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thesis entitled

TWIN SCREW EXTRUSION OF TEFF FLOUR

presented by

Muluken Engida Tilahun

has been accepted towards fulfillment
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TWIN SCREW EXTRUSION OF TEFF FLOUR

By

Muluken Engida Tilahun

A THESIS

Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of

MASTER OF SCIENCE

Department of Agricultural Engineering

1995

ABSTRACT

TWIN SCREW EXTRUSION OF TEFF FLOUR

Muluken Engida Tilahun

Teff (Eragrostis tefi) is a small seeded millet-like cereal grain. Teff is one of the
exotic grains appearing in health food stores in the U.S.A. Teff flour was successfully
extruded with a ZSK-30 co-rotating twin-screw extruder. A wide range of processing
variables were considered: feed moisture content (20-30%), barrel temperature (125-
175°C), and screw speed (100-300 rpm). Effects of varying process variables on
extrudate quality (expansion ratio, bulk density, water absorption index, water solubility
index, product moisture content, and color) and extruder response variables (percent
torque, specific mechanical energy, die temperature, and die pressure) were investigated.
Results of the study were analyzed using response surface methodology. Second order
polynomials were computed to model the measured process and product variables. The
resulting regression equations were used to generate response surfaces.

The overall results show that teff flour can be processed using a twin-screw
extruder to produce product properties similar to other extruded products currently used

in the food industry, and the calculated second order polynomials are sufficiently accurate

Muluken Engida Tilahun

and valuable for predicting properties of teff extrudates and extruder response with in the

range of variables considered.

ACKNOWLEDGEMENTS

I would like to express my sincere gratitude and thanks to Professor Ajit K.
Srivastava for serving as my advisor and for his guidance, comments, suggestions,
support, encouragement, enthusiasm, friendship, and patience. It has been an honor to
work with him. Thanks to Professor James F. Steffe and Professor Mark A. Uebersax for
serving as members of my committee and for their technical advice.

Thanks to the Rotary International for providing financial support for most part
of my graduate study. Thanks to Professor Robert D. von Bemuth for providing financial
support in the form of assistantship and funds to cover the research expenses. Thanks to
the composite materials and structures center of MSU and Michael J. Rich for the use of
their facilities and technical support.

Many thanks to my close friends: Misael and Philip who helped, encouraged, and
befriended me throughout my graduate study. I want to thank graduate students: Adnan,
Neba, Habib, Danny, Chris, Mohammed, Andrew, George, James, Geoffrey, Rick, and
Andy Wedel for making my stay more pleasant here at MSU.

Special thanks go to the author’s parents, Birtucan Mengesha and Tilahun Engida,
for their continued support, prayers, patience, and encouragement. Many thanks are due
to my wife, Mulusew yayehyirad for her love, support, encouragement, understanding and

tolerance.

iv

TABLE OF CONTENTS

Chapter Page
LIST OF TABLES .............................................. ix
LIST OF FIGURES ............................................. xi
NOMENCLATURE ............................................ xiii
I. INTRODUCTION ............................................ l
1.1 Economic Significance of Teff .............................. 2

1.1 Teff Products .......................................... 4

1.3 Discussion of the Need ................................. p . . 6

1.4 Objectives ............................................ 7

11. REVIEW OF LITERATURE .................................... 8
2.1 Extrusion Cooking ...................................... 8

2.1.1 Definition of Extrusion Cooking ...................... 8

2.1.2 Advantages of Extrusion Cooking .................... 10

2.1.3 Single Screw Extruders ........................... 11

2.1.4 Twin-Screw Extruders ............................ 15

2.1.4.1 Types of Twin-Screw Extruders ............... 15

2.1.4.2 Counter Rotating Twin-Screw Extruders ......... l6

2.1.4.3 Co-rotating Twin-Screw Extruders ............. 18

2.1.4.3.1 Screw Geometry ................... 19
2.1.4.3.2 Transport Phenomena ................ 20
2.1.4.3.3 Residence Time Distribution ........... 23
2.1.4.3.5 Mixing .......................... 26

2.1.5 Comparison between Single and Co-rotating Twin-Screw

Extruders ..................................... 26

2.1.6 Extrusion Cooking of Cereals and Starch ............... 28

2.1.6.1 Expansion .............................. 30

2.1.6.2 Solubility ............................... 31

2.2 Response Surface Methodology (RSM) ....................... 32
2.2.1 Basic Concepts and Assumptions of RSM .............. 33

