i

OPTIMIZATION OF SUGAR PRODUCTION FROM CASSAVA

RESIDUE USING RESPONSE SURFACE METHODOLOGY

MOHD SHAHRIL BIN MUSTAFA @ SULAIMAN

Universiti Malaysia Pahang

ii

UNIVERSITI MALAYSIA PAHANG UNIVERSITI MALAYSIA PAHANG

BORANG PENGESAHAN STATUS TESISBORANG PENGESAHAN STATUS TESIS

JUDUL : OPTIMIZATION OF SUGAR PRODUCTION FROM CASSAVA

RESIDUE USING RESPONSE SURFACE METHODOLOGY

SESI PENGAJIAN : 2009/2010

Saya MOHD SHAHRIL BIN MUSTAFA @ SULAIMAN

(HURUF BESAR)mengaku membenarkan tesis (PSM/Sarjana/Doktor Falsafah)* ini disimpan di Perpustakaan Universiti Malaysia Pahang dengan syarat-syarat kegunaan seperti berikut :

1. Tesis adalah hakmilik Universiti Malaysia Pahang2. Perpustakaan Universiti Malaysia Pahang dibenarkan membuat salinan untuk tujuan

pengajian sahaja.3. Perpustakaan dibenarkan membuat salinan tesis ini sebagai bahan pertukaran antara institusi

pengajian tinggi.4. **Sila tandakan ( √ )

SULIT (Mengandungi maklumat yang berdarjah keselamatan ataukepentingan Malaysia seperti yang termaktub di dalam

AKTA RAHSIA RASMI 1972)

TERHAD (Mengandungi maklumat TERHAD yang telah ditentukan oleh organisasi/badan di mana penyelidikan dijalankan)

√ TIDAK TERHAD

Disahkan oleh

(TANDATANGAN PENULIS) (TANDATANGAN PENYELIA)

Alamat Tetap: No 1366 Blok B3, PUAN ROHAIDA CHE MAN Felda Adela, Nama Penyelia 81900 Kota Tinggi, Johor Tarikh : 30 April 2010 Tarikh: 30 April 2010

CATATAN : * Potong yang tidak berkenaan.** Jika tesis ini SULIT atau TERHAD, sila lampirkan surat daripada pihak

berkuasa/organisasiberkenaan dengan menyatakan sekali sebab dan tempoh tesis ini perlu dikelaskan sebagai SULIT atau TERHAD.

Tesis dimaksudkan sebagai tesis bagi Ijazah Doktor Falsafah dan Sarjana secara penyelidikan, atau disertasi bagi pengajian secara kerja kursus dan penyelidikan, atau Lapuran Projek Sarjana Muda (PSM).

iii

“I hereby declare that I have read this thesis and in my opinion this thesisis sufficient in terms of scope and quality for the award of the degree of Bachelor of

Chemical Engineering (Biotechnology)”

Signature : ……………………………..

Supervisor : Puan Rohaida Binti Che Man

Date : 30 April 2010

iv

OPTMIZATION OF SUGAR PRODUCTION FROM CASSAVA RESIDUE

USING RESPONSE SURFACE METHODOLOGY

MOHD SHAHRIL BIN MUSTAFA @ SULAIMAN

A thesis submitted in fulfillment of the requirements for the award of the degree

of Bachelor of Chemical Engineering (Biotechnology)

Faculty of Chemical Engineering and Natural Resource

Universiti Malaysia Pahang

MAY 2010

ii

I declare that this thesis entitled “Optimization of Sugar Production From Cassava

Residue using Response Surface Methodology” is the result of my own research

except as cited in references. The thesis has not been accepted for any degree and

not concurrently submitted in candidature of any other degree

Signature : ………………………………………...

Supervisor : Mohd Shahril Bin Mustafa @ Sulaiman

Date : 30 April 2010

iii

This thesis is dedicated to all my love ones, especially to Mak &Abah. Thank You for the endless love & support.

iv

ACKNOWLEDGEMENT

Alhamdulillah, to Allah S.W.T I thank the most on His utmost love and

blessings for giving me the guidance in accomplishing this study. The most courteous

thank againt Him who made all thing achievable.

First and foremost, I would like to acknowledge and extend my heartfelt

gratitude to my supervisor, Puan Rohaida Bt Che man, for her guidance, criticisms and

inspiration she extended. Thank you so much for her vital encouragement and support.

I wish to express my deepest thankfulness to Encik Zaki who had been helping,

supporting and motivating me throughout this study. Thank you to the faculty of

Chemical & Natural Resources Engineering, UMP for its permission to carry out this

study.

Exclusive thanks from me to the people that I love especially Abah, Mak,

Shafiq, Abanglong, Ahmad Termizi, Elyanes Maslisa, Raja Norazean, Hanifan Abd

Jalil, Kamarul Izhan and to all who made this project done. Thank you so much for the

everlasting love, upright companionship, honourable supports and exquisite inspirations

through out this period.

v

ABSTRACT

Cassava residue, starch processing waste from cassava starch plant, was used as a

raw material in sugar production. This is study aims to optimize the production of sugar

from cassava residue using response surface methodology. The effect of parameters

such as incubation time and enzyme concentration is used to study the concentration of

glucose produced. Three different enzymes are used to produce glucose namely

cellulase, glucoamylase and α-amylase. Cellulase was the best enzyme that yielded the

most glucose. The optimization of the production of sugar is carried out by using

response surface methodology (RSM) based on the central composite design

(CCD).The optimal set parameters for enzyme concentration are 1.32 g/mL and 4.47

hours of incubation time. Glucose concentration of 1.059 g/L was achieved by using

these optimal parameters.

vi

ABSTRAK

Sisa ubi kayu adalah sisa pemprosesan kanji daripada pokok ubi kayu, digunakan

sebagai bahan mentah dalam penghasilan gula.. Tujuan kajian ini dilakukan adalah

untuk mengoptimumkan penghasilan gula daripada ubi kayu menggunakan kaedah

gerak balas permukaan (RSM). Pengaruh parameter seperti masa inkubasi dan

kepekatan enzim digunakan untuk mengkaji kepekatan glukosa yang dihasilkan. Tiga

jenis enzim digunakan dalam menghasilkan glukosa iaitu cellulase, glucoamylase and

α-amylase. Enzim terbaik yang menghasilkan gula yang paling tinggi adalah cellulase.

Penghasilan gula dioptimumkan menggunakan kaedah gerak balas permukaaan

berdasarkan reka bentuk komposit pusat (CCD). Parameter yang optimum adalah

dengan menggunakan 1.32 g/mL kepekatan enzim pada 4.47 jam masa inkubasi.

Kepekatan glukosa yang dihasilkan adalah 1.059 g/mL dengan menggunakan parameter

yang optimum ini.

