultivation and utility incosmetic and herbal drug applicationsrdquo Industrial Crops andProducts vol 32 no 3 pp 545ndash550 2010
[3] L Suguna P Sivakumar and G Chandrakasan ldquoEffects ofCentella asiatica extract on dermal wound healing in ratsrdquoIndian Journal of Experimental Biology vol 34 no 12 pp 1208ndash1211 1996
[4] T D Babu G Kuttan and J Padikkala ldquoCytotoxic and anti-tumour properties of certain taxa of Umbelliferae with specialreference to Centella asiatica (L) Urbanrdquo Journal of Ethnophar-macology vol 48 no 1 pp 53ndash57 1995
[5] S S Katare and M S Ganachari ldquoEffect of Centella asiatica onhypoxia induced convulsions and lithium-pilocarpine induced
Biotechnology Research International 5
status epilepticus and antilipid peroxidation activityrdquo IndianJournal of Pharmacology vol 33 article 128 2001
[6] G Jayashree G K Muraleedhara S Sudarslal and V B JacobldquoAnti-oxidant activity ofCentella asiatica on lymphoma-bearingmicerdquo Fitoterapia vol 74 no 5 pp 431ndash434 2003
[7] R J M Salim Optimisation of extraction of Centella asiaticaand Erythroxylum cuneatum and their evaluation as a neuropro-tective agent [Masterrsquos thesis] International Islamic UniversityMalaysia Selangor Malaysia 2010
[8] A S Sued Kesan ekstrak Centella asiatica Linnaeus dan Ery-throxylum cuneatumForma Cuneatum (Miquel) Kurz ke atassymptom tarikan pada tikus ketagihan morfina dan proteinserummereka [Masterrsquos thesis] Universiti KebangsaanMalaysiaSelangor Malaysia 2009
[9] T Kanchanapoom A Sirikatitham H Otsuka and S Ruchi-rawat ldquoCuneatoside a new megastigmane diglycoside fromErythroxylum cuneatum Blumerdquo Journal of Asian Natural Prod-ucts Research vol 8 no 8 pp 747ndash751 2006
[10] J F Emard J P Thouez and D Gauvreau ldquoNeurodecgnerativediseases and risk factors a literature reviewrdquo Social Science andMedicine vol 40 no 6 pp 847ndash858 1995
[11] L Zecca M B H Youdim P Riederer J R Connor and R RCrichton ldquoIron brain ageing and neurodegenerative disordersrdquoNature Reviews Neuroscience vol 5 no 11 pp 863ndash873 2004
[12] B Yang X Liu and Y Gao ldquoExtraction optimization ofbioactive compounds (crocin geniposide and total phenoliccompounds) from Gardenia (Gardenia jasminoides Ellis) fruitswith response surface methodologyrdquo Innovative Food Scienceand Emerging Technologies vol 10 no 4 pp 610ndash615 2009
[13] J H Kwon J M R Belanger and J R J Pare ldquoOptimization ofmicrowave assisted extraction (MAP) for ginseng componentsby response surface methodologyrdquo Journal of Agricultural andFood Chemistry vol 51 pp 1807ndash1810 2003
[14] W Huang Z Li H Niu D Li and J Zhang ldquoOptimization ofoperating parameters for supercritical carbon dioxide extrac-tion of lycopene by response surface methodologyrdquo Journal ofFood Engineering vol 89 no 3 pp 298ndash302 2008
[15] C S Ku and S PMun ldquoOptimization of the extraction of antho-cyanin from Bokbunja (Rubus coreanus Miq) marc producedduring traditional wine processing and characterization of theextractsrdquo Bioresource Technology vol 99 no 17 pp 8325ndash83302008
[16] T C P Dinis V M C Madeira and L M Almeida ldquoActionof phenolic derivatives (acetaminophen salicylate and 5-aminosalicylate) as inhibitors of membrane lipid peroxidationand as peroxyl radical scavengersrdquo Archives of Biochemistry andBiophysics vol 315 no 1 pp 161ndash169 1994
[17] YWu SW Cui J Tang andXGu ldquoOptimization of extractionprocess of crude polysaccharides from boat-fruited sterculiaseeds by response surface methodologyrdquo Food Chemistry vol105 no 4 pp 1599ndash1605 2007