2.2.2 The Applications of RSM in Food Processing ............ 35

2.3 Factorial Design ....................................... 37
2.3.1 Fractional Factorial Design ......................... 37

III. MATERIALS AND METHODS ................................ 40
3.1 Extruder ............................................ 42
3.2 Extrusion Conditions ................................... 47
3.3 Functional Testing of Extrudates ........................... 48
3.3.1 Product Moisture Content .......................... 48

3.3.2 Expansion Ratio ................................ 48

3.3.3 Bulk Density .................................. 48

vi

3.3.4 Water Absorption Index and Water Solubility Index ....... 49

3.3.5 Color ........................................ 49

3.4 Experimental Design and Statistical Analysis ................... 50

IV. RESULTS AND DISCUSSION ................................. 53
4.1 Model Fitting ........................................ 53

4.2 Expansion Ratio ....................................... 54

4.3 Bulk Density ......................................... 60

4.4 Water Absorption Index ................................. 63

4.5 Water Solubility Index .................................. 66

4.6 Product Moisture Content ................................ 69

4.7 Percentage Torque ..................................... 71

4.8 Die Temperature ...................................... 74

4.9 Die Pressure ......................................... 76

4.10 Specific Mechanical Energy .............................. 78

4.11 Color ............................................. 81

V. SUMMARY AND CONCLUSIONS .............................. 88
VI. SUGGESTIONS FOR FURTHER RESEARCH ...................... 90
APPENDICES ................................................ 91

APPENDIX A: Raw Data Table for Twin-Screw Extrusion of Teff flour . . 91
APPENDD( B: ANOVA and Stepwise Regression Tables for
Dependent variables ............................ 92

APPENDD( C: Motor Nameplate Data for ZSK—30 Extruder ......... 103

vii

BIBLIOGRAPHY

viii

Table

1.1

3.1

3.2

3.3

4.1

4.2

4.3

4.4

4.5

4.6

4.7

4.8

4.9

4.10

4.11

LIST OF TABLES

Page
Estimated Area Under Cultivation and Production of Major Crops
in Ethiopia .............................................. 3
Compositions of Teff Flour .................................. 41
Extruder Screw Configuration ................................ 44
Experimental Design for Teff Extrusion ......................... 52
Estimated Regression Coefficients for Expansion Ratio .............. 54
Estimated Regression Coefficients for Bulk Density ................. 60
Estimated Regression Coefficients for Water Absorption Index ......... 63
Estimated Regression Coefficients for Water Solubility Index .......... 67
Estimated Regression Coefficients for Product Moisture Content ........ 69
Estimated Regression Coefficients for Percent Torque ............... 72
Estimated Regression Coefficients for Die Temperature .............. 74
Estimated Regression Coefficients for Die Pressure ................. 76
Estimated Regression Coefficients for Specific Mechanical Energy ...... 79
Estimated Regression Coefficients for Color ’L’ ................... 82
Estimated Regression Coefficients for Color ’a’ ................... 82

ix

4.12 Estimated Regression Coefficients for Color ’b’ ................... 83

4.13 Summary of Estimated Regression Coefficients and P values for

Dependent Variables ...................................... 87

Figure
2.1
2.2
2.3
2.4
2.5
3.1
3.2
3.3

4.1

4.2

4.3

4.4

4.5

LIST OF FIGURES

Page
Single-flighted Extruder Screw ............................... 12
Typical Components of Single-Screw Extruder .................... 14
Screw Configurations of Twin-Screw Extruders ................... 17
Kneading Elements of a Twin-Screw Extruder .................... 21
Self Wiping Screw Profiles of Twin-Screw Extruder ................ 24
ZSK—30 Twin-Screw Extruder ............................... 43
Food Die used in Teff Extrusion .............................. 45
Screw Profile used in Teff Extrusion ........................... 46
Expansion Ratio versus Feed Moisture and Barrel Temperature
at 200 rpm Screw Speed .................................... 58
Bulk Density versus Feed Moisture and Barrel Temperature
at 200 rpm Screw Speed .................................... 62
Water Absorption Index versus Barrel Temperature and Screw Speed
at 25% Feed Moisture ..................................... 65
Water Solubility Index versus Feed Moisture and Barrel Temperature
at 200 rpm Screw Speed .................................... 68