TABLE OF CONTENT

vii

CHAPTER TITLE PAGE DECLARATION ii DEDICATION iii ACKNOWLEDGEMENT iv ABSTRACT v ABSTRAK vi TABLE OF CONTENT viii LIST OF TABLES x LIST OF FIGURES xi LIST OF SYMBOLS/ABBREVIATIONS xii LIST OF APPENDICES xiii 1 INTRODUCTION

1.1 Introduction 1

1.2 Problem Statement 2

1.3 Objective of Research 2

1.4 Scopes of Research

2 LITERATURE REVIEW

2.1 Cassava Residue 4

2.2 Structure of Cassava Residue Biomass 5

2.2.1 Cellulose 5

2.2.2 Hemicelluloses 6

2.2.3 Starch 7

2.3 Enzymatic Hydrolysis 8

2.3.1 Overview of Enzymatic Hydrolysis 9

2.4 Optimization of time and enzyme concentration on

Production of sugar by Using Response Surface

Methodology (RSM) 10

2.5 Production of Sugar 13

2.5.1 Production of xylose from Palm Oil Empty Fruit

Bunch (POEFB) using enzymatic hydrolysis process 13

2.5.2 Production of sugar from from pineapple cannery

waste using continuous fermentation 13

2.5.3 Production of sugar from from pineapple cannery

viii

waste using continuous fermentation 14

3 METHODOLOGY

3.1 Overview of Research Methodology 15

Enzymatic hydrolysis 16

3.1 Screening of Enzymes 16

3.2 Effect of Parameters 16

3.3 Effect of Incubation Time 17

3.4 Effect of Enzyme Concentration 17

3.5 Concentration of Glucose 17

3.7.1 Preparation of DNS reagent 17

3.7.2 Concentration of Glucose Analyzed by DNS 17

3.8 Response Surface Methodology (RSM) 18

4 RESULT AND DISCUSSION

4.1 Screening of Enzymes 20

4.2 Effect of Parameters on Glucose Concentration 21

4.2.1 Effect of Incubation Time 21

4.2.2 Effect of Enzyme Concentration 23

4.3 Determination of the Optimum of Parameters on Sugar

Production Using Response Surface Methodology

(RSM) 25

4.4 Optimization of sugar production using Response Surface

Methodology (RSM) 29

5 CONCLUSION ANR RECOMMENDATIONS

5.1 Conclusion 30

5.2 Recommendations 31

REFERENCES 32

APPENDIX A - B 39-44

ix

LIST OF TABLES

x

TABLE NO. TITLE PAGE

1.1 Chemical composition in cassava residue 5

1.2 Low level and high level of cultural condition 25

1.3 Experiments designed by Design Expert Software 26

1.4 ANOVA for response surface quadratic model for the

production sugar 27

1.5 Summaries of the optimized cultural conditions for sugar

production 29

LIST OF FIGURES

xi

FIGURE NO. TITLE PAGE

1.1 Cassava Starch Plant 1

1.2 Basics compound of cellulose is cellubiose, disaccharides

of two β-1-4 bonded glucoses 6

1.3 Structure of xylan and enzymes cleavage sites 7

1.4 Structure of starch 7

1.5 Schematic for basic process of enzymatic hydrolysis 9

1.6 Research design for the production of sugar 15

1.7 Screening of different enzyme 21

1.8 Effects of incubation time 22

1.9 Effect of enzyme concentration 23

1.10 Response surface plot of sugar production: Incubation time

vs. enzyme concentration. 28

LIST OF SYMBOL/ABBREVIATIONS

xii

ANNOVA - Analysis if variance

CCD - Central composite design

g - Gram

mL - Milliliter

DNS - Dinitrosaliccyclic Acid

HPLC - High performance liquid chromatography

Hr - Hour

L - Liter

g/mL - Gram per milliliter

g/L - Gram per liter

min - Minute

M - Molar

Nm - Nanometer

OD - Optical density

OFAT - One factor at a time

RSM - Response Surface Methodology

w/v - Weight per volume

°C - Degree celcius

% - Percentage

LIST OF APPENDICES

APPENDIX TITLE PAGE

xiii

A1 Preparation mixture of 0.1 M Sodium Hydroxide (NaOH),

(2000 mL) and 0.1 M Citric Acid (1000 mL) as Buffer 35

A2 Preparation of DNS Reagent (1 L) 36

A3.1 Preparation of 1 mg/mL of Glucose Standard Solution (10 mL) 37

A3.2 Standard Curve of Glucose 37

A4 Enzyme Dilution 38

B1 ANNOVA Results for Optimization of Parameters 39

1

CHAPTER 1

INTRODUCTION

1.1 Introduction

Cassava is a shrubby, tropical, perennial plant that is not well known in the

temperate zone. For the most people, cassava is most commonly associated with

tapioca. The plant grows tall, sometimes reaching fifteen feet, with leaves varying in

shape and size. The edible parts are the tuberous root and leaves. The tuber (root) is

somewhat dark brown in color and grows up to two feet long. Ironically, any

processed raw materials will end up producing waste. Due to the mass production of

which originated from cassava starch plant, encourage a mass production of its

waste, cassava residue which also called as biomass.

Recently, utilization of biomass resources has been the subject of the various

studies. Cassava residues is one of the biomass materials, which is the by product

from the cassava starch plant industry. Cassava residue has the potential to be utilized

to produce sugar due to its containing cellulose, hemi-cellulose and starch at levels of

24.99, 6.67 and 61% (w/w), respectively. Starch components in cassava residue were

converting to reducing sugar with different kind of enzymes (Teerapatr et al., 2006).

Figure 1.1 shows the cassava starch plant.

Figure 1.1: Cassava starch plan

2

1.2 Problem Statement

Annually, there is approximately many ton of cassava residues produced from

the starch factories in Malaysia. The cassava residue left over from the production

processes are abundant and still contain a high amount of starch content. Use of

cassava residue as raw material in sugar production not only reduces waste material

created from the cassava starch industry, but also lowers the cost of sugar production

(La-aied et al., 2006). In addition by using the cassava residue as raw material to sugar

production is to prevent from the environmental issues.

Fortunately, the main component of cassava residue consists of cellulose 24.99

% (w/w), hemi-cellulose 6.67 % (w/w) and starch 61 % (w/w) (Teerapatr et al., 2006).

Therefore, it can be consumed to produce sugar which has a major field of applications

in industries and can very useful to the environment and society at large.

1.3 Objective of Research

The main objective of this research is to optimize the production of sugar from

cassava residue using response surface methodology.

1.4 Scopes of Research

In order to achieve the objective, scope of study was divided into three as

the following:

i) To determine the best enzyme that can produce the most yield of glucose

using enzymatic hydrolysis by feeding three types of commercial enzymes

into the treated cassava residue, which are: (a) Cellulase, (b) Glucoamylase

and (c) α-amylase.

ii) To study the effect of parameters of the best chosen from (i) such as incubation

time and enzyme concentration on glucose concentration.

iii) To optimize the parameters of the enzyme chosen by using response surface

3

methodology (RSM) on sugar production.

4

CHAPTER 2

LITERATURE REVIEW

2.1 Cassava Residue

Cassava residue, which is the starch-processing waste from cassava starch

plant, was used as a raw material in sugar production (Cholada et al., 2004). The

cassava residue left over from these production processes are abundant and still

contain a high amount of starch content. Most cassava residue can be used as animal

feed due to its high content of protein and other nutrients which are necessary for

animal growth. In addition, cassava residue can be used to produce sugar. By using the

cassava residue as raw material in sugar production not only reduces waste material

created from the cassava starch industry, but also lowers the cost of sugar production

(Lerdluk et al., 2006).

The main component of most cassava residue containing cellulose at level

24.99%, hemicelluloses at level 6.67% and starch at level 68.34% ( Suthkamol et al.,

2006).Annually there is approximately one million of cassava residues produced from

the starch factories. The application of using these cassava residues especially for the

sugar production would be advantage for the country economy (Bongotrat et al.,

2004). Table 2.1 shows the chemical composition in cassava residue.