[18] T Satake K Kamiya Y An T Oishi and J Yamamoto ldquoTheanti-thrombotic active constituents from Centella asiaticardquoBiological and Pharmaceutical Bulletin vol 30 no 5 pp 935ndash940 2007
[19] T M Little and F J Hills Agricultural Experimentation Designand Analysis John Wiley New York NY USA 1978
[20] W W Daniel Biostatistics A Foundation for Analysis in theHealth Sciences vol 503 Wiley New York NY USA 5thedition 1991
33 Fit Statistics for the Response Some characteristics of theconstructed model can be explained by details in Table 4 andTable 5 The statistical analysis indicates that the proposedmodel was adequate possessing no significant lack of fitand with satisfactory values of the 119877-squared The quality offit of the polynomial model equation was expressed by thecoefficient of determination (1198772 adjusted 1198772 and adequateprecision) 1198772 is a measure of the amount of variation aroundthe mean explained by the model and equal to 09569 (CA)and 09028 (EC) The closer the value of 119877-squared is tothe unity the better the empirical model fits the actual dataThe smaller the value of 119877-squared is the less relevant thedependent variables in themodel have to explain the behaviorvariation [18] and [19] The adjusted-1198772 is adjusted for thenumber of terms in the model It decreases as the numberof terms in the model increases if those additional terms donot add value to the model Adequate precision is a signal-to-noise ratio It compares the range of the predicted values
at the design points to the average prediction error Ratiosgreater than four indicate adequate model discriminationAs for CA it was 21064 whereas for EC it was 9404 Thestandard deviation of 066 (CA) and 026 (EC) indicatesthat the model designed was acceptable with a minimumdeviation Coefficient of variation (CV) is the standarddeviation expressed as a percentage of the mean which is2534 (CA) and 1612 (EC) CVdescribes the extent towhichthe data were dispersed The small values of CV give betterreproducibility In general a high CV indicates that variationin the mean value is high and does not satisfactorily developan adequate response model [20]
The predicted residual error sum of squares (PRESS) is ameasure of model fitness to each point in the design whichgave an amount of 4629 (CA) and 1612 (EC)
4 Conclusion
The metal chelating activity of CA and EC was optimizedusing statistical analysis to improve the chelating activity ofthe both plants by varying the parameters for the extractionIt shows that the extraction parameters had been optimized(IC50= 0093mgmL at extraction temperature of 25∘C
speed of agitation at 200 rpm ratio of plantmaterial to solventat 1 g 45mL and extraction time at 15 hour) As for EC Run13 with extraction temperature at 60∘C speed of agitationat 200 rpm ratio of plant material to solvent at 1 g 35mLand extraction time at 1 hour had metal chelating activity atIC50= 03817mgmL
Acknowledgment
The authors would like to thank FRIM and MOSTI forproviding fund for the research
References
[1] M Latiff ldquoGenetic resources of medicinal plants in Malaysiardquoin Genetic Resources of Under-Utilised Plants in MalaysiaProceedings of the NationalWorkshop on Plant Genetic ResourcesHeld in Subang Jaya Malaysia A H Zakri Ed MalaysianNational Committee on Plant Genetic Resources KualaLumpur Malaysia 1989
[2] M T Thomas R Kurup A J Johnson et al ldquoElite geno-typeschemotypes with high contents of madecassoside andasiaticoside from sixty accessions of Centella asiatica of southIndia and the Andaman Islands for cultivation and utility incosmetic and herbal drug applicationsrdquo Industrial Crops andProducts vol 32 no 3 pp 545ndash550 2010
