Wahidu Zzaman - Ghent University · Shuvo my beloved son and Mrs. Julekha Wahid who always shares...

Faculty of Bioscience Engineering

Academic year 2011 – 2012

Optimization of antioxidant extraction

from jackfruit (Artocarpus heterophyllus Lam.) seeds

using response surface methodology

Wahidu Zzaman

Promoter: Prof. dr. ir. Koen Dewettinck

Tutor: Mohammad Mozidul Islam

Master’s dissertation submitted in partial fulfillment of the requirements for the degree of Master of Science in Nutrition and Rural Development,

main subject Human Nutrition

i

Copyright

“All rights reserved. The author, the promoter and the tutor permit the use of this Master’s

dissertation for consulting purposes and copying of parts for personal use. However, any other

use fall under the limitations of copyright regulations, particularly the stringent obligation to

explicitly mention the source when citing parts out of this Master’s dissertation”.

Ghent University, August, 2012

Promoter:…………………….. Tutor:..............................

Prof. dr. ir. Koen Dewettinck Mohammad Mozidul Islam

Author

Wahidu Zzaman

ii

ACKNOWLEDGEMENTS

In the name of Allah, the most gracious, the most merciful, all praise is God Lord of all creation.

I would sincerely like to thank all those who helped and inspired me to complete this

dissertation.

I express my sincere gratitude, heartfelt respect, profound regards and indebtedness to my

respected promoter Prof. dr. ir. Koen Dewettinck, Head of the laboratory of Food Technology

and Engineering, Department of Food Safety and Food Quality, Faculty of Bioscience

Engineering, Ghent University, for his scholastic guidance, constructive valuable suggestions

and continuous encouragement during the dissertation period. I am deeply indebted to my tutor

Mr. Mohammad Mozidul Islam, Doctoral Student, Department of Food Safety and Food Quality,

Faculty of Bioscience Engineering, Ghent University for showing whole hearted interest during

this research. His supportive suggestions and intellectual perception helped me to carrying this

research work.

I am also grateful to Mrs. ir. Anne-Marie Remaut-Dewinter, Mrs. ir. Kathleen Anthierens, Mrs.

Marian Mareen and Mrs. Ruth Van den Driessche for their friendly assistance and generous help

on every occasion. To all members and staff of the Laboratory of Food Technology and

Engineering, I am grateful for their cooperation and warm friendship. Especially Benny Lewille,

Corine Loijson, and Beatrijs Vermeule for their valuable technical assistance.

I am very much grateful to VLIR-UOS (Flemish Interuniversity Council–University

Development Cooperation) for the financial and logistic support to pursue this master program,

without which this study work would have not been possible and wish to extend my sincere

thanks to the VLIR staff for their cordial concern about the international student.

I would like to express my special thanks to my beloved parents, family members and relatives,

who always blessed, inspired and sacrificed during my study. I am so indebted to Jesanuzzaman

Shuvo my beloved son and Mrs. Julekha Wahid who always shares with me love, happiness and

sorrow.

Wahidu Zzaman

Ghent, August, 2012

iii

TABLE OF CONTENTS

ACKNOWLEDGEMENTS .......................................................................................................... II

TABLE OF CONTENTS ............................................................................................................ III

LIST OF TABLES ................................................................................................................... VII

LIST OF ABBREVIATIONS ....................................................................................................... IX

LIST OF APPENDICES ............................................................................................................. XI

ABSTRACT ........................................................................................................................... XII

CHAPTER I: INTRODUCTION ................................................................................................... 1

CHAPTER II: LITERATURE REVIEW ........................................................................................... 4

2.1. LIPID OXIDATION IN FOODS ......................................................................................................... 4

2.1.1. Mechanisms of Lipid Oxidation ..................................................................................... 5

2.1.2. Photo-oxidation............................................................................................................. 7

2.1.3. Enzyme-mediated Oxidation ......................................................................................... 7

2.2. ANTIOXIDANTS .......................................................................................................................... 8

2.2.1. Synthetic antioxidants ................................................................................................... 8

2.2.2. Natural Antioxidants ..................................................................................................... 9

2.3. EXTRACTION OF POLYPHENOLS FROM PLANT MATERIALS .................................................................. 12

2.4. MEASURING ANTIOXIDANT ACTIVITY IN FOOD ................................................................................ 12

2.5. RESPONSE SURFACE METHODOLOGY (RSM) ................................................................................. 13

2.5.1. Screening ..................................................................................................................... 14

2.5.2. Factorial design ........................................................................................................... 14

2.5.3. Fractional factorial design .......................................................................................... 15

2.5.4. Addition of central point to factorial design ............................................................... 15

2.5.5. Blocking and randomization ....................................................................................... 15

2.5.6. Analysis for screening experiment .............................................................................. 16

2.5.7. Optimization ............................................................................................................... 16

CHAPTER III: MATERIALS AND METHODS ............................................................................. 19

3.1. CHEMICALS ............................................................................................................................ 19

3.2. PREPARATION OF MATERIALS ..................................................................................................... 19

3.3. EXTRACTION PROCEDURE .......................................................................................................... 19

3.4. DPPH RADICAL SCAVENGING ACTIVITY ......................................................................................... 20

3.5. THE TOTAL PHENOLIC COMPOUNDS (FCR) .................................................................................... 21

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3.6. FERRIC REDUCING ANTIOXIDANT POWER (FRAP) ........................................................................... 22

3.7. EXPERIMENTAL DESIGN ............................................................................................................. 23

3.7.1. Variable identification and screening ......................................................................... 24

3.7.2. Fitting a first-order model ........................................................................................... 25

3.7.3. Fitting second-order model ......................................................................................... 26

3.8. CORRELATION BETWEEN PHENOLIC CONTENT AND ANTIOXIDANT ACTIVITIES ........................................ 29

3.9. STATISTICAL ANALYSIS............................................................................................................... 29

CHAPTER IV: RESULTS AND DISCUSSION .............................................................................. 30

4.1. SAMPLE ................................................................................................................................. 30

4.2. STANDARD CURVES .................................................................................................................. 30

4.3. FACTORS SCREENING AND IDENTIFICATION .................................................................................... 30

4.4. FITTING MODELS ..................................................................................................................... 31

4.4.1. Response surface analysis of radical scavenging property (DPPH) ............................ 31

4.4.2. Response surface analysis of total phenol content (FCR) ........................................... 34

4.4.3. Response surface analysis of antioxidant activity (FRAP) ........................................... 36

4.5. OPTIMIZATION AND VERIFICATION OF THE MODELS ......................................................................... 38

4.6. ROLE OF POLYPHENOLS AS ANTIOXIDANT ...................................................................................... 40

CHAPTER V: CONCLUSION AND FURTHER RECOMMENDATION ............................................ 42

5.1. CONCLUSION .......................................................................................................................... 42

5.2. FURTHER RECOMMENDATION .................................................................................................... 43

REFERENCES ........................................................................................................................ 44

v

LIST OF FIGURES

Figure 1. Mechanism of auto-oxidation............................................................................... 6

Figure 2. Mechanism of photo-oxidation............................................................................. 7

Figure 3. Antioxidant (AH) reactions with free radicals generated during lipid oxidation. 8

Figure 4. Chemical structures of some common synthetic antioxidants.............................. 9

Figure 5. Chemical structures of common flavonoids found in plants................................ 11

Figure 6. The 22 factorial design.......................................................................................... 14

Figure 7. Blocking in design................................................................................................ 15

Figure 8. Box-Behnken design for three factors-(a) shows the geometric representation

and (b) shows the design......................................................................................

17

Figure 9. Factor combinations for a central composite design............................................ 18

Figure 10. The standard curve by fitting the percentage of radical scavenging effect

versus its corresponding Trolox concentration.....................................................

20

Figure 11. The standard curve fitted by plotting absorbance versus the corresponding

concentration of Gallic acid solutions..................................................................

21

Figure 12. The standard curve by fitting the absorbance versus its corresponding standard

ascorbic solutions..................................................................................................

22

Figure 13. A flow diagram of the overall optimization of the process.................................. 23

Figure 14. Standard residual plots of the three linear fitted models, where responses are

(a) DPPH, (b) FCR and (c) FRAP......................................................................

26

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Figure 15. Standard residual plots of the three quadratic models, where responses are (a)

DPPH (b) FCR and (c) FRAP..............................................................................

28

Figure 16. Response surface plot showing the combined effect of (a) ethanol

concentration and temperature at fixed 55.01 ml/g DM liquid-to-solid ratio, (b)

liquid-to-solid ratio and temperature at fixed 72.19 % ethanol concentration,

(c) ethanol concentration and liquid-to-solid ratio at fixed 35 oC temperature

on radical scavenging activity measured by DPPH (mg TE/100 g DM)..............

32





on phenolic content measured by FCR (mg GAE/100 g DM).............................

35





on phenolic content measured by FRAP (mg AA/100 g DM).............................

37

Figure 19. Superimposed contour plots of responses DPPH and FRAP as a function of

liquid-to-solid ratio and ethanol concentration at temperature 35 oC...................

40

vii

LIST OF TABLES

Table 1. Regression coefficients and coefficient of determination of standard curves... 23

Table 2A. A full 24 factorial design experimental design and corresponding responses

for an ethanolic extraction with x1=ethanol concentration (%, v/v),

x2=temperature (oC), x3=liquid-to-solid ratio (ml/g DM), x4=time (minute);

DPPH=radical scavenging property (mg TE/100 g DM); FCR=total phenolic

content (mg GAE/100 g DM); FRAP=ferric reducing antioxidant property

(mg AA/100 g DM)...........................................................................................

24

Table 2B Experimental results of full factorial (24) screening design to identify most

influencing factors.............................................................................................

25

Table 3A. A full 23 factorial design experimental design with three replication at the

center and corresponding responses for an ethanolic extraction with

x1=ethanol concentration (%, v/v), x2=temperature (oC), x3=liquid-to-solid

ratio (ml/g DM); DPPH=radical scavenging property (mg TE/100 g DM);

FCR=total phenolic content (mg GAE/100 g DM); FRAP=ferric reducing

antioxidant property (mg AA/100 g DM)..........................................................

25

Table 3B Coefficient of determination (R2) and lack of fit values to evaluate the linear

fitted models with the experimental data of Table 3A; where DPPH=radical

scavenging property (mg TE/100 g DM); FCR=total phenolic content (mg

GAE/100 g DM); FRAP=ferric reducing antioxidant property (mg AA/100 g

DM).....................................................................................................................

26

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Table 4A

Box-Behnken design and corresponding responses for an ethanolic extraction

with x1=ethanol concentration (%, v/v), x2=temperature (oC) and x3=liquid-to-

solid ratio (ml/ g DM); DPPH=radical scavenging property (mg TE/100 g

DM); FCR=total phenolic content (mg GAE/100 g DM); FRAP=ferric

reducing antioxidant property (mg AA/100 g DM)............................................

27

Table 4B Regression coefficients, the coefficient of determination (R2), lack of fit

values for the second order fitted models. Predicted values are DPPH=radical

scavenging property (mg TE/100 g DM); FCR=total phenolic content (mg

GAE/100 g DM); FRAP=ferric reducing antioxidant property (mg AA/100 g

DM).....................................................................................................................

28

Table 5. Maximum predicted values from the second order fitted models. Responses

are DPPH=radical scavenging property (mg TE/100 g DM); FCR=Total

phenolic content (mg GAE/100 g DM); FRAP=Ferric reducing antioxidant

property (mg AA/100 g DM)..............................................................................

39

Table 6. Experimental data for verification of the models predicted at optimal

condition with x1=ethanol concentration (v/v %), x2=temperature (oC),

x3=liquid-to-solid ratio (ml/g DM); Responses are DPPH=radical scavenging

property (mg TE/100 g DM); FCR=Total phenolic content (mg GAE/100 g

DM); FRAP=Ferric reducing antioxidant property (mg AA/100 g DM)...........