Table 2.1: The chemical composition in cassava residue

Composition Cassava residue

Cassava residue

Cassava residue(Sample 3)

5

(Sample 1) (Sample 2)Moisture 78.16 79.50 82.74protein 1.82 2.03 2.31Fat 0.09 0.20 0.16Ash 1.61 2.38 2.05Fiber 10.61 14.35 14.56Starch 69.90 61.84 64.36

2.2 Structure of Cassava Residue Biomass

Main components of most cassava residue are cellulose, hemi-cellulose and

starch. Each of this part plays a role in protecting each other and strengthens the plant

structure (La-aied et al., 2006).

2.2.1 Cellulose

Cellulose consists of linear macromolecular chains of glucose, linked by β-1,4-

glucosidic bonds between the number one and number four carbon atoms of the

adjacent glucose units. Native crystalline cellulose is insoluble and occurs as fibers of

densely packed, hydrogen-bonded and anhydroglucose chains of 15 to 10, 000 glucose

units (Howard et al., 2003).

Theoretically, complete hydrolysis of cellulose into glucose require three main

enzymes namely endo-β-glucanase, exo-β-glucanase and β-glucosidase. Endo-β-

glucanase randomly hydrolyses the cellulose polymers yielding shorter

oligosaccharides and also a few cellubiose molecules meanwhile, exo-β-glucanase acts

at the end of cellulose chains. β-glucosidase primarily hydrolyses cellubiose into

glucose. Figure 1.2 shows the basic compound of cellulose.

6

Figure 1.2: The basics compound of cellulose is cellubiose, disaccharides of two β-

1-4 bonded glucoses

2.2.2 Hemicelluloses

Hemicelluloses are heterogeneous polymers of pentoses (xylose, arabinose),

hexoses (mannose, glucose, galactose) and sugar acids. In hardwood hemicelluloses

mostly contains xylan, whereas softwood hemicelluloses contains mostly

glucomannans. Xylan of many plant materials are heteropolysaccharides with

homopolymeric backbone chains of 1,4-linked β-D-xylopyranose units. The

monosaccharides released upon hemicellulose hydrolysis include a large fraction of

pentoses (Gunda et al., 1970).

In several plants the majority of hemicelluloses is xylan which can be

hydrolyzed into xylose. Particularly the hemicellulose of hardwood is rich in xylan.

Consequently it is possible to obtain xylan and xylose as by-products from cellulose

industry using hardwood (Gunda et al., 1970).

Figure 1.3 shows the structure of xylan and enzymes cleavage site. The

complex structure of xylan needs different enzymes to complete its hydrolysis. The

main enzyme that randomly hydrolysis the main chain of xylan to produce mixture

xylooligosaccharides named as endoxylanase and β-xylosidase will liberate xylose

from short oligosaccharides. Moreover, the sides groups presents in xylan are liberated

by α-L-arabinofuranosidase, α-D-glucuronidase and acetyl xylan esterase. Figure 2

shows the structure of xylan.

7

Figure 1.3: Structure of xylan and enzymes cleavage sites

2.2.3 Starch

Starch is a polymer composed of glucose units primarily linked by a (1-4)

glucosidic bonds, with some additional a (1-6) linkages. Figure 1.4 shows the structure

of starch.

Figure 1.4: The structure of starch

It consists of a mixture of two types of polymers: amylose and amylopectin.

The mass fraction of amylose normally lies around 20-30 % for amylose. Starch

varieties with deviating compositions also exist (nearly 100 % pure amylopectin potato

and maize starch). Amylopectin is a branched polymer. Linear chains a (1-4)-linked

D-glucose residues are connected by (1-6)-D-glucosidic linkages. Amylose is a much

8

more linear polymer since the frequency of a (1-6) linkages (0.2 - 0.7 %) is much

smaller than in amylopectin (4 - 5 %) (Richard et al., 2004).

The enzymes that break down or hydrolyze starch into the constituent sugars

are known as amylases. Alpha-amylases are found in plants and in animals. Human

saliva is rich in amylase, and the pancreas also secretes the enzyme. Individuals from

populations with a high-starch diet tend to have more amylase genes than those with

low-starch diets; chimpanzees have very few amylase genes. It is possible that turning

to a high-starch diet was a significant event in human evolution (Steve, 2004).

Beta-amylase cuts starch in maltose units. This process is important for the

digestion of starch and also used in brewing, where the amylase from the skin of the

seed grains is responsible for converting starch to maltose.

2.3 Enzymatic Hydrolysis

There are two main approaches to convert cellulose, hemicelluloses and starch

into simple sugars such as glucose and xylose are acid hydrolysis and enzymatic

hydrolysis (Teerapatr et.al., 2004). Acid hydrolysis is relatively inexpensive and

simple but there are several major drawbacks associated with this process including:

degradation of sugars due to the high temperature, formation of fermentation

inhibitors, such as furfural, and expensive construction materials in equipment to

handle the acids. The enzymatic hydrolysis of cellulose, hemicelluloses and starch

can be more attractive because of its specificity and absence of competitive

degradation, which results in higher yields of sugars. However, enzyme costs can be

prohibitively high and it is important to reduce the costs of enzyme production

and/or to reduce the amount of enzyme used. Figure 1.5 shows schematic for basic

process of enzymatic hydrolysis.

Substrate Glucose

Cellulase

Hydrolysis

Different Enzymes Mixture

9

Hemicelluloses

Starch

α-amylase,

Glucoamylase,

Cellulase

Figure 1.5: Schematic for basic process of enzymatic hydrolysis

2.3.1 Overview Process of Enzymatic Hydrolysis

There is a process that simultaneously hydrolyzes cellulose, hemicelluloses and

starch to sugars by addition of enzymes. Cellulose, hemicelluloses and starch is

degraded by different enzymes that are able to hydrolyse to the sugar glucose. The

cellulose molecules are composed of long chains of sugar molecules. One major

process to Cellulose hydrolysis (cellulolysis) is an enzymatic reaction. In the

hydrolysis process, these chains are broken down to free the sugar, before it is

fermented for alcohol production.

Hemicelluloses are xylan which can be hydrolyzed into xylose. The complex

structure of xylan needs different enzymes to complete its hydrolysis. The main

enzyme that randomly hydrolysis the main chain of xylan to produce mixture of

xylooligosaccharides named as endoxylanase and β-xylosidase will liberate xylose

from short oligosaccharides. Moreover, the sides groups presents in xylan are liberated

by α-L-arabinofuranosidase, α-D-glucuronidase and acetyl xylan esterase (Badal,

2003).

2.4 Optimization of Time and Enzyme Concentration on Production of Sugar

by Using Response Surface Methodology (RSM)

10

During the preliminary investigations into this research it was realized that the

classical, “one-time-a-time” factorial design experiments, where a single factor is

varied while others are kept constant, are often expensive and time consuming and do

not take into account the possible interaction of various independent factors that would

skew the results. For these reasons, statistical methods have been developed to reduce

the cost and duration of experiments that also allow for the observation of any

interacting factors in the final process response. One such highly successful method is

“Response Surface Methodology” (RSM), which is defined as a statistical method that

uses quantitative data from appropriate experimental designs to determine and

simultaneously solve multivariate equations that specify the optimum product for a

specified set of factors through mathematical models. It involves four important steps:

(1) identification of critical factors for the product or process, (2) determination of the

range of factors levels, (3) selection of specific test samples by the experimental

design, (4) analysis of the data by RSM and data interpretation (Mutanda et al., 2008).