Product Moisture Content versus Feed Moisture and Barrel Temperature

xi

4.6

4.7

4.8

4.9

4.10

4.11

at 200 rpm Screw Speed .................................... 70
Percent Torque versus Barrel Temperature and Screw Speed

at 25% Feed Moisture ..................................... 73
Die Temperature versus Feed Moisture and Barrel Temperature

at 200 rpm Screw Speed .................................... 75
Die Pressure versus Barrel Temperature and Screw Speed

at 25% Feed Moisture ..................................... 77
Specific Mechanical Energy versus Feed Moisture and Screw Speed

at 150°C Barrel Temperature ................................ 80
Color ’L’ versus Feed Moisture and Screw Speed at 150°C Barrel

Temperature ............................................ 85
Color ’b’ versus Feed Moisture and Screw Speed at 150°C Barrel

Temperature ............................................ 86

xii

DP

DT

ER

E(t)
F(t)
H

H(9)

NOMENCLATURE

Color parameter

Color parameter

Axial distance between the flights (m)
Bulk Density (kg/m3)

Center line distance between screws (m)
Internal diameter of the barrel (m)

Die pressure (kPa)

Die temperature (°C)

Flight width perpendicular to the flight (m)
Expansion ratio

Exit age distribution of flow
Cumulative residence time distribution
Flight height (m)

Channel depth (m)

HTST High temperature short time

K

L

,L’

Consistency coefficient (Pa s“)
Fill length (m)

Color parameter

xiii

MC

AP

PMC

Length of the die channel (m)

Moisture content (%wb)

Flow behavior

Screw speed (rpm)

Number of parallel flights (thread starts) in a screw
Pressure rise (MPa)

Product moisture content (% wet basis)
Flow rate, extruder output (kg/h)

Flow rate in one die channel (kg/h)

Screw radius (m)

Die channel radius (m)

Outer radius of screw (m)

Response surface methodology

Screw pitch (m)

Specific mechanical energy (kJ/kg)
Residence time in the conveying section (3)
Residence time for a screw element (5)
Residence time in the melt pumping section (s)
Temperature (°C)

Average residence time (5)

Percent torque

Volume of one chamber (m3)

xiv

W Perpendicular distance between the flights along the helical path (m)
WAI Water absorption index (g/g)

WSI Water solubility index (g/g)

xi Coded design variable

Xc Actual value of experimental center point of Xi

AX, Unit change of variable Xi

Z Axial distance along the length of the screw (m)

on Drag flow geometry factor (m3)

B Pressure flow geometry factor (m‘)

B, Coefficient (empirical parameter)

0 Angle of rotation (rad)

«1) Angle of helix (rad)

8, Distance between the crest of screw flight and the inner surface of the barrel (m)

u Apparent viscosity (Pa '8)

1] Response variable

XV

CHAPTER I

INTRODUCTION

Eragrostis teff, called teff in the official language of Ethiopia, Amharic is a small
seeded millet-like cereal grain indigenous to Ethiopia. The cereal teff constitutes an
important part of the staple diet of Ethiopians. The seeds of teff grain are very small
($0.002 g or less than 1.5 mm in length). As in other small-seeded cereals, the embryo,
which is rich in protein and lipid, occupies a relatively large proportion of the grain
(Parker et al., 1989) and produces nutritious flour upon milling.

The total global production of small millets including teff, is many times less than
that of the three major cereals— wheat, rice, and corn. However, the small millets are used
as a major food staples under conditions where the major cereals are not adapted or as
a complement to the major cereals and legumes. The minor millets have been well
described by Rachie (1975) and Hulse et al. (1980). As an important food crop grown
in Ethiopia, and one which possesses characteristics enabling it to maintain its yield even
under adverse agronomic conditions, teff plays an important role in Ethiopia’s economy.

Teff is one of the exotic grains appearing in health food stores in the U.S.A. Teff
is being used as a source of protein (6 to 10%), calcium, fiber and iron for nutrition

minded Americans. The amino acid analysis of the cereal teff was made by Jansen et al.

2
(1962) and Lester and Bekele (1981). Its appealing, sweet and molasses-like flavor

makes teff a perfect alternative grain for people allergic to the gluten in wheat and related

problems (Sokolov, 1993).