39

ix

LIST OF ABBREVIATIONS

ANOVA Analysis of Variance

AH Antioxidant

AA Ascorbic acid

BHA Butylated hydroxyanisole

BHT Butylated hydroxytoluene

CVD Cardiovascular disease

CCD Central composite design

CCRD Central composite rotatable design

CV Coefficient of variation

DOE Design of experiment

DPPH 2,2-diphenyl-1-picrylhydrazyl

DM Dry matter

ET Electron transfer

FRAP Ferric ion reducing antioxidant power

FCR Folin-Ciocalteu Phenol-Reagent

GA Gallic acid

GAE Gallic acid equivalent

HAT Hydrogen atom transfer

HN Hydroxynonenal

MDA Malondialdehyde

ORAC Oxygen radical absorbance capacity

pAV p-anisidine value

x

PV Peroxide value

PG Propyl gallate

RSM Response surface methodology

TBHQ Tert-butyl hydroquinone

TBARS Thiobarbituric acid reactive substances

TRAP Total radical trapping antioxidant parameters

TE Trolox equivalent

TEAC Trolox equivalence antioxidant capacity

xi

LIST OF APPENDICES

Appendix Table 1: Crude extracts yield calculation of jackfruit seeds 52

Appendix Table 2: ANOVA on screening test for DPPH without interaction 53

Appendix Table 3: Full factorial (24) screening test for DPPH with 2 factors interaction. 54

Appendix Table 4: Full factorial (24) screening test for FCR without interaction 55

Appendix Table 5: Full factorial (24) screening test for FCR with interaction 56

Appendix Table 6: Full factorial (24) screening test for FRAP withoutu interaction 57

Appendix Table 7: Full factorial (24) screening test for FRAP with interaction 58

Appendix Table 8: ANOVA on second order regreession model for DPPH 59

Appendix Table 9: ANOVA check for the fitness of the model for DPPH 60

Appendix Table 10: ANOVA on second order regreession model for FCR 61

Appendix Table 11: ANOVA check for the fitness of the model for FCR 62

Appendix Table 12: ANOVA on second order regreession model for FRAP 63

Appendix Table13: ANOVA check for the fitness of the model for FRAP 64

xii

ABSTRACT

Response surface methodology (RSM) in combination with Box-Behnken experimental design

was performed in this study to optimize the extraction parameters for assessing maximum yield

of antioxidant activity from jackfruit seeds. The RSM with a three level, three-factor mixture

design was used to optimize the extraction condition. The aqueous extraction of antioxidant

compounds from freeze-dried powder of jackfruit seeds were optimized by using the three

independent variables, namely ethanol concentration (%, v/v), temperature (oC) and liquid-to-

solid ratio (ml/g DM) were selected after factorial screening. The second order polynomial

models were found to be adequate to fit with the experimental data for radical scavenging

activity (R2=0.985), antioxidant activity (R

2=0.968) and total phenolic content (R

2=0.981). The

optimal conditions were determined by using desirability function approach, where both radical

scavenging activity measured by 2,2-diphenyl-1-picrylhydrazyl (DPPH) and reducing activity

measured by ferric reducing antioxidant power (FRAP) were considered with equal importance.

Using this approach, the following optimal conditions can be recommended: ethanol 72.16%,

temperature 35oC and liquid-to-solid ratio 55.02ml/g DM. Under these conditions scavenging

activity of 1003.22mg Trolox eq./100g DW, reducing activity of 679.18mg Asc. Acid equ./100g

DM, and phenolic content of 1031.68mg GA/100g DM were obtained which was in close

conformity with predicted values, thus indicating the suitability of the models developed and the

success of RSM in optimizing the extraction setting. These methods could be utilized to prepare

crude extracts containing antioxidant from underutilized jackfruit seeds for industrial use as food

additives to protect the food products in retaining their sensorial quality, e.g. color, texture and

taste, as well as their nutritional quality.

1

CHAPTER I: INTRODUCTION

Jackfruit is the largest tree born fruit in the world. Historically, the fruit is native to India, and

with time, the fruit has spread all over the world. Now jackfruit can be found in Bangladesh,

Malaysia, Myanmar, Sri-Lanka, Indonesia, USA (Florida, California ), Australia, West Africa,

the Caribbean, Brazil, Puerto Rico, Nauru, Samoa and many other countries (Bose, 1985;

Elevitch and Manner, 2006; Haq, 2006; Samaddar, 1985). Jackfruit is a very popular fruit in

India and Bangladesh (Bose, 1985; Morton, 1987), and in recent years the fruit is gaining

popularity in the USA as well (Campbell and El-Sawa, 1998; Schnell et al., 2001). In India,

jackfruit is the third largest harvested fruit ranked after mango and banana (Morton, 1987).

During the season, the fruit grows in plenty and is quite cheap where grown, but expensive in

the off-season. (Jagtap et al., 2010; Morton, 1987). Each year approximately 30-50% the total

harvested fruit, e.g. jackfruit, is spoiled because of the lack of post-harvest processing in India

and Bangladesh (Ali, 2003). The ripe sweet bulbs of the fruit can be processed into ice cream,

jam, jelly, alcoholic beverages, nectars or fruit powder (Elevitch and Manner, 2006; Morton,

1987). However, the industrial use of jackfruit seed has not been as diversified as pulp; apart

from the use as a substitute of wheat flour (Prakash et al., 2009) it is also processed in can.

Jackfruit seeds are mostly consumed after roasting in some local dishes (Samaddar, 1985).

Additionally, with comparison with others tropical fruits, such as orange, mango, banana,

pineapple, papaya, jack fruit contains higher protein, calcium, iron and thiamine levels and is

considered a good source of essential nutrients (Bhatia et al., 1955; Haq, 2006). Seeds are

reported as comparatively higher in phenolic and antioxidant components than bulb (Lu and

Foo, 1999; Meyer et al., 1998; Soong and Barlow, 2004).

In term of health benefits, epidemiological studies already have indicated that diet enriched

with phenolics probably play the protective role against different degenerative diseases

(Halliwell, 2008). According to Haleem et al. (2008) most of the beneficial properties of

phenolics are attributed due to their antioxidant activity. One of the possibilities is to increase

the consumption of antioxidants as a functional compound in daily diets (Wijngaard and

Brunton, 2010). However, consumers often reject food products that are enriched in with

synthetic antioxidants and prefer natural antioxidants (Wettasinghe and Shahidi, 1999).

2

Besides playing the role as a functional component, antioxidants also help the food products in

retaining their sensorial quality, e.g. color, texture and taste, as well as their nutritional quality

through preventing the oxidation of essential fatty acids (Coda et al., 2012). Many researchers

have already illustrated that natural antioxidant compounds isolated from different sources, are

a good alternative for synthetic antioxidants in the retardation of fat oxidation (Saha et al.,

2011; Viuda-Martos et al., 2009).

Recently, a trend has been noticed for the search of newer antioxidants especially from the

plant origin (Singh and Rajini, 2004). According to Prasad et al. (2011) in recent years research

and development activities have especially focused on underutilized fruits. Moreover, in our

modern life waste valorization has become an important issue for food industries (Wijngaard

and Brunton, 2010). In (2004) Soong and Barlow emphasized on the importance of utilization

of jack fruit seeds as a source of natural food additives and ingredients. One of the possibilities,

is to use the seeds as a source of natural antioxidants (Bhushan et al., 2008). However before

extraction, the process should be optimized because factors like extraction time, temperature,

solvent concentration, pressure, solid-to-liquid ratio and pH can significantly influence the

extraction process (Prasad et al., 2011).

In a quest for natural antioxidant, Soong and Barlow (2004) used an identical method of

extraction process for jackfruit, avocado, longan, mango and tamarind for a respective

comparison of antioxidant activity between the edible portion and seeds. However according to

Liu et al. (2000), it would be difficult of establish an universally optimized extraction protocol

due to the complex internal matrix and diversity of the antioxidant compounds of natural

sources. Hence, the optimum extraction protocol is anticipated to be different according to the

type of fruits and between the edible portion and seeds.

In a traditional method of optimization, also known as “one factor at a time” optimization, an

individual factor is changed continuously while keeping all other remaining factors constant,

until the best value of response can be selected. This traditional technique is laborious and

could be erroneous, because it does not take into account the interactions between factors. This

limitation can easily be solved using specific design of experiment (DOE) (Box and Draper,

1987).

So far, no studies have reported the optimization of the antioxidant extraction from jackfruit

seeds. Hence, in this present study, the antioxidant activity of jackfruit seeds will be studied by

3

using three different in vitro assay systems namely, radical scavenging activity (DPPH),

antioxidant reducing activity (FRAP), and phenolic content (FCR), in order to identify the

overall optimized antioxidant extraction protocol from jackfruit seeds. Considering the residual

toxicity of solvent, in the study, only ethanol and water are used for the extraction procedure. A

full factorial experimental design (24) will be carried out in the beginning for the variable

screening, followed by 23 full factorial and Box-Behnken designs for the further optimization.

The objectives of this study are:

To explore the effects of solvent concentration, extraction time, extraction temperature

and liquid-to-solid ratio on the extraction of antioxidant properties from jackfruit seeds;

To optimise the extraction conditions for antioxidant properties from jackfruit seeds;

To valorise of an under utilized product (jackfruit seeds).

4

CHAPTER II: LITERATURE REVIEW

2.1. Lipid Oxidation in Foods

An antioxidant is a molecule that can prevent the oxidation of other molecules. Oxidation is the

interaction between oxygen molecules and all the different substances. It is chemical reaction

that transfers electrons or hydrogen atom from a substance to an oxidizing agent. Free radicals

are produced by oxidation reaction and the free radicals are extremely reactive and unstable

that prone to react with molecules, and these radicals can also start chain reactions.

Antioxidants can neutralize free radicals and stop these chain reactions by removing free

radical intermediates, and slow down other oxidation reactions. So antioxidants are often

reducing agents they do this by being oxidized themselves, such as polyphenols, ascorbic acid

or thiols.

Lipid oxidation is one of the major economic concerns in food industry as they may cause bad

effect on taste, flavour, colour, nutritional value and shelf life of foods (Juntachote et al.,

2006). Synthetic antioxidants such as butylated hydroxyanisole (BHA) and butylated

hydroxytoluene (BHT) are usually used to slow down the oxidative deterioration but due to

their possible toxic and carcinogenic effects there has been increasing worry over the use of

synthetic antioxidants to the fresh or processed foods (Arabshahi-Delouee and Urooj, 2006).

As a result, the use of natural and safe antioxidants, especially of tropical fruits and vegetables

has increased significantly in these recent years among consumer, institutionalists and food

scientists.

Lipids are one of the major components of many foods, and often need for the development of

flavor, texture and color characteristic. Nevertheless, lipids are highly unstable and are readily

reacted by oxygen, causing to a chain of chemical reactions that produce undesirable flavor and

odor compounds. These oxidative reactions can be speed up by metals (e.g., iron, copper),

light, temperature, and enzymes. Lipids are two main types as saturated or unsaturated fatty

acids. The term „saturated‟ referring to the fact that all carbon atoms are bound to as many

hydrogens as possible whereas unsaturated fatty acids have one (mono-unsaturated) or more

(poly-unsaturated) double bonds between carbon atoms. The food products contain high levels

of unsaturated fats such as meat and meat products, dairy, fish and oils that are particularly

susceptible to oxidative reactions as oxygen is able to attack those double bonds and yield the

5

formation of free-radicals. The oxidations of lipids produce off-flavor and limiting the shelf-

life of lipids and lipid containing foods (Shahidi, 1997). The lipid oxidation is a major

economic problem in food industry because it makes products undesirable to consumer‟s

satisfaction. Food industries bear significant losses because of decreased product shelf-life

caused by lipid oxidation.