Basically, Response Surface Methodology (RSM) is a systematic approach that

can be obtained by using an inverse process of first, specifying the criteria and then

computing the ‘best’ design according to a formulation. Response Surface

Methodology (RSM) is a collection of statistical and mathematical techniques useful

for developing, improving and optimizing the design process. RSM encompasses a

point selection method (also referred to as Design of Experiments, Approximations

methods and Design Optimization) to determine optimal settings of the design

dimensions. It has important applications in the design, development and formulation

of new products as well as in the improvement of existing product designs. RSM

which includes factorial design and regression analysis can build models to evaluate

the effective factors and study their interaction and select the optimum conditions in

limited number of experiments (Chauhan and Gupta, 2004).

A successive response surface method is an iterative method which consists of

a scheme to assure the convergence of an optimization process. The scheme

determines the location and size of each successive region of interest in the Design

Space, builds a response surface in this region, conducts an design optimization and

will check the tolerances on the response and design variables for termination. This

RSM method has been widely used to evaluate and understand the interaction between

11

different physiological and nutritional parameters (Ambati and Ayyanna, 2001;

Hounjg et al., 1989).

There have been a few reports on optimization lipase production by

Pseudomonas sp. whereas Liu et., al (2006) have reported 5-fold increase in lipase

yield after optimization. Production of an alkaline lipase from Burkholderia

multivorans increased by 12-fold by a combination of Response Surface Methodology

(RSM) and scale-up attempt using 14 L bioreactor (Gupta et al., 2008). Thus the level

of lipase production by the strain is significantly high and can be cost effective for its

applications.

Optimization of ethanol production from hot-water extracts of sugar maple

chips was conduct by Jian et al., 2009. Hot-water extracts from sugar maple chips

prior to papermaking was employed in this study to produce ethanol by Pichia stipitis

58784. The effects of several factors such as seed culture age, fermentation time,

inoculum quantity, agitation rate, percent extract, concentration of inorganic nitrogen

source (NH4)2SO4 and pH value on ethanol production were investigated by orthogonal

experiments. Orthogonal analysis shows that the optimal fermentation was obtained in

the condition of 48 hour seed culture, 120 hour fermentation, 16% inoculum, 180 rpm,

containing 30 % extracts, 8 % ammonium sulphate supplement and pH 5. This optimal

condition was verified at 800 mL level in a 1.3 L fermentor. The ethanol yield reached

82.27 % of the theoretical (20.57 g/L) after 120 hour (Jian Xu and Shijie Liu, 2009).

Optimization of temperature, sugar concentration and inoculum size to

maximize ethanol production without significant decrease in yeast cell viability was

done by Cecilia et al., 2009. Aiming to obtain rapid fermentations with high ethanol

yields and retention of high final viabilities (responses), a 23 full-factorial central

composite design combined with response surface methodology was employed using

inoculum size, sucrose concentration and temperature as independent variables. From

this statistical treatment, two well-fitted regression equations having coefficients

significant at the 5 % level were obtained to predict the viability and ethanol

production responses. Three dimensional response surfaces showed that increasing

temperatures had greater negative effects on viability than on ethanol production.

12

Increasing sucrose concentrations improved both ethanol production and viability

(Karen et al., 2009).

The interactions between the inoculum size and the sucrose concentrations had

no significant effect on viability (Meline et al., 2009). Thus, the lowering of the

process temperature is recommended in order to minimize cell mortality and maintain

high levels of ethanol production when the temperature is on the increase in the

industrial reactor. Optimized conditions (200 g/L initial sucrose, 40 g/L of dry cell

mass, 30 °C) were experimentally confirmed and the optimal responses are 80.8±2.0

g/L of maximal ethanol plus a viability retention of 99.0±3.0% for a 4 hour

fermentation period. During consecutive fermentations with cell reuse, the yeast cell

viability has to be kept at a high level in order to prevent the collapse of the process.

Optimization study for sorbitol production by Zymomonas mobilis in sugar

cane molasses was conducted by Cazetta et al., 2005. Sorbitol production by

Zymomonas mobilis in sugar cane molasses was investigated by batch fermentation.

The effects of the mean sugar concentration, temperature, agitation and culture time

were studied simultaneously by factorial analysis. Maximum sorbitol production was

determined using a second-order central composite design and analyzed by the

superficial response method. Response surface methodology analysis showed that the

best conditions for sorbitol production were 300 g/L total reducing sugars (TRS) in the

culture medium, 36 hour at 35 °C fermentation time (Silva et al., 2004).

2.5 Production of Sugar

13

Many types of plant biomass can produce the sugar using some method. For

example palm oil empty fruit bunch (POEFB) by using enzymatic hydrolysis process

(Choudhury et al., 2007), production of sugar from maize starch by using enzymatic

hydrolysis process (Singh et al., 1999).

2.5.1 Production of Xylose From Palm Oil Empty Fruit Bunch (POEFB) Using

Enzymatic Hydrolysis Process

Xylose is found in the embryos of most edible plants. Xylan, the

hemicelluloses used as raw material in xylose production. Xylanase, the enzyme that is

used to hydrolysed the structure of xylan to produce xylose. This study aims to

improve the production of xylose from palm oil empty fruit bunch (POEFB) using

enzymatic hydrolysis process. The effect of cultural conditions such as enzyme

concentration and incubation time is used to study the production of xylose. Three

different xylanases enzyme are used to produce xylose namely Multifect Xylanase,

Multifect CX 12L and optimase CX 72L. Multifect CX 12L was the best enzyme that

yielded the most xylose. The optimization of the production of xylose is carried out by

using response surface methodology (RSM) based on the central composite design

(CCD). The optimal set cultural conditions for enzyme concentration are 50 U/g and

35.5 hours of incubation. Xylose production of 23.15 mg/mL was achieved by using

these optimal conditions (Rahman et al., 2007).

2.5.2 Production of Sugar From Maize Starch Using Enzymatic Hydrolysis

Process Crude amylases were prepared from Bacillus subtilis ATCC 23350 and

Thermomyces lanuginosus ATCC 58160 under solid state fermentation. The effect of

various process variables was studied for maximum conversion efficiency of maize

starch to glucose using crude amylase preparations. Doses of pre-cooking α-amylase,

post-cooking α-amylase, glucoamylase and saccharification temperature were found to

produce maximum conversion efficiency and were these selected for optimization.

Full factorial composite experimental design and response surface methodology were

used in the design of experiments and analysis of results. The optimum values for the

14

tested variables for the maximum conversion efficiency were: pre-cooking α-amylase

dose 2.243 U/mg solids, post-cooking α-amylase dose 3.383 U/mg solids,

glucoamylase dose 0.073 U/mg solids at a saccharification temperature of 55.1 °C.

The maximum conversion efficiency of 96.25 % was achieved. This method was

efficient; only 28 experiments were necessary to assess these conditions, and model

adequacy was very satisfactory, as coefficient of determination was 0.9558

(Adinarayana et al., 2005).

2.5.3 Production of Sugar From Sorghum Straw Using Acid Hydrolysis Process.

Xylose is a hemicellulosic sugar that can be used as a carbon and energy

source for the growth of microorganisms. The main use of xylose is its bioconversion

to xylitol. Sorghum straw is a raw material for xylose production that has not yet been

studied. The objective of this work was to study xylose production by hydrolysis of

sorghum straw at 122 °C, using three concentrations of sulphuric acid (2 %, 4 % and 6

%). Kinetic parameters of mathematical models for predicting the concentration of

xylose, glucose, acetic acid and furfural were found and optimal conditions selected.