[3] L Suguna P Sivakumar and G Chandrakasan ldquoEffects ofCentella asiatica extract on dermal wound healing in ratsrdquoIndian Journal of Experimental Biology vol 34 no 12 pp 1208ndash1211 1996
[4] T D Babu G Kuttan and J Padikkala ldquoCytotoxic and anti-tumour properties of certain taxa of Umbelliferae with specialreference to Centella asiatica (L) Urbanrdquo Journal of Ethnophar-macology vol 48 no 1 pp 53ndash57 1995
[5] S S Katare and M S Ganachari ldquoEffect of Centella asiatica onhypoxia induced convulsions and lithium-pilocarpine induced
Biotechnology Research International 5
status epilepticus and antilipid peroxidation activityrdquo IndianJournal of Pharmacology vol 33 article 128 2001
[6] G Jayashree G K Muraleedhara S Sudarslal and V B JacobldquoAnti-oxidant activity ofCentella asiatica on lymphoma-bearingmicerdquo Fitoterapia vol 74 no 5 pp 431ndash434 2003
[7] R J M Salim Optimisation of extraction of Centella asiaticaand Erythroxylum cuneatum and their evaluation as a neuropro-tective agent [Masterrsquos thesis] International Islamic UniversityMalaysia Selangor Malaysia 2010
[8] A S Sued Kesan ekstrak Centella asiatica Linnaeus dan Ery-throxylum cuneatumForma Cuneatum (Miquel) Kurz ke atassymptom tarikan pada tikus ketagihan morfina dan proteinserummereka [Masterrsquos thesis] Universiti KebangsaanMalaysiaSelangor Malaysia 2009
[9] T Kanchanapoom A Sirikatitham H Otsuka and S Ruchi-rawat ldquoCuneatoside a new megastigmane diglycoside fromErythroxylum cuneatum Blumerdquo Journal of Asian Natural Prod-ucts Research vol 8 no 8 pp 747ndash751 2006
[10] J F Emard J P Thouez and D Gauvreau ldquoNeurodecgnerativediseases and risk factors a literature reviewrdquo Social Science andMedicine vol 40 no 6 pp 847ndash858 1995
[11] L Zecca M B H Youdim P Riederer J R Connor and R RCrichton ldquoIron brain ageing and neurodegenerative disordersrdquoNature Reviews Neuroscience vol 5 no 11 pp 863ndash873 2004
[12] B Yang X Liu and Y Gao ldquoExtraction optimization ofbioactive compounds (crocin geniposide and total phenoliccompounds) from Gardenia (Gardenia jasminoides Ellis) fruitswith response surface methodologyrdquo Innovative Food Scienceand Emerging Technologies vol 10 no 4 pp 610ndash615 2009
[13] J H Kwon J M R Belanger and J R J Pare ldquoOptimization ofmicrowave assisted extraction (MAP) for ginseng componentsby response surface methodologyrdquo Journal of Agricultural andFood Chemistry vol 51 pp 1807ndash1810 2003
[14] W Huang Z Li H Niu D Li and J Zhang ldquoOptimization ofoperating parameters for supercritical carbon dioxide extrac-tion of lycopene by response surface methodologyrdquo Journal ofFood Engineering vol 89 no 3 pp 298ndash302 2008
[15] C S Ku and S PMun ldquoOptimization of the extraction of antho-cyanin from Bokbunja (Rubus coreanus Miq) marc producedduring traditional wine processing and characterization of theextractsrdquo Bioresource Technology vol 99 no 17 pp 8325ndash83302008
[16] T C P Dinis V M C Madeira and L M Almeida ldquoActionof phenolic derivatives (acetaminophen salicylate and 5-aminosalicylate) as inhibitors of membrane lipid peroxidationand as peroxyl radical scavengersrdquo Archives of Biochemistry andBiophysics vol 315 no 1 pp 161ndash169 1994
[17] YWu SW Cui J Tang andXGu ldquoOptimization of extractionprocess of crude polysaccharides from boat-fruited sterculiaseeds by response surface methodologyrdquo Food Chemistry vol105 no 4 pp 1599ndash1605 2007
[18] T Satake K Kamiya Y An T Oishi and J Yamamoto ldquoTheanti-thrombotic active constituents from Centella asiaticardquoBiological and Pharmaceutical Bulletin vol 30 no 5 pp 935ndash940 2007