Oxidative rancidity is the lipid oxidation in which various kinds of fats produce oxidized

flavors in the presence of oxygen over time and can make a wide range of lipid-containing

products during storage period. It is the most important factor that decreases the shelf-life of

edible oils (Ryan et al., 2008). In addition, oxidative and hydrolytic rancidity are the major

reason of milk quality. Hydrolytic rancidity is the hydrolysis of triglycerides in the presence of

water and usually a catalyst such as lipoprotein lipase in milk and milk products. This lipase

releases the free fatty acids which contribute to the rancid, bitter and unpleasant taste in milk

and milk products (Gonzalez-Cordova and Vallejo-Cordoba, 2001). Lipid oxidations are not

only affecting off-lavor and odor development, but also have bad impact on food texture, color

and nutritive value of the products. The secondary products of lipid oxidation such as

malondialdehyde (MDA) and 4-hydroxynonenal (4-HN) are known to interact with amino

acids and proteins to produce undesirable products (Shahidi, 1997). In addition, textural

changes are caused by oxidized products to the oxidative induction of protein cross linking

(Kanner and Rosenthal, 1992). The oxidized products are also capable of destroying lipid-

soluble vitamins and essential fatty acids (Shahidi, 1997). Several key nutrients in milk are

destroyed by reason of light-induced lipid oxidation called photo-oxidation such as riboflavin

(Vitamin B2) and ascorbic acid (Vitamin C).

2.1.1. Mechanisms of Lipid Oxidation

The lipid oxidation can take place into three primary mechanisms: auto-oxidation,

photosensitized oxidation and enzyme catalyzed oxidation process. Auto-oxidation process is

extremely significance when it come contact to food. The auto-oxidation is a free-radical

mediated chain reaction whereby unsaturated fatty acids are attacked by molecular oxygen to

produce free radicals and a host of other oxidation products that negatively affect on texture,

taste, safety and nutritional quality of food products. The auto-oxidation happens into three

6

stages: initiation that is the formation of free radicals, propagation making free-radical chain

reaction and termination cause formation of non radical products which is shown in Figure. 1.

2.1.1.1. Initiation

The initiation is the formation of free radicals via a hydrogen atom generalization by oxidizing

agents. The oxidizing agents are singlet oxygen free radicals and transition metals. The

formation of a hydrogen atom from an unsaturated fatty acid by an initiator makes to the

generation of a lipid free radical (L•) and the L• quickly reacts with molecular oxygen to form

the lipid peroxyl radical (LOO•) in the products.

2.1.1.2. Propagation

The stages of propagation include the fast acceleration of the chain reaction started in initiation

stage. In propagation stage, the peroxyl radical abstract a hydrogen atom from another

unsaturated fatty acid and a lipid hydroperoxide (LOOH) and another L• are produced. The

hydroperoxides are highly unstable primary products of oxidation, but do not contribute to the

undesirable flavors and odors commonly associated with rancid food products. But due to their

instability, peroxides can continue in the chain reaction and are further degraded into

secondary reaction products such as aldehydes, ketones and acids. These secondary products of

oxidation are mainly responsible for off-odor and off-flavor development in oxidized food

products.

2.1.1.3. Termination

The termination stage involves in which free radicals start to react to one another to make more

stable and nonradical products, thus completing one cycle. There can be reinitiating causing the

cycle to repeat as (Shahidi, 1997).

Initiation : LH →L•

Propagation : L• + O2 → LOO•

LOO• + LH → LOOH + L•

Termination : LOO• + LOO• → non-radical products

LOO• + L• → non-radical products

L• + L• → non-radical products

Figure 1. Mechanism of auto-oxidation (Shahidi, 1997).

7

2.1.2. Photo-oxidation

The photo-oxidation involves in which oxidation occurs due to the reaction of a

photosensitizing agent with molecular oxygen in the presence of light in food products shown

in Figure. 2. Common photosensitizers in food products are riboflavin chlorophyll and food

dyes. In photo-oxidation, a photosensitizer (1S) absorbs ultraviolet light (hν) and reaches an

excited state (3S*) and the excited sensitizer is then able to shift that energy to triplet oxygen

atom, e.g. ground state oxygen (3O2), thereby producing the more extremely reactive singlet

oxygen (1O2). The electrophilic nature of singlet oxygen permits it to directly attack

unsaturated fatty acids and photo-oxidation take place at a much quicker rate than auto-

oxidation. Transparent packaging and colorful foods make ideal conditions for the contact of

food to light, thus raising the likelihood of oxidative damage products (Kanner and Rosenthal,

1992). The photo-oxidation in food primarily happens through the following mechanism route

(Cuppett et al., 1997).

1S + hν →1S* →3S*

3S + 3O2→ 1O2 + 1S (energy transfer)

1O2 + LH→ LOOH

Figure. 2 Mechanism of photo-oxidation (Cuppett et al., 1997).

2.1.3. Enzyme-mediated Oxidation

Lipids oxidation can also be an enzyme-mediated way in which endogenous enzymes catalyze

reactions that make to the generation of free radical. These enzymatic reactions are occurred by

superoxide radical anion (O2•-) along with hydrogen peroxide (H2O2. For example, the enzyme

superoxide dismutase, catalyzes a reaction that converts O2•- to H2O2 and O2 molecules.

During the Fenton reaction (shown below), metal ions such as iron react with H2O2 to produce

the highly reactive hydroxyl (OH•) radical and the hydroxyl radical can directly attack the

double bond in lipids to begin the process of lipid oxidation (Cuppett et al., 1997).

Fenton Reaction: Fe2+ + H2O2 → Fe3+ + OH• + OH-

8

2.2. Antioxidants

Antioxidants slow down lipid oxidation in foods and prevent the cardiovascular disease (CVD)

and cancer in human. In order to slow down lipid oxidation food industry currently uses a

variety of synthetic antioxidants including butylated hydroxytoluene (BHT), butylated

hydroxyanisole (BHA), tert-butyl hydroquinone (TBHQ) and propyl gallate (PG) in food

products. Natural antioxidants, α-tocopherol, vitamin C and rosemary extracts are used by

industry.

The reactions shown in Figure 3 are suggesting that an appropriate antioxidant can totally stop

lipid oxidation in food products. An antioxidant, AH, reacts with free radicals and neutralizes

them in the following mechanism involved (Cuppett et al., 1997).

L• + AH → LH + A• (1)

LO• + AH → LOH + A• (2)

LOO• + AH → LOOH + A• (3)

LR• + A• → LA (4)

LO• + A• → LOA (5)

Figure 3. Antioxidant (AH) reactions with free radicals generated during lipid

oxidation (Cuppett et al., 1997).

2.2.1. Synthetic antioxidants

The synthetic antioxidants are widely employed to increase the shelf-life of various food

products. Commonly used synthetic antioxidants in the food industry are BHT, BHA and

TBHQ which is shown in Figure. 4. For undesirable color changes the use of PG are limited in

food industry. The BHT and BHA are hydrophobic phenolic antioxidants that hinder free-

radical initiated chain reactions in foods. The defense against lipid oxidation may occur for the

formation of a BHT radical, which have a lower reduction potential than that of lipid peroxyl

radicals in foods. The BHA is commonly used in combination with BHT or PG which generate

a synergistic effect in reaction. The TBHQ is less volatile than BHA and BHT but is stable at

9

elevated temperatures also. For the stability of TBHQ at higher temperatures, it has established

to be more effective in polyunsaturated vegetable oil products (Patterson, 1989). Though

Synthetic antioxidants are extremely effective to slow down lipid oxidation, there have been

recent consumer concerns over possible adverse health effects associated with these products.

Many studies have showed that BHT and BHA cause a wide range of health trouble such as

enlarged liver, increased liver microsomal enzyme activity and make some ingested materials

into toxic and carcinogenic substances, especially if they are used in higher concentrations

(Rehman, 2003).

Figure 4. Chemical structures of some common synthetic antioxidants (Patterson, 1989)

2.2.2. Natural Antioxidants

Many researchers have been carried out to identify the sources of natural antioxidants that can

be used as an alternate of their synthetic antioxidants in recent years. The natural antioxidants

are recognized safe by consumers because they are naturally found in plant materials (Frankel,

1999). The natural antioxidants such as ascorbic acid, β-carotene and other carotenoids have

also been used in food products. The natural antioxidants not only decrease lipid oxidation in

food systems, but have also been shown to take part in a significant role in preventing a

number of chronic diseases including heart disease, Alzheimer‟s and Parkinson‟s diseases and

cancer (Chu et al., 2002; Tedesco et al., 2001; Weinreb et al., 2004; Youdim et al., 2002).

Antioxidant activity of plant extracts can be in large part credited to the existence of

polyphenolic compounds located within the plant tissue materials. The polyphenols are playing

a great deal of interest because their consumption in the diet may inhibit cancer, strokes and

10

neurological disorders. It is estimated that we consume about 1g of polyphenols per day

because of the most abundant antioxidants in our diets (Scalbert and Williamson, 2000).

Almost several thousands of natural polyphenols have been well-known in plants and plant

food materials. The polyphenolic compounds are present in high concentrations in a variety of

fruits, vegetables and beverages such as tea and wine products. They are also found in

agricultural byproducts such as jackfruit seeds, peanut skins, hulls and roots, grape seeds and

skins and in a number of herbs and spice products. The polyphenols are vital to plant growth

and development and give a protective mechanism against injury and infection (Karakaya and

Taş, 2001). Various polyphenolic compounds have been found to have a much higher

antioxidant properties than vitamins C and E and β-carotene within the same food products

(Chu et al., 2002). The flavonoids are the major class of polyphenolic compounds and can be

divided into several sub-classes such as flavanols (catechin and catechin gallate esters),

anthocyanidins and flavonols (quercetin, myricetin, kaempferol), flavanones and flavones

(luteolin) shown in Figure. 5. Every flavonoids consist of a 15-carbon (C6C3C6)

diphenylpropane skeleton structure. As the 15-carbon backbone have the form of two benzene

rings (A and B) connected to a third heterocylic ring called the C ring in structure. The

differences in substitution on ring C help to distinguish the different classes of flavonoid

compounds. The flavonols, for example, lack a carbonyl at the carbon-4 (C-4) position on the

C ring and the C-4 position in flavonols is occupied instead, by a keto group in structure. Most

familiar of the flavanols are the flavan-3-ols, (+)-catechin and (-)-epicatechin that are

recognized to give green tea some of its antioxidant activities.

The number, existence, placement and degree of substitution of hydroxyl groups on the

benzene ring gives much of the structural variation found in flavonoid compounds (Bohm,

1998). The flavonoids can act as free radical scavengers, singlet oxygen quenchers or metal

chelators, depending on their chemical structure which is shown in Figure. 5 and there is much

discusses in the literature in regards to which structural configuration give the highest degree

of antioxidant properties. It is assumed that the antioxidant activity of flavonoids can be

attributed to the hydroxyl groups positioned at the 3‟,4‟-OH of ring B and the 2,3-double bond

of ring C, and the ability to stop free radical chain reactions increases with the number of OH

groups on rings A and B in structure. The flavonoids can act as metal chelators by binding

metals at two points: the orthodiphenol grouping in ring B and the ketol structure in the C ring

11

of flavonol compounds. Therefore different metals show different properties with regard to

chelation by flavonoid compounds (Rice-Evans et al., 1996).

Figure 5. Chemical structures of common flavonoids found in plants (Bohm, 1998).