1.1 Economic Significance of Teff
Teff (Eragrostis tefi‘) is an important cereal cultivated and utilized in Ethiopia.
As shown in Table 1.1 teff occupies about 29% of the cultivated land under cereals while
sorghum 18%, barley 18%, com 17%, wheat 13%, and finger millet 5%.
The significant contribution of teff to Ethiopia’s economy may be summarized as:
1. It is the crop that is produced on the largest area used for cereal production in
Ethiopia with an annual production of 1.2 million tones;
2. The most important cereal to farmers, bringing the highest price in the market as
compared to any other cereal;
3. The most consumer preferred grain for making injera (bread);
4. Agronomically versatile and reliable cereal crop even under adverse conditions
such as low moisture stress, water logging and not attacked by storage pests

such as weevils.

Table 1.1 Estimated area under cultivation and production of major crops in
Ethiopia, 1977-78 to 1985-1986.

Hectarag I (’000 (’000) ,

Range I Mean a Range Mean %
487-779 _»3_ 1429'1 f 641 _ --.
796-927 850 18 ' 690-1168 910 18
653-947 830 17 L 948-1603 1205 23
215238 227 5 , 187-240 203 _
726-1026 865 18 “507-1643 1069 21 ‘

1296-1513 29 : 912-1426 1141
. was. reg ‘ ‘cepartmt 3’: . ”To. _rnrstry ’0'
Agriculture, Addis Ababa.
Central Statistical Authority, 1987. Time series data on area production and yield of
major crops, 1979/80 to 1985/86. Statistical bulletin 56, CSA, Addis Ababa.

 
  
   
 
  

 

 

 

 

 

 

   
     

 

 

 

 

 
    

 

 

1.2 Teff Products

Teff grain is used for making several types of flat breads that form the basic
traditional diet of Ethiopians. Teff is also used, to a lesser extent, in porridge, tella (local
beer), and katikalla (local spirit). Teff is not suitable for making leavened bread because
the flour lacks gluten, but when available, wheat and other cereal flours may be mixed
with teff flour for a variety (Stewart and Asnake, 1962). Recently, the Natural Resources
Institute of U.K. found that at least 20% hammer-milled teff flour can be added to the
wheat flour and excellent bread produced.

The most popular type of flat bread is injera, a flexible, spongy, pancake-er
fermented and baked product perforated with ’eyes’. In the traditional process of making
injera the dough is prepared by mixing teff flour with water and allowing the endogenous
flora to perform fermentation, or irsho, a thin paste saved from the previous fermentation
may be added as a starter culture. While preparing for baking, the liquid that settled on
the dough at the end of the primary fermentation is discarded. Then, absit, a boiled
portion of the fermented dough is added to the bulk dough to initiate the secondary
fermentation which usually lasts about one hour. The fermented dough, which has the
consistency of batter, is poured onto the hot oiled surface of the pan or metad, which is
a round smooth clay griddle. The metad is then covered with a tight-fitting lid to retain
the steam and baked for a few minutes to produce injera.

In bread made from wheat flour, the endospenn stored proteins form a continuous
gluten network, in which starch granules and gas bubbles are held (Bechtel et al., 1978).

However, according to Parker et al. (1989), teff storage proteins play no part in the

5

structural integrity of injera. However, they may add to the texture and the major
contributor to the injera matrix is gelatinized starch. This type of steam-leavened starch
matrix is also found in commercially-produced wafers made from wheat flour. In wafer
production, as in the cooking of injera, it is important that batter-like dough have a higher
water content, that a steamy atmosphere be maintained throughout the cooking period, and
that heat be efficiently transferred from the cooking surface (Stevens, 1976).

As compared to other cereals, information on processing of small millets like teff
for food and industrial uses is very limited. However, Malleshi (1989) indicated that
small millets can be processed to diversify their uses and to improve their nutritive value
and consumer acceptability. Milled or decorticated millets could be used in preparation
of flakes, quick-cooking cereals, or extruded products.

Extrusion is an efficient food processing method in terms of energy consumption,
because it combines a number of unit operations like mixing, heating, shearing, and
texturization. The high temperature short time extrusion cooking has proven to achieve
microbiological safety and stability, without running the risk of overcooking, discoloration
or damage to nutritional or functional properties (Smith and Ben—Gera, 1980).
Applications of extrusion cooking include the production of pregelatinized and modified
starches, expanded cereals and confectionery products, bread crumbs, biscuits, crackers,
baby foods, chewing gum, pet foods and texturized protein foods.