[19] T M Little and F J Hills Agricultural Experimentation Designand Analysis John Wiley New York NY USA 1978
[20] W W Daniel Biostatistics A Foundation for Analysis in theHealth Sciences vol 503 Wiley New York NY USA 5thedition 1991
status epilepticus and antilipid peroxidation activityrdquo IndianJournal of Pharmacology vol 33 article 128 2001
[6] G Jayashree G K Muraleedhara S Sudarslal and V B JacobldquoAnti-oxidant activity ofCentella asiatica on lymphoma-bearingmicerdquo Fitoterapia vol 74 no 5 pp 431ndash434 2003
[7] R J M Salim Optimisation of extraction of Centella asiaticaand Erythroxylum cuneatum and their evaluation as a neuropro-tective agent [Masterrsquos thesis] International Islamic UniversityMalaysia Selangor Malaysia 2010
[8] A S Sued Kesan ekstrak Centella asiatica Linnaeus dan Ery-throxylum cuneatumForma Cuneatum (Miquel) Kurz ke atassymptom tarikan pada tikus ketagihan morfina dan proteinserummereka [Masterrsquos thesis] Universiti KebangsaanMalaysiaSelangor Malaysia 2009
[9] T Kanchanapoom A Sirikatitham H Otsuka and S Ruchi-rawat ldquoCuneatoside a new megastigmane diglycoside fromErythroxylum cuneatum Blumerdquo Journal of Asian Natural Prod-ucts Research vol 8 no 8 pp 747ndash751 2006
[10] J F Emard J P Thouez and D Gauvreau ldquoNeurodecgnerativediseases and risk factors a literature reviewrdquo Social Science andMedicine vol 40 no 6 pp 847ndash858 1995
[11] L Zecca M B H Youdim P Riederer J R Connor and R RCrichton ldquoIron brain ageing and neurodegenerative disordersrdquoNature Reviews Neuroscience vol 5 no 11 pp 863ndash873 2004
[12] B Yang X Liu and Y Gao ldquoExtraction optimization ofbioactive compounds (crocin geniposide and total phenoliccompounds) from Gardenia (Gardenia jasminoides Ellis) fruitswith response surface methodologyrdquo Innovative Food Scienceand Emerging Technologies vol 10 no 4 pp 610ndash615 2009
[13] J H Kwon J M R Belanger and J R J Pare ldquoOptimization ofmicrowave assisted extraction (MAP) for ginseng componentsby response surface methodologyrdquo Journal of Agricultural andFood Chemistry vol 51 pp 1807ndash1810 2003
[14] W Huang Z Li H Niu D Li and J Zhang ldquoOptimization ofoperating parameters for supercritical carbon dioxide extrac-tion of lycopene by response surface methodologyrdquo Journal ofFood Engineering vol 89 no 3 pp 298ndash302 2008
[15] C S Ku and S PMun ldquoOptimization of the extraction of antho-cyanin from Bokbunja (Rubus coreanus Miq) marc producedduring traditional wine processing and characterization of theextractsrdquo Bioresource Technology vol 99 no 17 pp 8325ndash83302008
[16] T C P Dinis V M C Madeira and L M Almeida ldquoActionof phenolic derivatives (acetaminophen salicylate and 5-aminosalicylate) as inhibitors of membrane lipid peroxidationand as peroxyl radical scavengersrdquo Archives of Biochemistry andBiophysics vol 315 no 1 pp 161ndash169 1994
[17] YWu SW Cui J Tang andXGu ldquoOptimization of extractionprocess of crude polysaccharides from boat-fruited sterculiaseeds by response surface methodologyrdquo Food Chemistry vol105 no 4 pp 1599ndash1605 2007
[18] T Satake K Kamiya Y An T Oishi and J Yamamoto ldquoTheanti-thrombotic active constituents from Centella asiaticardquoBiological and Pharmaceutical Bulletin vol 30 no 5 pp 935ndash940 2007
[19] T M Little and F J Hills Agricultural Experimentation Designand Analysis John Wiley New York NY USA 1978
[20] W W Daniel Biostatistics A Foundation for Analysis in theHealth Sciences vol 503 Wiley New York NY USA 5thedition 1991