These were 2 % H2SO4 at 122 °C for 71 minutes, which yielded a solution with 18.17

g xylose/L, 6.73 g glucose/L, 0.9 g furfural/L and 1.51 g acetic acid/L (Ramirez et al.,

2001).

15

CHAPTER 3

MATERIAL AND METHODS

3.1 Overview of Research Methodology

Basically, there are three steps to be accomplished in producing the highest

yield of sugar. The first step is to performing an enzymatic hydrolysis method on the

cassava residue by (i) screening the best enzyme (ii) studying the effects of

parameters; incubation time and enzyme concentration. Finally, optimize the

parameters to maximize the production of sugar by using response surface

methodology (RSM). Details on this research design are shown in Figure 1.6.

Figure 1.6: Research design for the production of sugar

3.2 Enzymatic Hydrolysis

Experiment was carried out with constant pretreated substrate 1 g (dried

weight) in shake flask (250 mL volume) with enzyme. The substrate was suspended

1. Screening of Enzymes: Enzymatic hydrolysis

3. Optimization by RSM

Cellulase Glucoamylase α-amylase

2. Effect of parameters on the best enzyme Incubation time

Enzyme concentration

16

to mixture are Sodium Hydroxide (NaOH) and Citric acid (C6H8O7) as buffer. The pH

of buffer was measured by pH meter. The enzymatic hydrolysis was performed by

incubating the mixture in an oven. Supernatant was collected through filter paper.

Reaction was stopped by boiling the mixture at 100 °C for 3 minutes.

3.3 Screening of Enzymes

Different types of enzyme were used to determine the best enzyme that can

produce the most yield of sugar by feeding three types of commercial enzymes into the

treated cassava residue: (1) Cellulase, (2) Glucoamylase, (3) α-amylase. On cellulase

treatment, cassava residue was hydrolyzed with cellulase at PH 4.8, 40 °C, α-amylase

at pH 5.5, 100 °C and glucoamylase at pH 4.5, 60 °C. Time to hydrolyze is 7 hour and

concentration of each enzymes is 1 g/mL.

3.4 Effect of Parameters

In the production of sugar, the yield of the product depends on its cultural

parameters in executing the experiments. There are various parameters that affect the

yield of sugar such as enzyme concentration, incubation time, reaction time, reaction

temperature, agitation rate, pH of buffer used and also the concentration of substrate.

However, this research is to study about the effect of incubation time and

enzyme concentration. It is believe that these two parameters give a big impact on

sugar yield.

3.5 Effect of Incubation Time

The best enzyme chosen from above is used in this step but with a range of

time of 2, 4, 6, 8, 10 and 12 hours.

17

3.6 Effect of Enzyme Concentration

The best time will then used for further study on cultural conditions but with a

range of concentration of 0.5, 1.0, 1.5, 2.0 and 2.5 g/mL.

3.7 Concentration of Glucose

This concentration of glucose analysis is to determine how many reducing

glucose yield from the hydrolyzed samples all along this research experiments.

3.7.1 Preparation of DNS reagent

An amount of 10.6 g of 3,5 - dinitrosalicylic acid crystals was dissolved with

19.8 g of NaOH in 1,416 of distilled water. 306 g of sodium-potassium tartrate

(Rocchelle salts) was added into the mixture. A phenol crystal was melt at 50 °C using

a water bath. Then 7.6 mL of the dissolved phenol was added to the mixture. Next, 8.3

g of sodium meta-bisulfate (Na2sS2O4) was added and finally, NaOH was added to

adjust the pH to 12.6, if required (Fisher and Stein, 1961).

3.7.2 Concentration of Glucose Analyzed by DNS

Total glucose produced was measured by DNS method (Sanjeev et al., 2002).

The reaction was done by boiled 2 mL of diluted samples in test tubes for 3 minutes in

water at 100 °C. At the end of boiled period, 2 mL of the sample is then mixed with 2

mL of DNS reagent to stop the reaction followed by boiled for 5 minutes to generate

the colour. A colour change was measured at wavelength 540 nm by using Uv-vis

Spectrophotometer. Blank lacking of distilled water was analyzed simultaneously with

each batch of samples. Standard curve of glucose was prepared by replacing the

sample with various concentration of glucose. Concentration glucose was calculated

from the absorbance (A540nm) of the sample using the calibration curve.

18

3.8 Response Surface Methodology (RSM)

Design Expert Software (State-Ease Inc, Statistic Made Easy, Minneapolis,

MN, USA, (Version 6.0.4) was applied to execute the response surface methodology.

The central composite design (CCD) was chosen in the optimization process due to its

ability in providing factorial analysis in 2 levels of the factors involves (enzyme

concentration & incubation time), which will be from the centre point to the star point.

The relation between the coded values and actual values is described in Equation 3.1:

Xi = (Ai – Ao) / ∆A (Equation 3.1)

Where;

Xi = Coded value of the variable

Ai=Actual value

Ao= Actual value of Ai at the centre point

The quadratic model to predict the optimal point is coded in Equation 3.2:

Y=βo+∑βiXi+∑βiiXi2+∑βijXiXj (Equation 3.2)

Where; Y= Predict response variable

βo= Offset term

βi= Linear effect

βii= Interaction effect

X= Coded levels of the independent variables

The regression equation above was optimized for the optimal values using

Design Expert Software. An experiment was conducted in a randomized order to stay

away from systematic bias. Next, the ANOVA test was used to analyzed the

19

experimental data obtained. P-value was determined to identify the significance of the

quadratic model. A P-value ≤0.05 are considered to be significant.

CHAPTER 4

20

RESULT AND DISCUSSION

4.1 Screening of Enzymes

Three types of enzymes were screened to determine which one of them is the

best enzyme that can hydrolyze the cassava residue producing the most glucose. These

enzymes were used as catalysts to produce glucose that contain in cassava residue.

Figure 1.7 shows the difference between these three enzymes in concentration

of glucose. The experiments were conducted in the same cultural conditions which are

incubated at 7 hours in an oven with 1 g/mL of each enzyme concentration.

Cellulase shows the highest yield of concentration of glucose in Figure 1.7. An

amount of 1.018 g/L, 0.6813 g/L and 0.9664 g/L of concentration were detected from

cellulase, glucoamylase and α–amylase respectively. Based on report Mark et al.,

(2007), hydrolyses with cellulase had higher yields of glucose, galactose and xylose

than hydrolyses without cellulase. Moreover, Krishnamurthy et al., (2010) reported

that, by using cellulose pretreatment for the any production, higher yield will be

obtained. Therefore, cellulase enzyme was used for the next study on the effect of

cultural conditions on glucose concentration.

Glucoamylase Cellulase α-amylase0

0.2

0.4

0.6

0.8

1

1.2

Types of enzyme

Con

cent

ratio

n gl

ucos

e (g

/L)

21

Figure 1.7: Screening of different enzyme.

4.2 Effect of Parameters on Glucose Concentration

Studies on the parameters that affect the concentration of glucose were carried

out by using the conventional method. The method studies one factor at a time

(OFAT) while keeping the others at a certain level. This study was done to determine

the optimum range of parameters for further optimization process. Principally, the low

and high levels of every parameter were obtained before and after peak values

respectively in the appropriate experiments. The parameters involved were incubation

time and enzyme concentration.

4.2.1 Effect of Incubation Time

The best time was determined by conducting an enzymatic hydrolysis, where

the samples were incubated in an oven at 40° C by using 1 g/mL enzyme

concentrations, whereas the incubation time is varied in a range from 2 – 12 hours.