12

2.3. Extraction of polyphenols from plant materials

Naturally derived antioxidants to inhibit lipid oxidation in food products but standard

procedures for the extraction of these compounds from plant materials must be developed in

order to extend commercial uses. The researchers have developed a variety of extraction

procedures usually based on method. According to Waterman and Mole, 33 different extraction

procedures have been reported in recent plant biochemical literature review. Variation in these

procedures are extraction times ranging from 30 seconds to 96 hours and from 2 to 200 for

ratios of solvent volume to sample weight (Bohm, 1998). The main fact that one single plant

may contain up to several thousand secondary metabolites requires developing high

performance and rapid extraction methodology (Mandal et al., 2007). Optimization and

standardization of the extraction process is urgently needed to reduce time, energy and solvent

consumptions (Torres and Bobet, 2001).

2.4. Measuring antioxidant activity in food

In order to measure the antioxidant activity (AOA), there are a number of chemical assays that

have been developed. These assays are roughly divided into two main types depending on the

type of reaction that is involved: i) assays based on hydrogen atom transfer (HAT) and ii) assay

based on electron transfer (ET). The HAT-based assays are a competitive reaction scheme in

which the antioxidant and substrate compete for thermally generated peroxyl radicals (Huang

et al., 2005) and HAT-based assays include oxygen radical absorbance capacity (ORAC) and

total radical trapping antioxidant parameters (TRAP). Whereas ET-based assays is the capacity

of an antioxidant to reduce an oxidant and the oxidant changes color when reduced and the

degree of color change is correlated to the antioxidant concentration present in the sample

compounds. The ET-based assays are the Folin-Ciocalteu total phenols assay, Trolox

equivalence antioxidant capacity (TEAC), ferric ion reducing antioxidant power (FRAP) and

DPPH. The variety of the testing systems, methods and conditions employed for oxidation is a

major factor in the difficulty of interpreting the literature regarding antioxidant capacity of

natural antioxidants derived from plant extract materials (Frankel, 1999). The difficulty of the

topic of antioxidants coupled with the improper use of questionable methods has lead to a state

of confusion in the antioxidant research activities (Kinsella et al., 1993).

13

The antioxidants activity is not only dependent upon the chemical reactivity (e.g., free radical

scavenging and chelating) of the antioxidant but also on factors including physical location,

interaction with other food components and environmental conditions in food systems (Decker

et al., 2005). Also the results derived these chemical assays are valid only for the specified

reaction conditions employed in the assay, and those conditions are usually not accurate

representations of real food systemic environments. However the current methods of

measuring AOA there are several methods that have been used as industry standards when it

comes to assessing oxidative deterioration in food products. These assessment methods are

thiobarbituric acid reactive substances (TBARS) assay, peroxide value (PV), p-anisidine value

(pAV), active oxygen method, Rancimat tests and sensory analysis. TBARS and PV are the

most frequently used although there are some restrictions to both tests. Recently, the use of the

ORAC assay to determine oxidation in food has increased in popularity in food system.

Nevertheless, sensory analysis play the most reliable method as the task of assessing the

acceptability and preference of products is best carried out by customers.

2.5. Response surface methodology (RSM)

Response surface methodology (RSM) is a statistical method in which quantitative data used to

determine and solve the multivariate equations from suitable experimental designs. To

determine the interrelationships among the test variables and to describe the combined effect of

all test variables on the response these equations were graphically represented as response

surfaces which are used to describe how the test variables affected the response. The use of

RSM in any experiments or optimization process, will save cost, energy time, and identify the

caused of defects and also eliminated waste during production process. It is reported that many

food researches and product developments such as in bread formulation design, cookies and

also in development and optimization of baked goods formulation such as cake are performed

using response surface methodology (RSM) (Myers and Montgomery, 1995).

An experimental design is a general step to be applied in any experiments and RSM study.

Firstly, the experiment is design to find out the purpose of the study and determined the

responses and factors. The independent variables included processing conditions or

ingredients. Dependent variables or Responses measured can be chemical constituents such as

percent antioxidants, physical measurements such as viscosity, sensory scores, shelf life of a

14

product or microbiological stability results). Antioxidant extraction from plants or product

development is generally employed in two stages, namely screening and optimization process

(Dean and Voss, 1999).

2.5.1. Screening

Screening is the investigation of a great number of something looking for those with a

particular feature or problem. The aim of screening is to identify the critical control variables

from a collection of many potential variables so that the experiments will be more efficient and

fewer runs or tests required (Montgomery, 2005). It estimates the effect of each factor and

selects factors which produced a significant effect on the response variable for further testing.

For this purpose two level factorial and fractional factorial designs are employed (Myers et al.,

1989).

2.5.2. Factorial design

The factorial design is broadly employed in experiments involving several factors to examine

the interaction effects of the factors on a response or dependent variable by carry out all

possible combinations of levels and variable. During two level factorial designs, each variable

is studied at only two levels, called the (-) and (+) levels which is known as 2k factorial design

process. For 2k

design; only two factors (A and B) are involved and each run at two levels and

this design is called a 22 (4 factor combinations) factorial design process (Montgomery, 2005).

A plot of the experimental region tested in a 22 factorial is shown in Figure 6.

Figure 6. The 22 factorial design (Montgomery, 2005)

15

2.5.3. Fractional factorial design

The fractional factorial design is employed to investigate only a fraction of the factor

combinations in a full factorial design process. In fractional factorial design it does not

determine the interaction effects between factors but used to test only a fraction of the factor

such as a one half fraction of a 23 design is designated as a ½ 2

3 or 2

3 – 1 which have only four

factor combinations compared to eight combinations in factorial design process (Dean and

Voss, 1999).

2.5.4. Addition of central point to factorial design

The addition of replicated centre points in a 2k factorial design is to give a protection against

curvature and to obtain an independent estimate of error in design (Montgomery, 2005).

2.5.5. Blocking and randomization

Protection against known enemies is called blocking. It makes sure that the blocking variable

is as orthogonal as possible to all the predictive variables. As for example, design to compare

paints from 4 suppliers shown in Figure 7.

1 2 3 4

A C C D

C B A B

D A D A

B D B C

Figure 7. Blocking in design (Dean and Voss, 1999)

Protecting against unknown enemies is called randomization. The response can be affected by

factors unknown at the time of designing the experiment and even unknown after the analysis

such as time temperature and concentration. One of these gets seriously confounded with a

variable of interest can be happened. The randomization is the best weapon to prevent this

error.

Therefore grouping together experiments is known as blocking, which helped in preventing

experimental error, while randomization reduced the correlation with time in experiment (Dean

16

and Voss, 1999). As for example, when the 2k factorial design is replicated for n times then

each set of this design is considered as a block and each replicated of the design is run in a

separated block in design. Therefore the runs in each block were completed in random order

(Montgomery, 2005).

2.5.6. Analysis for screening experiment

During screening experiment, the first order model is constructed after evaluating the effects

and interactions as shown in Equation 2.1 in the case of two independent variables or factors

(Montgomery, 2005).

First order model: y = ß0 + ß1χ1 + ß2χ2 + e (2.1)

From the equation, y is the response, χ‟s represent factors, ß0 represents the y- intercept, ß‟s

are known as parameters and e is the residuals or error. After an analysis of residuals and

analysis of variance (ANOVA), when a model is built to evaluate how well the model

represented the data which consisted of percent of confidence, percent of variation and

coefficient of variation (CV) effect (Box et al., 2005).

2.5.7. Optimization

The aim of optimization is to determine the optimum levels of the factors studied. It included

both response surface methods and mixture experiments. Quantitative data is used to make an

empirical model that illustrated the relationship between the response and each factor

investigated.

During optimization experiments the model most often used was the full second order

polynomial model as shown in Equation (2.1) and (2.2) which including the interaction effects

between factors and curvature effects. Usually two or three the number of factors in response

surface method was used. Therefore, the model was used to evaluate the effects of each factor,

interactions between and among factors and curvature effects. Such as ß11χ12 is the Curvature

effect, produced parabolic shapes when the model was graphically represented. When two

different levels of the same factor produced similar values of response and higher or lower

responses at intermediate factor levels then these effects occurred (Box et al., 2005).

17

Second order model:

y = ß0 + ß1χ1 + ß2χ2 + ß11χ12 + ß22χ22 + ß12χ1χ2 + e (2.2)

From the equation, y is the response, ß0 represents the y-intercept and ß‟s was the regression

coefficient, χ1 represents the first factor, χ2 represent the second factor and e represents the

usual random error.

Full response surface- second order design is a design that allows the estimation of a full

quadratic model (Box-Behnken, and Central composite design). For three level factors, Box

and Behnken (1960) introduced designs that are widely used in response surface methods to fit

second-order models to the response and the designs are known as Box-Behnken designs. The

combination of two level factorial designs with incomplete block designs used to develop the

designs shown in Figure 8, the Box-Behnken for three factors designs.

Figure 8. Box-Behnken design for three factors show the geometric representation

(Box et al., 2005).

By the combination of 22design with a balanced incomplete block design having three

treatments and three blocks the design is obtained. The benefit of Box-Behnken designs is the

fact that they are all spherical designs and need factors at only three levels to be run (Box et al.,

2005).

The Central composite design (CCD) consists of three types of points: Star points or axial

points, the axial points are created by a Screening Analysis, Factorial points or cube points, the

18

cube points come from a Full Factorial design and Centre point, a single point in the center is

created by a nominal design shown in Figure 9 (Myers and Montgomery, 1995).

Figure 9. Factor combinations for a central composite design (Myers and

Montgomery, 1995)

The Central composite design (CCD) is widely used for fitting a second order method in

response surface method which consisted of four runs at the corners of the square, four axial

runs and four runs at the centre of this square that introduced by Box and William on 1957.

The model was established by the analysis of variance (ANOVA) to test the adequacy of the

model and the tests such as percent of variation, percent of confident, coefficient of variation

(CV), press and R2 value and „Root MSE‟ value. The model was described in a three

dimensional response surface plot and it represented a different response value and showed the

factors levels responsible for that response which provided an understanding of how the

experiment behaved when the factor levels were altered. A suitable model was selected when

R2 value was maximum and the „press‟ and „Root MSE‟ value was minimum. The coefficient

of variation (CV) value of the model should not exceeded than 10 % while the maximum R2

value was not less than 80 % which indicated that the confidence level of the chosen model

was not due to the experimental error and the model was significant (Montgomery, 2005).

19

CHAPTER III: MATERIALS AND METHODS

3.1. Chemicals

2,2-diphenyl-1-picrylhydrazyl (DPPH) and ferric chloride (FeCl3) were purchased from Sigma-

Aldrich Chemical Co. (USA). Folin-Ciocalteu Phenol-Reagent (FCR), ethanol (C2H6O),

sodium carbonate (Na2CO3), di-sodium hydrogen phosphate (Na2HPO4) were purchased from

Chem-Lab (Belgium). Sodium phosphate, monobasic di-hydrate (NaH2PO4.2H2O),

Tricholoroacetic acid (C2HCl3O2), Potassium ferricyanide K3[Fe(CN)6] were supplied by

Acros Organics (USA).

3.2. Preparation of materials

According to Jagadeesh (2007) the chemical composition of jackfruit depends upon the type of

cultivar. Therefore, one specific jackfruit variety named Khaja (firm one) was included in this

study. Whole matured fruits were provided by the Bangladesh Agricultural University

(Mymensigh, Bangladesh). On arrival, fruits were stored in a dry place until ripening. Upon

ripening the seeds were separated from the pulp and were peeled off. Peeled seeds were

lyophilized and hermetically stored at -20oC.

3.3. Extraction procedure

The lyophilized samples were milled to a very fine power by using a planetary ball mill

(Retsch PM 400, Germany) and strained with 0.3 mm strainer, in order to get rid of bigger

chunks. The extraction was executed at three stages. Approximately, 0.23 g of sample (wet

powder) was used for each extraction. For the first extraction, 24 full factorial screening design

(Table 2A) was used, where x1 ethanol (% ,v/v), x2 temperature (oC), x3 liquid-to-solid ratio

(ml/g DM), and x4 time (minute) were independent variables. However, in the second and third

phases, only three independent variables of x1 ethanol (%, v/v), x2 temperature (°C), x3 liquid-

to-solid ratio (ml/g DM) were used, utilizing the design of experiment of 23 full factorial with

center points (Table 3A) and Box-Behnken (Table 4A), respectively. The samples were always

vortexed well before and after the extraction. During the extraction procedure the samples were

kept as airtight as possible, in order to prevent evaporation losses. At the end of each

extraction, the samples were immediately cooled with ice water, and the extracts were filtered

20

using Whatman filter paper (Number 1). Filtrates were used to determine radical scavenging

property (DPPH), antioxidant reducing activity (FRAP) and total phenolic content (FCR) of the

extracts. The extracts yield after drying was precisely measured using four decimal electronic

balance for yields calculation and is expressed as percentage.

3.4. DPPH radical scavenging activity

The method of Brand-Williams (1995) was slightly modified for the DPPH radical scavenging

assay. An aliquot of 0.1 ml from the extract was mixed with 3.9ml of an 80% ethanolic 0.06

mM DPPH solution in a tube. The tubes were vortexed well and allowed to stand in the dark

for 30 minutes. Hereafter, the absorbance of the mixtures was measured at 515 nm by using a

Varian Cary 50 UV-Vis Spectrophotometer. A DPPH radical solution without adding the

aliquot was used as a control while Trolox was used as a standard. Standard samples were

prepared by a serial dilution of the ethanolic Trolox stock solution, and using the control

solution, the DPPH radical scavenging effect for the standard mix was calculated. The standard

curve (Figure 10 and Table 1) was prepared by fitting the percentage of radical scavenging

effect versus its corresponding Trolox concentration.

20 40 60 80

0.0

50

.15

0.2

50

.35

% Radical Scavanging

Tro

lox E

qu

. C

on

ce

ntr

atio

n (

mg

/ml)

Figure 10. The standard curve by fitting the percentage of radical scavenging

effectversus its corresponding Trolox concentration.

21

The results were expressed in mg Trolox equivalent per 100g dry matter sample (mg TE/100 g

DM). The DPPH radical scavenging effect was calculated using the following equation 3.1:

DPPH radical scavenging effect (%) 1001

AbsorbanceAbsorbance

CONTROL

SAMPLE (3.1)

3.5. The total phenolic compounds (FCR)

To measure the total phenolic compounds of the extracts the modified version of the FCR

assay (Matthäus, 2002) was used. 100µl of aliquot was mixed with 2 ml of 2% Na2CO3 in a

tube and incubated for 2 minutes; afterwards 100µl of Folin-Ciocalteu Phenol-Reagent (diluted

with distilled water 1:1) was added. The mixtures were vortexed well and incubated in the dark

for 90 minutes at 25oC. Hereafter the absorbance was measured at 750 nm wavelength by using

a Varian Cary 50 UV-Vis spectrophotometer. A blank sample was prepared by adding 100µl of

distilled water instead of the aliquots. Gallic acid (GA) was used as a standard and a serial

aqueous dilution of Gallic acid solution was prepared. The standard curve (Figure 11 and Table

1) was fitted by plotting absorbance versus the corresponding concentration of Gallic acid

solutions. The results were expressed in mg Gallic acid equivalent/100g dry matter (mg GAE/

100g DM).

0.1 0.2 0.3 0.4 0.5 0.6 0.7

0.0

00

.10

0.2

00

.30

Absorbance at 750 nm

Ga

lic A

cid

Co

nce

ntr

atio

n (

mg

/ml)

Figure 11. The standard curve fitted by plotting absorbance versus the

correspondingconcentration of Gallic acid solutions.

22

3.6. Ferric reducing antioxidant power (FRAP)

The total antioxidant activity of the extracts was measured by using a modified method of

measuring ferric reducing antioxidant power as described by Oyaizu (1986). An aliquot of

0.3ml of ethanolic extract was added and vortexed with 0.85ml of 0.2 M phosphate buffer (pH

6.6) and 0.85ml of potassium ferricyanide (1%). After incubating the mixture at 50oC for 20

minutes, 0.85ml of trichloroacetic acid (10%) was added and vortexed well. Finally, 2.85ml of

distilled water and 0.57ml of FeCl3 (1%) were added and the mixture was incubated at 25oC for

30 minutes. After the second incubation, absorbance was measured at 700nm by using a Varian

Cary 50 UV-Vis spectrophotometer. A blank was prepared in parallel, where distilled water

was added instead of the aliquot. The standard ascorbic acid was prepared by a serial aqueous

dilution of stock solution. The standard curve (Figure 12 and Table 1) was prepared by fitting

the absorbance versus its corresponding standard ascorbic solutions. The results were

expressed as mg ascorbic acid equivalent antioxidant capacity/100g dry matter (mg AA/100g

DM).

0.5 1.0 1.5 2.0 2.5 3.0

0.1

0.2

0.3

0.4

0.5

Absorbance at 700 nm

Asco

rbic

Acid

Co

nce

ntr

atio

n (

mg

/ml)

Figure 12. The standard curve by fitting the absorbance versus its

corresponding standard ascorbic solutions.

23

Table 1. Regression coefficients and coefficient of determination of standard curves

Legend: a

Standard curves were fitted by least square linear regression

3.7. Experimental design

Response surface methodology (RSM) was applied to optimize the extraction process.

Software package R (foundation for statistical computing, version 2.14.1) was used for the

design of the experiments and statistical data analysis (ANOVA). The whole experiment was

approached in several key steps, which are outlined in Figure 13 and explained in detail in the

following sections.

Figure 13. A flow diagram of the overall optimization of the process

Standard curvea Intercept Slope Coefficient of

determination

(R2)

1. Radical scavenging Vs mg of Trolox -0.0166 0.00402 0.994

2. Absorbance Vs mg of Gallic acid -0.0171 0.50284 0.997

3. Absorbance Vs mg of Ascorbic acid 0.0070 0.16615 0.996

Identifying variables

Fitting first-order model

Fitness

check

Fitting second-order

model

Optimization by

desirability function

Determination of optimum

set of operational condition

Ok

Not ok

Fitness

check

Ok

Not ok

Screening of variables

Desig

n m

od

ific

atio

n

24

3.7.1. Variable identification and screening

Many factors influence the extraction efficiency (Prasad et al., 2009). The four most promising

variables, namely, ethanol concentration, temperature, liquid-to-solid ratio and time were

initially selected. After a factorial screening test, only ethanol concentration, temperature and

liquid-to-solid ratio were identified as the most influencing variables. The experimental design

and results of the four factorial screening tests is presented in Table 2A and Table 2B,

respectively.

Table 2A. A full 24 factorial design experimental design and corresponding responses for an

ethanolic extraction with x1=ethanol concentration (%, v/v), x2=temperature (oC), x3=liquid-to-

solid ratio (ml/g DM), x4=time (minute); DPPH=radical scavenging property (mg TE/100 g

DM); FCR=total phenolic content (mg GAE/100 g DM); FRAP=ferric reducing antioxidant

property (mg AA/100 g DM).

Run No. x1 x2 x3 x4 Response1

DPPH

Response2

FCR

Response3

FRAP

1 -1 (55) -1 (40) -1 (25) -1 (10) 503.399 610.501 374.305

2 +1 (90) -1 (40) -1 (25) -1 (10) 732.546 821.065 470.092

3 -1 (55) +1 (70) -1 (25) -1 (10) 199.592 388.246 282.006

4 +1 (90) +1 (70) -1 (25) -1 (10) 173.749 260.023 219.657

5 -1 (55) -1 (40) -1 (25) +1 (40) 263.914 441.924 333.847

6 +1 (90) -1 (40) -1 (25) +1 (40) 684.017 664.682 478.027

7 -1 (55) +1 (70) -1 (25) +1 (40) 242.521 485.169 297.542

8 +1 (90) +1 (70) -1 (25) +1 (40) 491.482 601.701 336.048

9 -1 (55) -1 (40) +1 (55) -1 (10) 862.680 1011.782 594.918

10 +1 (90) -1 (40) +1 (55) -1 (10) 912.271 1313.234 624.070

11 -1 (55) +1 (70) +1 (55) -1 (10) 329.181 527.247 362.619

12 +1 (90) +1 (70) +1 (55) -1 (10) 702.536 684.887 524.461

13 -1 (55) -1 (40) +1 (55) +1 (40) 605.249 738.540 464.238

14 +1 (90) -1 (40) +1 (55) +1 (40) 844.992 817.360 584.500

15 -1 (55) +1 (70) +1 (55) +1 (40) 161.772 520.333 344.159

16 +1 (90) +1 (70) +1 (55) +1 (40) 501.019 723.329 498.233

25

Table 2B. Experimental results of full factorial (24) screening design to identify most

influencing factors

Variables DPPH FCR FRAP

Ethanol concentration (%, v/v) 0.01680* 0.02161* 0.02200*

Temperature (oC) 0.00445** 0.00147** 0.00378**

Liquid-to-solid ratio (ml/g DM) 0.02797* 0.00207** 0.00215**

Time (minute) 0.29528 0.13729 0.60232

Legend: *p<0.05,**p<0.01,***p<0.001

3.7.2. Fitting a first-order model

An experimental design of two level three factorial (23), with three replications at the center

(Table 3A) was used to check the fitness of the experimental data in a first-order model. The

fitness was checked for three responses, namely DPPH radical scavenging, FCR and FRAP.

The data in Table 3B and Figures 14 were used as a tool to check the model fitness.

Table 3A. A full 23 factorial design experimental design with three replication at the center

and corresponding responses for an ethanolic extraction with x1=ethanol concentration (%,

v/v), x2=temperature (oC), x3=liquid-to-solid ratio (ml/g DM); DPPH=radical scavenging

property (mg TE/100 g DM); FCR=total phenolic content (mg GAE/100 g DM); FRAP=ferric

reducing antioxidant property (mg AA/100 g DM).

Experimental

Runs

x1 x2 x3 Response

1 DPPH

Response

2 FCR

Response

3 FRAP

1 -1 (55) -1 (45) -1 (35) 446.197 461.356 368.174

2 +1 (80) -1 (45) -1 (35) 736.080 654.245 542.869

3 -1 (55) +1 (75) -1 (35) 274.152 293.986 301.938

4 +1 (80) +1 (75) -1 (35) 462.537 339.920 390.855

5 -1 (55) -1 (45) +1 (55) 615.199 549.372 535.610

6 +1 (80) -1 (45) +1 (55) 802.700 644.232 604.422

7 -1 (55) +1 (75) +1 (55) 588.357 462.531 488.090

8 +1 (80) +1 (75) +1 (55) 561.127 306.274 483.064

9 0 (67) 0 (60) 0 (45) 643.069 556.741 521.968

10 0 (67) 0 (60) 0 (45) 642.750 581.632 541.034

11 0 (67) 0 (60) 0 (45) 613.150 522.573 506.565

26

Table 3B. Coefficient of determination (R2) and lack of fit values to evaluate the linear fitted

models with the experimental data of Table 3A; where DPPH=radical scavenging property (mg

TE/100 g DM); FCR=total phenolic content (mg GAE/100 g DM); FRAP=ferric reducing

antioxidant property (mg AA/100 g DM).

Properties Predicted value

DPPH

Predicted value

FCR

Predicted value

FRAP

Coefficient of determination

(R2)

0.943 0.8827 0.8994

p value of lack of fitness test 0.02499* 0.04665* 0.03731*

Legend: *p<0.05,**p<0.01,***p<0.001

��

300 500 700 900

-2-1

01

2

(a)

Fitted values

Sta

ndard

resid

uals

300 400 500 600 700

-2-1

01

2

(b)

Fitted values

Sta

ndard

resid

uals

300 400 500 600 700

-2-1

01

2

(c)

Fitted values

Sta

ndard

resid

uals

Figure 14. Standard residual plots of the three linear fitted models, where responses are (a)

DPPH, (b) FCR and (c) FRAP.