The effects of incubation time on the concentration of glucose are shown in Figure 1.8.

0 2 4 6 8 10 12 140

0.2

0.4

0.6

0.8

1

1.2

Time (hour)

Con

cent

ratio

n gl

uose

(g/L

)

Figure 1.8: Effects of incubation time

22

Based on the result in Figure 1.8, initially the concentration of glucose

produced increased exponentially at the time of 2 – 6 hour. Based on report by Hao et

al., (2010), as substrate loading increased, the end-product inhibition caused by

cellobiose and glucose increased. It is because, the enzyme produces product at an

initial rate that is approximately linear for a short period after the start of the reaction.

When there were higher of time, the more the interaction between enzymes and

substrates.

However, as the reaction proceeds and substrate is consumed, the rate

continuously slows. Based on the Figure 1.8, at the time of 8 – 12 hour the

concentration of glucose started to decreased. Based on report Cinar, (2005), the

higher enzyme loadings resulted in faster total hydrolysis and probably end-product

inhibition occurred. Moreover, Kristense, (1972) reported that a digestibility trials

undertaken on redclaw gastric juice here showed that reducing sugars could be

produced in relatively short time frames suggesting that cellulose digestion by redclaw

occurs rapidly. As redclaw have very short, simple digestive tracts effectively a

straight tube. Digestive enzymes need to act rapidly as digestive times are relatively

rapid.

The best time to be used in order to obtain the maximum yield of glucose is 6

hours. This condition will then used for the effect of enzyme concentration.

4.2.2 Effect of Enzyme Concentration

Based on the result from the effect of enzyme concentration, the best time was

used to study the effect on enzyme concentration. All of the samples were incubated

for 6 hours, in an oven at 40 °C by using a various concentration of enzyme in a range

of 0.2 – 2.5 g/mL. The effects of enzyme concentration on the concentration of

glucose are shown in Figure 1.9.

23

0 0.5 1 1.5 2 2.5 30.8

0.85

0.9

0.95

1

1.05

Concentration enzyme (g/mL)

Con

cent

ratio

n gl

ucos

e (g

/L)

Figure 1.9: Effect of enzyme concentration

Based on the result in Figure 1.9, initially the concentration of glucose

produced increased exponentially at the concentration of 0.5 – 1.5 g/mL. However, at

the concentration of 2.0 – 2.5 g/mL the concentration of glucose started to decreased.

When there were higher concentration of enzyme, the more the interaction

between enzymes and substrates so, the concentration of glucose increased initially.

Based on report by Rajesh et al., (2009), higher enzyme loading rate decreased the

migration time of cellulases on the cellulose surface and increased the liberation of

reducing sugars till the complete surface coverage of the substrate.

Moreover, Sarkar et al., (1998) reported that increase in enzyme concentration

saturated the active sites of the enzyme with substrate leading to lower activity.

Nevertheless, when there are excessive of enzymes, there are higher probabilities of

inhibitors to inhibit these enzymes that caused by proteins interaction. This may

deduce the results that less glucose concentration as it is been proved by the above

Figure 1.9 at the concentration of 2.0 - 2.5 g/mL of enzyme.

Based on a report by Nathalie et al., (2006), the understanding of proteins

interactions is important for plant protection. The protections could be required to

enhance plant resistance. The role of plant cell wall polysaccharides in disease

24

resistance has been reviewed recently. To penetrate and use plant cell walls

nutritionally, pathogens secrete a remarkable array of polysaccharides degrading

enzymes including exopolygalacturonases, endopolygalacturonases, pectin

methylesterases, pectin lyases and pectate iyases, acetyl esterases xylanases and a

variety of endogluconases that cleave cellulose, plants resists hydrolytic attack by

deploying inhibitor proteins of Cell Wall Degrading Enzymes (CWDE).

As a result, the best enzyme concentration was at the 1.5 g/mL. Clearly, these

two parameters; incubation time and enzyme concentration are very important in

concentration of glucose. Thus, these factors are used for further study in the

optimization to obtain higher yield of glucose concentration.

4.3 Determination of the Optimum of Parameters Sugar Production

Using Response Surface Methodology (RSM)

Optimization on the parameters in glucose productions are performed using

response surface methodology (RSM). The parameters involved are incubation time

and enzyme concentration. The low level and high level of the parameters are

determined from the previous result. Table 2.2 shows the low level and high level of

each parameter.

Table 2.2: Low level and high level of parameters

Parameters Low Level High Level

Incubation Time (hr) 4 8

Enzyme Concentration (g/mL) 1.0 2.0

25

By using central composite design (CCD), the experiments with different

combination of enzyme concentration and reaction time were performed. Experiments

arranged by Design Expert Software and the producing of concentration of glucose

are listed Table 2.3

Run 4 gave the highest concentration of glucose which produced 1.063 g/L of

concentration. The parameters of Run 4 are: 1.50 g/mL of enzyme concentration and 6

hours of incubation time. Conversely, Run 3 gave the lowest concentration of glucose

which produced only 0.8122 g/L of concentration. The parameters of Run 3 are: 1.00

g/mL of enzyme concentration and 8 hours of incubation time. This is because,

according to Marimuthu et al., (2005), less prolonged reaction time, the better sugar

yields. Moreover, Sun and Cheng, (2002) reported that a increasing the dosage of

cellulases in the process, to a certain extent, can enhance hydrolysis yields.

Table 2.3: Experiments designed by design expert software

Run Factor 1 A: Time (hr)

Factor 2 A: Enzyme Concentration (g/mL)

Response: Reducing Sugar

(g/L)1 3.17 1.50 12 6.00 0.79 0.98983 8.00 1.00 0.81224 6.00 1.50 1.0635 6.00 1.50 1.0226 4.00 2.00 1.047 8.00 2.00 0.88328 8.83 1.50 0.79219 4.00 1.00 1.05310 6.00 2.21 0.993211 6.00 1.00 1.042

26

Table 2.4 shows the ANOVA and regression analysis for the concentration of

glucose. The precision of a model can be checked by determination coefficient (R2)

and correlation confession (R). As a rule, a regression model having an R2 value

higher than 0.9 is considered to have a very high correlation (Haaland, 1989). The

value of R indicates better correlation between the experimental and predicted values.

In Table 2.4, the P-value obtained for regression model 0.0006 compared to a

desired significant level of 0.05. This signified that the regression model is precise in

predicting the pattern of significance to the concentration of glucose.

Table 2.4: ANOVA for response surface quadratic model for the concentration of

glucose.

Sources Sum of Square

Degree of Freedom

Mean Square

F-Value

P-Value (Prob>F)

R2

Model 0.092 50.018

35.83 0.0006 0.9728 Significant

A-Enzyme concentration 0.060

10.060

116.89 0.0001

B-Incubation Time 4.931E-

004

14.931E-

004

0.96 0.3713

A20.030

10.030

57.72 0.0006

B23.413E-

13.413E-

6.67 0.0492

27

003 003Residual

2.558E-003

55.115E-

004Lack of Fit

1.717E-003

35.723E-

004

1.36 0.4500 Not Significant

Pure Error8.407E-

004

24.203E-

004

Correlation Total 0.094

10

To investigate the effects of the two parameter on the concentration of glucose,

the response surface methodology was used and the three-dimensional plot was drawn.

Figure 1.10 shows the response surface plot for the parameters studied; incubation

time and enzyme concentration.