3.7.3. Fitting second-order model

Second-order models are mostly used for optimization purposes related to food products

(Granato et al., 2010). In this study, the authors‟ primary interest was to fit a second-order

model by using a central composite rotatable design (CCRD), because of its interesting ratable

property. Moreover, it would have allowed reusing experimental data of 23 factorial designs,

with eight more additional experimental runs (six for axial points and two extra runs at the

center point). But since the axial (star points) runs resulted in a number of outliers in the

model, another widely used design, namely Box-Behnken, was used as an alternative in the

27

study. The three variables identified in the screening test were used as independent variables,

while type of responses kept same as in earlier experiments. Selections of ranges for these

independent variables were based on the earlier experimental data and results. The

experimental design (inputs and responses) for the construction of quadratic models, and the

predicted outputs are presented in the Table 4A and Table 4B, respectively. The coefficient of

determination (R2), lack of fit test (p values), and standard residual plots presented in the Table

4B and Figure 15, respectively, were used as the tools for model fitness check. Finally, these

second-order models were validated (Table 6) in order to demonstrate their predictive

adequacy.

Table 4A. Box-Behnken design and corresponding responses for an ethanolic extraction with

x1=ethanol concentration (%, v/v), x2=temperature (oC) and x3=liquid-to-solid ratio (ml/ g

DM); DPPH=radical scavenging property (mg TE/100 g DM); FCR=total phenolic content

(mg GAE/100 g DM); FRAP=ferric reducing antioxidant property (mg AA/100 g DM)

Run No. x1 x2 x3 Response

1

DPPH

Response

2

FCR

Response

3

FRAP

1 -1 (60) -1 (35) 0 (47.5) 924.090 971.740 618.208

2 +1 (90) -1 (35) 0 (47.5) 1010.474 872.857 573.617

3 -1 (60) +1 (60) 0 (47.5) 388.300 531.303 338.821

4 +1 (90) +1 (60) 0 (47.5) 898.352 744.976 485.618

5 -1 (60) 0 (47.5) -1 (35) 629.246 789.982 393.355

6 +1 (90) 0 (47.5) -1 (35) 787.721 807.360 405.800

7 -1 (60) 0 (47.5) +1 (60) 737.156 828.609 541.833

8 +1 (90) 0 (47.5) +1 (60) 993.506 967.091 522.293

9 0 (75) -1 (35) -1 (35) 925.705 925.803 533.739

10 0 (75) +1 (60) -1 (35) 633.823 690.676 349.856

11 0 (75) -1 (35) +1 (60) 1065.628 985.797 650.200

12 0 (75) +1 (60) +1 (60) 756.961 806.585 531.764

13 0 (75) 0 (47.5) 0 (47.5) 919.060 924.976 591.927

14 0 (75) 0 (47.5) 0 (47.5) 951.573 959.559 533.840

15 0 (75) 0 (47.5) 0 (47.5) 931.190 928.031 588.612

16 0 (75) 0 (47.5) 0 (47.5) 900.423 963.858 589.230

17 0 (75) 0 (47.5) 0 (47.5) 891.193 936.390 584.192

28

Table 4B. Regression coefficients, the coefficient of determination (R2), lack of fit values for

the second order fitted models. Predicted values are DPPH=radical scavenging property (mg

TE/100 g DM); FCR=total phenolic content (mg GAE/100 g DM); FRAP=ferric reducing

antioxidant property (mg AA/100 g DM)

Constants Predicted

DPPH

Predicted

FCR

Predicted

FRAP

Intercept ao 918.688*** 942.563*** 577.560***

Concentration a1 126.408*** 33.831** 11.889

Temperature a2 -156.057*** -122.832*** -83.713***

Liquid-to-solid Ratio a3 72.095*** 46.783** 70.417***

Concentration2 a11 -86.003*** -83.149*** -62.032**

Temperature2 a22 -27.381 -79.195*** -11.463

Liquid-to-solid Ratio2 a33 -45.777* -11.153 -49.708**

Concentration×Temperature a12 105.917*** 78.139*** 47.847**

Temperature×Liquid-to-solid Ratio a23 -4.196 13.979 16.362

Concentration×Liquid-to-solid Ratio a13 24.469 30.276* -7.996

Coefficient of determination (R2) 0.9849 0.9812 0.9676

p value of lack of fit test 0.1802 0.1384 0.4191

Legend: *p<0.05,**p<0.01,***p<0.001

400 600 800 1100

-2-1

01

2

(a)

Fitted values

Sta

ndard

resid

uals

500 700 900 1100

-2-1

01

2

(b)

Fitted values

Sta

ndard

resid

uals

350 450 550 650

-2-1

01

2

(c)

Fitted values

Sta

ndard

resid

uals

Figure 15. Standard residual plots of the three quadratic models, where responses

are (a) DPPH (b) FCR and (c) FRAP

29

3.8. Correlation between phenolic content and antioxidant activities

Seeds are a particularly rich source in antioxidant and phenolic compounds (Lu and Foo, 1999;

Meyer et al., 1998). It was reported that changes in antioxidant activity in different seeds were

largely due to the changes in its phenolic contents (Soong and Barlow, 2004; Yen and Chuang,

2000). In this study, all experimental responses (Table 2A, Table 3A and Table 4A) of DPPH

and FRAP were compared separately, with their corresponding FCR values, in order to

evaluate their correlations.

3.9. Statistical analysis

The well-known software package R version 2.14.1 (foundation for statistical computing) was

used to conduct the experimental design as well as the statistical analysis. Data were analyzed

by multiple regression using the least-square method. First-order polynomials were used for

both full factorial (24) screening test and the successive evaluation of linear fitness for full

factorial (23) design experiment, with three runs at the center. The variables (factors) that were

found to be significant (p≤0.05) in the screening test were only taken into account for the later

experiments. The first-order polynomial equation 3.2 that was used is as follows:

(3.2)

Upon failure to fit with the first-order models, the experimental data were fitted with the

second-order polynomial equations 3.3 using Box-Behnken design as follows:

(3.3)

Where, yr represents the measured responses variables, while xi and xj are the levels of

independent variables. ao is a constant (predicted response at the center), ai, ,aii and aij are the

linear, quadratic and two factor interactive coefficient of the model, respectively. All the tests

of statistical significance were based on the total error criteria with a confidence level of 95%.

30

CHAPTER IV: RESULTS AND DISCUSSION

4.1. Sample

Jackfruit seed was selected in this study, in order to valorize this non-toxic and edible part of

the fruit. Comparing with other tropical fruits, jackfruit contains a higher amount of essential

nutrients (Bhatia et al., 1955; Haq, 2006). Again, the seeds also contain significantly higher

amount of antioxidant and phenolic compounds compared to pulp (Soong and Barlow, 2004).

We found average 11.36 % yield of the seed extract on the basis of dry weight is shown in

appendix Table 1.

4.2. Standard curves

Crude extracts from seed contain numerous substances that exhibit antioxidant activity (Vattem

et al., 2005). In this study, antioxidant properties of extracts were measured by

spectrophotometry. Therefore, standard curves were used to characterize the measured

absorbance into a meaningful unit. The high coefficient of determinations (0.994<R2)

presented in Table 1, demonstrates that all standard curves were fitted very well. Thus, using

these standard curves, absorbance data measured for DPPH, FRAP and FCR were successfully

translated into mg TE/100 g DM, mg AA/ 100 g DM and mg GAE/ 100 g DM, respectively.

4.3. Factors screening and identification

Many factors influence the extraction process (Prasad et al., 2009). In order to identify the

most important among them, researchers often use full or fractional factorial design, depending

on the number of factors considered (Kliemann et al., 2009; Li-Hsun et al., 2004). In this study,

four independent variables were initially selected, and a screening test was carried out by using

24 full factorial experimental design as shown in Table 2A. The results (Table 2B) illustrated

that the influence of time (minute) was negligible (0.05<p) within the selected range, compared

with ethanol concentration, temperature and liquid-to-solid ratio. This result might be due to

the combined effect of high surface to volume ratio and the porous structure of the freeze-dried

powder. Porous powder particles from the lyophilization process resulted in a rapid penetration

of solvent in the solid matrix due to the capillary action. While on the other hand the high

surface to volume ratio allowed the exchange of mass from solid matrix to solvent quick

31

enough, eventually those might have rendered the time insignificant. Based on this result, the

extraction time was fixed at 10 minutes (-1 coded), and only three other factors were

considered for further experiments.

4.4. Fitting models

The three most important independent variables: ethanol concentration (x1), temperature (x2),

and liquid-to-solid ratio (x3), as identified from the screening test, were used to construct

models for optimization. A 23 full factorial design with three replications at the center was

used, in order to fit experimental data by linear models. However, it is apparent from the Table

3B that none of the three models fitted well with experimental data (p<0.05). Moreover, Figure

14 provided enough evidence of curvature at the middle of each model. This nonlinear

behavior can caused by the intricate orientation of the food matrix and diversely embedded

bioactive chemical occupant in it. Perhaps this is the reason why, second-order models are

mostly used for food development and optimization purposes (Prasad et al., 2011; Prasad et al.,

2009; Saha et al., 2011; Wijngaard et al., 2011; Wijngaard and Brunton, 2010; Yang et al.,

2009). The failure to fit experimental data with linear models led to more complex second

order polynomial models, in order to get a better approximation of the curvatures (Figure 14)

from earlier analyses. For second order models, the experiments were designed according to

the Box-Behnken design, and both the coded and uncoded variables used in this RSM design

are listed in Table 4A. Ranges for these individual variables were selected based on

preliminary experimental results.

Each of the multiple regression equations was generated using the coded values of in the

independent variables and the corresponding responses. The comparative value of these

multiple regression coefficients were used to determine the influence of three independent

variables towards the responses. Table 4B summarizes the regression coefficients and the result

of ANOVA - the significance of the coefficients of all models. The coefficients of these second

order polynomial models were also used in prediction of optimum level.

4.4.1. Response surface analysis of radical scavenging property (DPPH)

The percentage radical scavenging effect of the extract was measure by DPPH value. The

results in Table 4B demonstrate that the scavenging property of the ethanolic extract was in

quadratic relationship with the extraction parameters, based on satisfactory coefficient of

32

determination (0.98≤R2) and lack of fit test (0.18≤p). The randomized distribution of standard

residuals (±2) around zero (Figure 15a) further confirmed the model fitness and absence of

outliers.


concentration and temperature at fixed 55.01 ml/g DM liquid-to-solid ratio, (b) liquid-

to-solid ratio and temperature at fixed 72.19 % ethanol concentration, (c) ethanol

concentration and liquid-to-solid ratio at fixed 35 oC temperature on radical scavenging

activity measured by DPPH (mg TE/100 g DM).

33

The radical scavenging property of the extracts measured by DPPH was significantly affected

by all the linear factors (a1, a2, a3), together with quadratic factors of concentration (a11),

liquid-to-solid ration (a33), as well as the interactive factor of concentration and temperature

(a12). Figure 16a illustrates the effect of ethanol concentration and temperature on DPPH at the

fixed liquid-to-solid ration of 55.01 ml/g DM. Where, with the increase of ethanol

concentration, DPPH values also increased and reached a maximum level and dropped again.

Apparently, polarity played an important role; the increase of ethanol concentration in the

solvent caused a decrease in its polarity, which favored the extraction of less polar

components, that is, seemingly those who contributed to improve of DPPH values. Beside this,

increase of ethanol concentration promoted the breakdown of cell membrane that enhance the

permeability of the solvent into the solid matrix (Vatai et al., 2009; Zhang et al., 2006).