Incubation time and enzyme concentration gave the significant effect to the

concentration of glucose (Figure 1.10). Higher enzyme concentration and shorter

incubation time increase the concentration of glucose, whereas lower enzyme

concentration and longer incubation time decrease the concentration of glucose. The

maximal concentration of 1.063 g/L glucose was obtained when using 1.5 g/mL of

enzyme concentration and 6 hours of incubation time.

Based on report by Marimuthu et al., (2005), less prolonged reaction time, the

better sugar yields. Moreover, Sun and Cheng, (2002) reported that a increasing the

dosage of cellulases in the process, to a certain extent, can enhance hydrolysis yields.

The production of total and individual soluble glucose reached the maximal level after

five hours of reaction. Extending the reaction beyond five hours enhanced the yields of

glucose, but did not result significant increase in the concentration of total soluble

glucose from cassava residue.

28

Figure 1.10: Response surface plot of glucose concentration: Incubation time vs.

enzyme concentration.

4.4 Optimization of Sugar Production Using Response Surface Methodology

(RSM)

Based on the Table 2.5, the concentration of glucose was successfully

optimized after the optimization process was carried out. The Incubation time and

enzyme concentration used was reduced after optimization. Table 2.5 shows summary

of the optimized parameters for concentration of glucose.

Table 2.5: Summary of the optimized parameters for sugar production

Parameters Before Optimization After Optimization

Valu Concentration Value Concentration Glucose (g/L)

29

e Glucose (g/L)

Incubation

Time (hr) 8.83

0.7921

4.47

Predict Experimental

1.066 1.059Enzyme

concentration

(g/mL)

1.50 1.32

CHAPTER 5

CONCLUSION AND RECOMMENDATIONS

5.1 Conclusion

30

The concentration of glucose was successfully obtained by hydrolyzing the

cassava residue by using hydrolyses enzymes. Three types of enzymes which are

cellulase, glucoamylase and α-amylase have been employed to hydrolyze the substrate;

cassava residue to produce glucose. The best enzyme that yields the most glucose is

cellulose, followed by α-amylase and glucoamylase.

Therefore, cellulose is used for further parameters studied by (i) incubation time

at 4 – 8 hours and (ii) enzyme concentration in the range of 0.5 – 2.5 g/mL. Based on

the result before optimization, the response surface methodology shows that the

conditions in concentration of glucose by using 1.50 g/mL of enzyme concentration at

8.83 of incubation time producing only 0.7921 g/L of reducing glucose. However after

optimization occurred, the concentration of glucose was successfully increased until

1.059 g/L using the optimum condition of 1.32 of enzyme concentration at 4.47 of

incubation time.

This research demonstrated that this agricultural waste of cassava residue which

had no economical value could be converted by enzymatic hydrolysis to a more

valuable product.

As a result, with optimum use of cassava residue, it will not only solve the

environmental pollution problems but also will give back a high economic value

product to the cassava starch plant industry above all.

5.2 Recommendations

In order to improve this research method and results to a better concentration

of glucose, it can be carried out in various modifications on these parameters. The

concentration of glucose using the method of enzymatic hydrolysis can also be done

using the method of acid hydrolysis which is cheaper in terms of the cost of raw

material. Besides using only single enzyme, the concentration of glucose can be

increased by mixing the three commercialized enzymes (cellulose, glucoamylase and

α-amylase). There are also differences when using immobilize enzyme and free

enzyme in concentration of glucose which can be studied to compare the concentration

between the two types of enzymes.

31

The concentration of glucose was analyzing using DNS reagent. But for future

studies, analyzing the samples using High Performance Liquid Chromatography

(HPLC) can gives more accurate concentration of glucose. In this study, the substrate

used is cassava residue. In future, to study the concentration of substrate will give the

significant in concentration of glucose. Moreover, there are more substrate other than

cassava residue that are also capable of producing sugar such as Cotton Stalk, Corn

Stover, Wheat Straw, Tobacco Stalk and Palm Oil Empty Fruit Bunch.

This research can also be done by varying the agitation rate and the reaction

temperature while incubating. The pH of the buffer may give effect on the

productions. Therefore, this can also be study by varying the pH of buffer. In addition,

other kind of buffer can also be used such as Potassium Phosphate and Ampso.

REFERENCES

Adinarayana, K. and Suren, S. (2005). Response Surface Optimization of Enzymatic

Hydrolysis of Maize Starch for Higher Glucose Production. Biochemical

Engineering. 27: 179-190

Ammbati, P. and Ayyanna, C. (2001). Optimization of Medium Constituents and

Fermentation Condition for Citric Acid Production from Palmyra Jaggery

using Response Surface Methodology. World Journal of Microbiology and

Biotechnology. 17: 331-335

Badal, C. S. (2004). Production, purification and properties of endoglucanase from a

32

newly isolated strain of Mucor circinelloides. Process Biochemistry.

39: 871-1876

Cecilia, L. Joao, O. T. Karen, F. O. Crisla, S. S. and Meline, R. M. (2009).

Optimization of temperature, sugar concentration, and inoculum size to

maximize ethanol production without significant decrease in yeast cell

viability. Biotechnological Products and Process Engineering. 83: 627-637

Chauhan, B. and Gupta, R. (2004). Application of Statistical Experimental Design for

Optimization of Alkaline Protease Production from Bacillus sp. RGR-14.

Biochemistry Process. 39: 2115-2122

Gaur, R. and Gupta, A. (2008). Lipase from Solvent Tolerant Pseudomonas

aeruginosa strain: Production Optimization by Response Surface

Methodology and Application. Bioresource Technology. 99: 4796-4802

Gunda, K.P.B. and Walteir, L. (1970). Enzymatic Degradation of Cellulose,

Cellulose Derivatives and Hemicelluloses In Relation to the Fungal Decay of

Wood 1. Wood Science and Technology. 4: 273-283

Hao, F. Chen, Z. and Xiang, Y.S. (2010). Optimization of enzymatic hydrolysis of

steam-exploded corn stover by two approaches: Response surface methodology

or using cellulase from mixed cultures of Trichoderma reesei RUT-C30 and

Aspergillus niger NL02. Biosource Technology. 101: 4111-4119

Sowbhagya, H.B. Srinivas, P. and Krishnamurthy, P. (2010). Effect of enzymes on

extraction of volatiles from celery seeds. Food Chemistry. 120: 230-234

Herrera, S. J. Tellez, L. Ramirez, J. A and Vazquez, M. (2003). Production of Xylose

from Sorghum Straw using Hydrochloric Acid. Cereal Science.

37: 267-274

Houjg, J.Y. Chen, K. C. and Hsu, W.H. (1989). Optimization of Cultivation Medium

Composition for Isoamylase Production. Journal of Applied Microbiology and

Biotechnology. 39: 61-64

Howard, R.L.A. Jansen, E.V.R. and Howard, S. (2003). Lignnocellulose

Biotechnology: Issues of Bioconversion and Enzyme Production. African

Biotechnology. 2: 602-619

Inci, C. (2005). Effects of cellulase and pectinase concentrations on the

colour yield of enzyme extracted plant carotenoids. Process Biochemistry. 40:

33

945-949

Jian, X. and Shijie, L. (2009). Optimization of Ethanol Production from Hot-water

Extracts of Sugar Maple Chips. Renewable Energy. 34: 2353-2356

Jin, Z. Yong, H.W. Ju, C. Ling Z. L. Ying, P. Z. and Si, L. Z. (2010. Optimization of

cellulase mixture for efficient hydrolysis of steam-exploded corn stover by

statistically designed experiments. Biosource Technology. 101: 4111-4110

Kristensen, J.H (1972). Carbohydrases of Some Marine Invertebrates With Notes on

Their Food and on the Natural Occurrence of the Carbohydrates Studied.

Biotechnology.14: 130–142.

Kyung, H.C, Hee, S.J, Na, K.L. Se, H.P, Na, W.L. and Hyun, D.P. (2009).

Optimization of the Enzymatic Production of 20 (S)-Ginsenoside Rg3 from

White Ginseng Extract using Response Surface Methodology. New

Biotechnology. 26: 3-4

Li, L. Liu, X. Guo, Y and Ma, E (2005). Activity of the enzymes of the antioxidative

system in cadmium-treated Oxya chinensis (Orthoptera Acridoidae).

Environmental Toxicology and Pharmacology. 20: 412-416

Mark, R. W. Wilbur, W.W. Karel, G. and Randall G.C. (2007). Hydrolysis of

grapefruit peel waste with cellulase and pectinase enzymes. Bioresource

Technology. 98: 1596–1601

Cazetta, M.L. Celligoi, M.A.P.C. Buzato, J.B. Scarmino, I.S. and Silva, R.S.F.

(2005). Optimization Study for Sorbitol Production by Zymomonas Mobilis in

Sugar Cane Molasses. Process Biochemistry. 40: 747-751

Marimuthu, J. Ye, W.Z. In, W.K and Jung, K. L. (2009). Enhanced Saccharification

of Alkali-Treated Rice Straw by Cellulase from Trametes Hirsuta and

Statistical Optimization of Hydrolysis Conditions by RSM. Bioresource

Technology.100: 5155-516

Mutanda, T. Wilhelmia, B. S. and Whiteley, C. G. (2008). Response Surface

Methodology: Synthesis of Inulooligosaccharides with an Endoinulinase from

Aspergillus Niger. Enzyme and Microbial Technology. 43: 362-368.

Nathalie, J. (2006). Plant Protein Inhibitors of Cell Wall Degrading Enzymes. Trends

In Plant Science. 23: 359-367

Rajesh, S. Rajender, K, Kiran, B and Narsi, R.B. (2009). Optimization of Synergistic

34

Parameters for Thermostable Cellulase Activity of Aspergillus Heteromorphus

using Response Surface Methodology. Biochemical Engineering. 48: 28-35

Richard F. T. John, K. and Xi, Q. (2004). Starch-Composition, Fine Structure and

Architecture. Journal of Cereal Science. 39: 151-165

Rahman, S.H.A. Choudhury, J.P. Ahmad, A.L. and Kamaruddin, A.H. (2007)

Optimization Studies on Acid Hydrolysis of Oil Palm Empty Fruit Bunch Fiber

for Production of Xylose. Bioresource Technology. 98: 554-559

Sun, Y. and Cheng, J. (2002). Hydrolysis of Lignocellulosic Materials for Ethanol

Production: A review. Bioresoure Technolology. 83: 1–11

Steve, J. (2004). Improving starch for food and industrial applications. Current

Opinion in Plant Biology. 7: 210-218

Suthkamol. S. Ancharida, B. F. and Teerapatr, S. (2006). Conversion of Cassava

Waste into Sugar Using Aspergillus Niger and Trichoderma reesei for Ethanol

Production. Biotechnology. 54: 234-240

Teerapatr. S, Cholada, S. Bangatrat, P. Wichien, K. and Sirintip, C. (2004). Utilization

of Waste from Cassava Starch Plant for Ethanol Production. Sustainable

Energy and Environment (SEE). December 1-3. Hua Hin, Thailand, 360 – 365

Teerapatr, S. Lerdluk, K and La, S. (2006). Approach of Cassava Waste Pretreatments

for Fuel Ethanol Production in Thailand. Biotechnology. 31: 241-246

Xiao, M.X. Alys, J.A. Neil, A.R. Alex, J.A. Gang, P.X. and Peter, B.M. (1999).

Charac terisation of Cellulase Activity in the Digestive System of the

Redclaw Crayfish (Cherax quadricarinatus). Aquaculture. 180: 373-386

APPENDIX A

MATERIAL AND METHODS

Appendix A1

35

Preparation mixture of Sodium Hydroxide (NaOH), (2000 mL) and Citric Acid

(C6H8O7), (1000 mL) as Buffer

i) Preparation of Sodium Hydroxide (NaOH), (2000 mL)

Molecular weight of Sodium Hydroxide, NaOH = 40 g/mol

Prepare volume of 10.8 mL from 0.1 M Sodium Hydroxide (NaOH) solution

Then, 10.8 mL Sodium Hydroxide ( NaOH) was diluted with 1000 mL

distilled water in volumetric flask.

ii) Preparation of Citric Acid (C6H8O7) , (1000 mL)

Molecular weight of Sodium Hydroxide, NaOH = 192.14 g/mol

Prepare mass of 19.214 g from 0.1 M on Citric Acid (C6H8O7)

Therefore, 19.2 g of Citric acid (C6H8O7) was mixed into 1000 mL distilled

water.

iii) Preparation of buffer

Then, add some drop of Sodium Hydroxide, (NaOH) and Citric Acid

(C6H8O7) into the mixture to achieve the value of pH needed.

Appendix A2

Preparation of DNS Reagent (1 L)

Table A – 1: Ingredients of DNS Reagents

No. Chemical g/L

1. DNS Acid 10

36

2. Phenol Crystal 2

3. Sodium Sulfite 0.5

4. Sodium Hydroxide 10

5. Potassium – Sodium Tartarate 182

Appendix A3

1. Preparation of 1 mg/mL of Glucose Standard Solution (10 mL)

1 mg/mL = 1 mg of glucose in 1 mL buffer

1 mL = 1 mg

10 mL = 10 mg

Hence, 10 mg of glucose is added into 10 mL of buffer (Mixture of 0.1 M

Sodium Hydroxide, NaOH with 0.1 M Citric Acid)

37

2. Standard Curve of Glucose

Concentration Glucose (g/L) OD (λ = 540 nm)

0.2 0.424

0.4 1.158

0.6 1.834

0.8 2.276

1.0 2.482

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1.10

0.5

1

1.5

2

2.5

3

f(x) = 2.61700000000001 x + 0.064599999999994R² = 0.957209038006657

Standard Curve of Glucose

Concentration glucose (g/L)

A 5

40

Figure A – 1: Standard Curve of Glucose

Appendix A 4

Enzyme Dilution

(W enzyme / V buffer) % = g/mL

Thus, W enzyme = 0.5 g, 1.0 g, 1.5 g, 2.0 g, 2.5 g

V buffer = 100 mL

Therefore, add each weight of enzyme into 100 mL of buffer buffer (Mixture of 0.1 M

Sodium Hydroxide, NaOH with 0.1 M Citric Acid).

38

APPENDIX B

OPTIMIZATION PROCESS

Appendix B1

ANNOVA Results for Optimization of Cultural Conditions

39

Figure B – 1: Graph of the normal plot of residual of sugar production

40

Figure B-2: Residual versus predicted values of sugar production

Figure B-3: Residual values of sugar production for each run experiment

41

Figure B-4: Residuals versus time values of sugar production

Figure B-5: Graph of the Outlier T of sugar production

42

Figure B-6: Graph o the Cook’s distance of sugar production

Figure B-7: Leverage versus run values of sugar production

43

Figure B-8: Graph of predicted versus actual values of sugar production

Figure B-9: Plot of the Box-Cox for power transforms