Nevertheless, at a very high ethanol concentration the polarity dropped low enough, which was

probably not ideal for the extraction. On the other hand coagulation of native proteins present

in the solid matrix, might have also inhibited the diffusion of compounds into the solvent

(Vatai et al., 2009; Zhang et al., 2006) and resulted in a decrease of DPPH value at a higher

ethanol concentration.

Temperature had a negatively effect on the response DPPH. As it can be seen from Figure 16a

and Figure 16b the DPPH value linearly drops along the temperature axis, which indicates

compounds contained in the extract were mostly heat sensitive. However, because of the

twisted of surface (Figure 16a) produced by temperature with an interaction with

concentration, the DPPH value increased away from that axis. The possible explanation could

be that, although increasing temperature might have accelerated chemical degradation of

bioactive compounds, high temperature also decreased the dielectric constant of the solvent

that could favor the extraction of less polar compounds.

When liquid-to-solid ratio increased, the DPPH value of the extract also increased and reached

a plateau, but in further increase, a slight decrease in DPPH value was observed in Figure 16c.

A reasonable explanation could be that more solvent required higher amount of bioactive

compounds to be saturated, and in a way high liquid-to-solid ratio drove more mass flow from

the solid matrix to the solvent. However, a higher dilution of bioactive compounds resulted in

more mobility of their chemical degradation process as well, that was probably why DPPH

34

value decreased at the higher liquid-to-solid ratio. This result was also consistent with the

studies of Prasad et al. (2011).

4.4.2. Response surface analysis of total phenol content (FCR)

The total polyphenol content of the extract was measured by FCR value. Data presented in

Table 4B testify to (0.98<R2 and 0.138<p) a quadratic relationship between the extraction

parameters and the extracts‟ FCR values. A standard residual plot (Figure 15b,) also confirmed

the fitness of model and lack of any outliers.

The response surface plot in Figure 17a (see next page) illustrates the effect of ethanol

concentration and temperature on the response value of FCR. The effect of linear (a1) and

quadratic (a11) terms of the ethanol concentration and its interaction with temperature (a12)

were significantly (p<0.05) influencing polyphenol extraction (Table 4B). The effect of ethanol

concentration on FCR was very similar that was observed in the section 4.5 for DPPH, hence

the extraction mechanism might be explained in the same way.

Both the linear (a2) and the quadratic (a22) effects of temperature were significantly (Table 4B)

affecting phenolics extraction. Figure 17a indicates that the FCR value slightly tends to

increase in the beginning with temperature, however, it quickly decreased again, when

temperature was further increased. This was agreed by Richter et al. (1996), who found slight

increase of temperature can improve the phenolic content in extraction through the increase in

phenolic solubility, diffusion rate, and reduced solvent viscosity and surface tension. However,

a further increase in temperature decreased the phenolics content, possibly caused by thermal

degradation and interference of compound solubility through chemical or enzymatic

degradation with or without other plant compounds (Kiassos et al., 2009). In this case, the

temperature degradation was predominant, which indicates that the extract contained a

majority of heat sensitive phenolic compounds.

The liquid-to-solid ratio showed only a linear (a3) positive impact (Table 4B) on the extraction

of phenolic compounds. Figure 17b shows that the FCR value increased with the increase of

liquid-to-solid ratio and ends-up forming a plateau eventually. This behavior was in good

agreement with Prasad et al. (2009), who explained that when liquid-to-solid ratio increased

more solvent could enter in the cells and allowed more phenolic compounds to permeate into

the extract.

35

Figure 17. Response surface plot showing the combined effect of (a) ethanol concentration and

temperature at fixed 55.01 ml/g DM liquid-to-solid ratio, (b) liquid-to-solid ratio and

temperature at fixed 72.19 % ethanol concentration, (c) ethanol concentration and liquid-to-

solid ratio at fixed 35 oC temperature on phenolic content measured by FCR (mg GAE/100 g

DM).

36

This allows us to choose any value of liquid-to-solid ratio close to its maximum limit or

beyond, but one should avoid the use of unnecessary excess use of solvent in optimization of a

process, considering the economical aspect.

4.4.3. Response surface analysis of antioxidant activity (FRAP)

FRAP value was used as a measure of antioxidant activity. The influence of three independent

variables toward FRAP value of the extract was successfully modeled with a second-order

polynomial equation as presented in the Table 4B (0.97<R2 and 0.41<p). Random distribution

of standard residuals on either side of zero within the limit of ±2 as can be seen in Figure 15c,

which also demonstrated goodness of fit without outliers.

Ethanol concentration showed a weak positive linear (a1) effect on FRAP value. However, the

quadratic (a11) and its interaction effect with temperature (a12) were found significant (Table

4B and Figure 18a). Apparently, this result indicated, that the ideal ethanol concentration was

somewhere in the middle of experimented range. Moreover, temperature affects the polarity of

the solvent by changing the dielectric constant; this might have significantly influenced the

extraction of phenolic compounds by interacting with solvent‟s ethanol concentration.

The significance of temperature (a2) and liquid-to-solid ratio (a3 and a33) on FRAP (Table 4B)

and Figure 18c were very much similar as shown in the section 4.5 for DPPH, hence the

extraction mechanism can probably be explained in the same way.

37

Figure 18. Response surface plot showing the combined effect of (a) ethanol concentration and

temperature at fixed 55.01 ml/g DM liquid-to-solid ratio, (b) liquid-to-solid ratio and

temperature at fixed 72.19 % ethanol concentration, (c) ethanol concentration and liquid-to-

solid ratio at fixed 35 oC temperature on phenolic content measured by FRAP (mg AA/100 g

DM).

38

4.5. Optimization and verification of the models

The major aim of this study was to maximize the antioxidant activity in the extracts. Both the

DPPH and FRAP assays characterize the antioxidant property of the extracts. However, two

different sets of optimum conditions were derived, when they were optimized (maximized)

separately as can be seen from Figure 19. So in order to take both into account, we used the

simultaneous optimization technique. There are two approaches, which are used most

frequently in RSM optimization. The first one is the superimposition of contour plots of

responses and the manual deduction of the desirable value. But according to Granato et al.

(2010) this graphical approach is inefficient and cannot be automated. Therefore, the second

approach of simultaneous optimization using desirability function was used.

During the optimization process both responses of DPPH and FRAP were maximized with the

given same preferences as presented in Table 6. Figure 19 is showing the superimposed

contour plots for both DPPH and FRAP at temperature of 35 oC, where the small cross mark

represents the optimum condition. During this simultaneous optimization the desirability

values of 0 was assigned for the minimum to 1 for the maximum responses for every model.

Hereafter, the optimum condition showed an overall desirable property of 0.984; where,

individual desirability values were obtained 0.993 and 0.975 for responses of FRAP and

DPPH, respectively.

The predictive quality of every model was also tested at the recommended optimum condition.

All the responses were replicated five times at the optimum condition, and the results are

presented in Table 6. The arithmetic mean of the experimental values was 1003.22 mg TE/100

g DM, 1031.68 mg GAE/100 g DM and 679.18 mg AA/100 g DM for DPPH, FCR and FRAP,

respectively. Experimented data were approaching the predicted values. Deviations between

experimental values and the predicted values can be explained by the lack of perfectly fitted

models and experimental errors.

39

Table 5. Maximum predicted values from the second order fitted models. Responses are

DPPH=radical scavenging property (mg TE/100 g DM); FCR=Total phenolic content (mg

GAE/100 g DM); FRAP=Ferric reducing antioxidant property (mg AA/100 g DM).

Responses Independent variables Predicted

value

(maximum)

Ethanol concentration

(v/v %)

Temperature

(oC)

Liquid-to-Solid

ratio (ml/g DM)

DPPH +0.25 (78.71) -1.00 (35.00) +0.90 (58.75) 1084.20

FCR +0.08 (76.22) -0.65 (39.41) +1.00 (60) 1016.02

FRAP -0.33 (70.10) -1.00 (35.00) +0.57 (54.63) 671.09

Table 6. Experimental data for verification of the models predicted at optimal condition with

x1=ethanol concentration (v/v %), x2=temperature (oC), x3=liquid-to-solid ratio (ml/g DM);

Responses are DPPH=radical scavenging property (mg TE/100 g DM); FCR=Total phenolic

content (mg GAE/100 g DM); FRAP=Ferric reducing antioxidant property (mg AA/100 g

DM).

Optimal

condition

Response 1 DPPH Response 2 FCR Response 3 FRAP

Predicted Experimentala Predicted Experimental

a Predicted Experimental

a

x1=72.16 %

x2=35 oC

x3=55.02

(ml/g DM)

1066.96

1003.22±8.29

1003.85

1031.68±9.17

669.83

679.18±14.09

Legend: a All experiments were repeated five times

40

Figure 19. Superimposed contour plots of responses DPPH and FRAP as a

function of liquid-to-solid ratio and ethanol concentration at temperature 35 oC.

4.6. Role of polyphenols as antioxidant

Fruit seeds contain a significantly higher proportion of phenolic compounds then of the edible

portion (Soong and Barlow, 2004). In this study, the antioxidant activities of jackfruit seeds

were compared with its total phenolic content. The antioxidant activity of jackfruit seeds was

in high agreement with the amount of phenolics. At different combinations of extraction

parameters (Table 2A, Table 3A and Table 4A), FCR values were compared with DPPH and

FRAP values using the Pearson correlation coefficient (r). The following correlation

coefficients (r) were obtained 0.89, 0.85 and 0.88 with comparison between FCR and DPPH,

meanwhile for FCR and FRAP it was 0.91, 0.81 and 0.86 using the dataset from Table 2A,

Table 3A and Table 4A, respectively. This high correlation indicates that the majority of

antioxidant property of jackfruit seed was due to its phenolic content. Data of Table 5 and 6

also support this assumption, because optimization was done by maximizing DPPH and FRAP

41

values; however, at the optimum condition FCR values were also approaching to the maximum

predicted value, together with DPPH and FRAP value. This result was in agreement with

(Vattem et al., 2005), who reported that seeds were comparatively richer in phenolic

compounds, which was probably because of the antioxidant activities of the phenolics aid in

protection of healthy propagation of the species.

42

CHAPTER V: CONCLUSION AND FURTHER RECOMMENDATION

5.1. Conclusion

In this study, all models developed by using RSM technique showed an adequate predictive

quality. The optimal condition was predicted by desirability function and successively verified.

During this simultaneous optimization process our main aim was to achieve a maximal

antioxidant activity as measured by both DPPH and FRAP. From the optimisation method, the

optimum conditions for maximum antioxidant activity were as follows: ethanol 72.16 %,

temperature 35 oC and liquid-to-solid ratio 55.02 ml/g DM. However, these models can be

used further, in order to achieve desired responses, where one can take into account economic

aspects and/or available installations. Taking into consideration that jackfruit seeds are

discarded as waste and not effectively utilized, these in vitro results suggest the possibility that

waste could be effectively used to alleviate oxidative stress as an ingredient in health or

functional food. As a consumer demands for healthier products containing fewer synthetic

additives are increasing so phenolic compounds derived from agricultural by-products such as

jackfruit seeds may be a viable alternative to synthetic antioxidants in food industry. Though,

efficient, cost-effective procedures for the extraction of these natural antioxidant compounds

need to be developed in order to meet commercial demand. Thus great value can be added to

what would otherwise be considered as waste products. Further research is needed to develop

of alternative extraction procedures that serve to reduce extraction time and solvent

consumption while maximizing the recovery of phenolic compounds present in the jackfruit

seeds. Now that the extraction process has been optimized, future research should be

concentrated on the antioxidants‟ performance in food product. Beside this, profiling of major

phenolic and antioxidant compounds can also be worthwhile in understanding their role as

functional components.

43

5.2. Further recommendation

In future work the extracts should be conducted to distinguish, isolate and purify the extracted

phenolic compounds for the benefit of consumers as food supplements and other industrial

uses. It is recommended that the extracts be fractionated to determine which compounds are

primarily responsible for the antioxidant activity. In recent years, supercritical fluids extraction

(SFE) is the most extensively studied for the extraction of natural compounds. In fact SFE has

direct advantages over traditional extraction technique. Traditional extraction procedure has

some drawback, time consuming, laborious, have low selectivity and/or low extraction yields

and solvent loss during heating and filtration. Furthermore, these traditional techniques

produce large amounts of polluting organic compounds. Whereas SFE is a flexible process

because of continuous modulation of the solvent power of the SFE that eliminate harmful

organic solvent and reduce the cost of post processing of the extracts for solvent removal. The

most widely used SFE solvent is carbon dioxide (CO2) because it is safe, available and cost

effective. Supercritical operation is done at relatively low pressure and room temperatures. The

higher investment costs are the only serious disadvantages compared to traditional atmospheric

pressure extraction. Finally, the solid portion retained after the extraction was particularly rich

in carbohydrate and fiber, so valorization of this byproduct can be an interesting topic for

future research as well.

44

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52

LIST OF APPENDICES

Appendix Table 1: Crude extracts yield calculation of jackfruit seeds

No of

Replication

Initial weight

(mg)

Final weight

(mg)

Percentage of

crude yield

Average

percentage

of crude

yield

1 0.2139 0.0256 11.96

11.36

2 0.2149 0.0232 10.79

3 0.2328 0.0237 10.18

4 0.2273 0.0285 12.53

5 0.2284 0.0259 11.34

53

Appendix Table 2: ANOVA on Full factorial screening test for DPPH without interaction

Call:

lm (formula = y ~ conc + temp + time + LSRat, data = D11coded.df)

Residuals:

Min 1Q Median 3Q Max

-230.658 -33.121 -4.508 47.141 164.699

Coefficients:

Estimate Std. Error t value Pr(>|t|)

(Intercept) 513.18 32.50 15.790 6.63e-09 ***

conc 117.14 32.50 3.604 0.004138 **

temp -162.95 32.50 -5.014 0.000394 ***

time -38.81 32.50 -1.194 0.257510

LSRat 101.78 32.50 3.132 0.009549 **

---

Signif. codes: 0 „***‟ 0.001 „**‟ 0.01 „*‟ 0.05 „.‟ 0.1 „ ‟ 1

Residual standard error: 130 on 11 degrees of freedom

Multiple R-squared: 0.8178, Adjusted R-squared: 0.7515

F-statistic: 12.34 on 4 and 11 DF, p-value: 0.0004716

54

Appendix Table 3: Full factorial screening test for DPPH with 2 factors interaction

Call:

lm(formula = y ~ (conc + temp + time + LSRat)^2, data = D11coded.df)

Residuals:

1 2 3 4 5 6 7 8

-0.9045 87.5167 39.6198 -126.2319 -105.2698 18.6577 66.5546 20.0575

9 10 11 12 13 14 15 16

18.4567 -105.0688 -57.1719 143.7841 87.7177 -1.1055 -49.0024 -37.6097

Coefficients:


(Intercept) 513.1821 33.2152 15.450 2.06e-05 ***

conc 117.1436 33.2152 3.527 0.01680 *

temp -162.9514 33.2152 -4.906 0.00445 **

time -38.8121 33.2152 -1.169 0.29528

LSRat 101.7796 33.2152 3.064 0.02797 *

conc:temp -0.1795 33.2152 -0.005 0.99590

conc:time 38.8624 33.2152 1.170 0.29472

conc:LSRat 8.0977 33.2152 0.244 0.81708

temp:time 37.7784 33.2152 1.137 0.30693

temp:LSRat -28.3850 33.2152 -0.855 0.43182

time:LSRat -47.8933 33.2152 -1.442 0.20890

Signif. codes: 0 „***‟ 0.001 „**‟ 0.01 „*‟ 0.05 „.‟ 0.1 „ ‟ 1

Residual standard error: 132.9 on 5 degrees of freedom



55

Appendix Table 4: Full factorial screening test for FCR without interaction

Call:

lm(formula = y ~ conc + temp + time + LSRat, data = D11coded.df)

Residuals:


-246.54 -83.84 -25.05 56.15 270.23

Coefficients:


(Intercept) 663.13 40.00 16.579 3.96e-09 ***

conc 72.66 40.00 1.817 0.09661 .

temp -139.26 40.00 -3.482 0.00513 **

time -39.00 40.00 -0.975 0.35053

LSRat 128.96 40.00 3.224 0.00810 **

---

Signif. codes: 0 „***‟ 0.001 „**‟ 0.01 „*‟ 0.05 „.‟ 0.1 „ ‟ 1

Residual standard error: 160 on 11 degrees of freedom



56

Appendix Table 5: Full factorial screening test for FCR with interaction

Call:


Residuals:

1 2 3 4 5 6 7 8 9 10 11

-30.84 26.20 85.11 -80.48 -22.34 26.97 -31.94 27.31 -31.73 36.37 -22.54

12 13 14 15 16

17.91 84.91 -89.54 -30.63 35.26

Coefficients:


(Intercept) 663.13 22.06 30.066 7.64e-07 ***

conc 72.66 22.06 3.294 0.02161 *

temp -139.26 22.06 -6.314 0.00147 **

time -39.00 22.06 -1.768 0.13729

LSRat 128.96 22.06 5.847 0.00207 **

conc:temp -29.04 22.06 -1.317 0.24507

conc:time 4.98 22.06 0.226 0.83032

conc:LSRat 19.95 22.06 0.905 0.40709

temp:time 97.76 22.06 4.432 0.00681 **

temp:LSRat -38.88 22.06 -1.763 0.13822

time:LSRat -53.20 22.06 -2.412 0.06070 .

Signif. codes: 0 „***‟ 0.001 „**‟ 0.01 „*‟ 0.05 „.‟ 0.1 „ ‟ 1




57

Appendix Table 6: Full factorial screening test for FRAP withoutu interaction

Call:

lm(formula = y ~ conc + temp + time + LSRat, data = D11coded.df)

Residuals:


-112.89 -32.48 6.77 30.72 64.62

Coefficients:


(Intercept) 424.295 13.654 31.076 4.54e-12 ***

conc 42.591 13.654 3.119 0.009761 **

temp -66.205 13.654 -4.849 0.000512 ***

time -7.221 13.654 -0.529 0.607406

LSRat 75.355 13.654 5.519 0.000181 ***

---

Signif. codes: 0 „***‟ 0.001 „**‟ 0.01 „*‟ 0.05 „.‟ 0.1 „ ‟ 1



F-statistic: 16 on 4 and 11 DF, p-value: 0.0001482

58

Appendix Table 7: Full factorial screening test for FRAP with interaction

Call:


Residuals:

1 2 3 4 5 6 7 8 9 10

-26.903 31.764 35.141 -40.002 -26.886 22.025 18.648 -13.787 32.705 -37.566

11 12 13 14 15 16

-40.943 45.804 21.084 -16.223 -12.846 7.985

Coefficients:


(Intercept) 424.295 12.993 32.656 5.06e-07 ***

conc 42.591 12.993 3.278 0.02200 *

temp -66.205 12.993 -5.095 0.00378 **

time -7.221 12.993 -0.556 0.60232

LSRat 75.355 12.993 5.800 0.00215 **

conc:temp -6.082 12.993 -0.468 0.65942

conc:time 14.537 12.993 1.119 0.31406

conc:LSRat 15.575 12.993 1.199 0.28433

temp:time 18.126 12.993 1.395 0.22180

temp:LSRat -1.077 12.993 -0.083 0.93713

time:LSRat -19.646 12.993 -1.512 0.19092

Signif. codes: 0 „***‟ 0.001 „**‟ 0.01 „*‟ 0.05 „.‟ 0.1 „ ‟ 1




59

Appendix Table 8: ANOVA on second order regreession model for DPPH

Call:

rsm(formula = y ~ SO(conc, temp, LSRat), data = D11codedbb.df)

Residuals:


-29.031 -16.781 0.373 16.372 32.885

Coefficients:


(Intercept) 918.688 14.194 64.725 5.52e-11 ***

conc 126.408 11.221 11.265 9.71e-06 ***

temp -156.057 11.221 -13.907 2.35e-06 ***

LSRat 72.095 11.221 6.425 0.000359 ***

conc:temp 105.917 15.869 6.674 0.000284 ***

conc:LSRat 24.469 15.869 1.542 0.166997

temp:LSRat -4.196 15.869 -0.264 0.799068

conc^2 -86.003 15.467 -5.560 0.000851 ***

temp^2 -27.381 15.467 -1.770 0.119995

LSRat^2 -45.777 15.467 -2.960 0.021116 *

---

Signif. codes: 0 „***‟ 0.001 „**‟ 0.01 „*‟ 0.05 „.‟ 0.1 „ ‟ 1



F-statistic: 50.58 on 9 and 7 DF, p-value: 1.528e-05

60

Appendix Table 9: ANOVA (numerical) check for the fitness of the model for DPPH

Model 1: y ~ conc + temp + LSRat + conc * temp + temp * LSRat + conc *

LSRat + I(conc^2) + I(temp^2) + I(LSRat^2)

Model 2: y ~ conc * temp * LSRat

Res.Df RSS Df Sum of Sq F Pr(>F)

1 7 7051.2

2 4 2327.5 3 4723.7 2.7061 0.1802

61

Appendix Table 10: ANOVA on second order regreession model for FCR

Call:

rsm(formula = y ~ SO(conc, temp, LSRat), data = D11codedbb.df)

Residuals:


-24.381 -14.532 -2.327 16.440 24.381

Coefficients:


(Intercept) 942.563 11.387 82.774 9.89e-12 ***

conc 33.831 9.002 3.758 0.007093 **

temp -122.832 9.002 -13.644 2.67e-06 ***

LSRat 46.783 9.002 5.197 0.001258 **

conc:temp 78.139 12.731 6.138 0.000473 ***

conc:LSRat 30.276 12.731 2.378 0.049020 *

temp:LSRat 13.979 12.731 1.098 0.308533

conc^2 -83.149 12.409 -6.701 0.000277 ***

temp^2 -79.195 12.409 -6.382 0.000374 ***

LSRat^2 -11.153 12.409 -0.899 0.398621

---

Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1



F-statistic: 40.54 on 9 and 7 DF, p-value: 3.238e-05

62

Appendix Table 11: ANOVA (numerical) check for the fitness of the model for FCR





1 7 4538.4

2 4 1300.9 3 3237.5 3.3182 0.1384

63

Appendix Table 12: ANOVA on second order regreession model for FRAP

Call:

lm(formula = y ~ conc + temp + LSRat + conc * temp + temp * LSRat +

conc * LSRat + I(conc^2) + I(temp^2) + I(LSRat^2), data = D11codedbb.df)

Residuals:


-43.721 -9.488 5.529 11.670 21.796

Coefficients:


(Intercept) 577.560 11.442 50.477 3.14e-10 ***

conc 11.889 9.046 1.314 0.230171

temp -83.713 9.046 -9.254 3.56e-05 ***

LSRat 70.417 9.046 7.785 0.000108 ***

I(conc^2) -62.032 12.469 -4.975 0.001610 **

I(temp^2) -11.463 12.469 -0.919 0.388507

I(LSRat^2) -49.708 12.469 -3.987 0.005279 **

conc:temp 47.847 12.793 3.740 0.007261 **

temp:LSRat 16.362 12.793 1.279 0.241655

conc:LSRat -7.996 12.793 -0.625 0.551754

---

Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1




64

Appendix Table13: ANOVA (numerical) check for the fitness of the model for FRAP





1 7 4582.2

2 4 2420.2 3 2162 1.1911 0.4191

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