Extrusion cooking of cereals has been studied by many researchers including:
cereal starches and corn semolina by Mercier and Feillet (1975); corn grits by El-Dash

et al. (1983), Owusu-Ansah et al. (1983, 1984); corn, rice, and potato flours with whey

6
protein concentrate by Kim and Maga (1987); sorghum by Phillips and Falcone (1988);

wheat flour, rye flour, oat grits and whole grain barley by Vainionpaa (1991);
degerminated yellow corn meal by Halek and Chang (1991); rice flour by Grenus et al.
(1993).

As compared to the single-screw extruders, modern twin-screw extruders are more
intricate, offer better control of internal shear and residence time of heat sensitive
materials, and can be used for processing very low-moisture products to minimize drying
requirement (Harper, 1981). Although twin-screw extruders have a variety of designs, the

co-rotating, intermeshing screw type has gained the widest acceptance.

1.3 Discussion of the Need

There is a growing interest in producing and selling this mini-grain in the U.S.A.
Wayne Carlsol of Caldwell, Idaho and Wokinesh Spice Blends, Inc. of Oshtemo,
Michigan are growing and selling teff as a cash crop.

In the modern world, snacks and extruded products are becoming increasingly
popular and important. Ms. Rebecca T. Wood of Caldwell, Idaho has prepared ten
recipes for cooking with teff: teff waffles, jalapeno teff fillets, great chocolate cake,
chocolate mint refrigerator cookies, moroccan chicken stew, muffins, teff banana bread,
double-teff butter-pecan tea cakes, teff breakfast cereal, and date cake. Such encouraging
start may help promote the use of teff in other foods as well.

Extrusion cooking of teff could produce a healthier breakfast cereal of acceptable

taste and nutritional value. The cereal may possibly be used by people who suffer from

7

grain allergies. In Ethiopia, the production of teff is currently limited to domestic
consumption. If teff can be used as an alternative to substitute some of the wheat or
other cereals, this may favor agricultural development and commerce in its country of
origin (and could potentially do the same in other countries).

The effect of raw material on extrusion stability, output and product quality is
dependent on the extruder process parameters. In normal cereal extrusion, three variables:
water feed rate, barrel temperature, and screw speed are critical to control product
characteristics. Extrusion cooking of cereals like wheat, rice, corn, and oats has been

widely studied. However, published work on teff-extrusion is non-existent.

1.4 Objectives
The main objective of this study was to examine the extrudability of teff flour.
The specific objectives of this work were:
1. To study the effects of primary extrusion processing variables: feed moisture,
barrel temperature and screw speed on extrusion of teff flour;
2. To study the functional properties of extrudates;
3. To study the relation between product properties and the extrusion processing

parameters used.

CHAPTER 11

REVIEW OF LITERATURE

2.1 Extrusion Cooking
2.1.1 Definition of Extrusion

Extrusion has been defined as a process that involves forcing a material to flow
under a variety of controlled conditions and to pass through a shaped hole or slot at a
predetermined rate. Extrusion can be used for different functions such as mixing,
cooking, forming, puffing depending on the extruder design and the process conditions.
Extrusion is used in the production of pasta, breakfast cereals, biscuits, crackers, crisp
breads, baby foods, snack foods, confectionery items, chewing gum, texturized vegetable
proteins, modified starches, pet foods, dried soaps, and dry beverage mixes (Linko et al.,
1983).

Extruders can be categorized into one of three main types: piston extruders, roller
extruders, and screw extruders (Thorz, 1986). Piston extruders are mainly used for
forming. They consist of a single or a group of pistons which deposit quantities of
materials onto a conveyor. Roller extruders consist of two counter-rotating rolls with

smooth or profiled surfaces. They are also used as forming machines. Screw extruders

9

have single, twin, or multiple screws rotating with in a stationary barrel to push the
material forward through a die.

Early applications of extrusion to food date back to the mid- to late 1800’s to the
production of sausage and processed meats. A piston extruder to stuff casings, and a
simple food chopper having a single screw to force soft foodstuffs through a die plate
were used for these applications (Harper, 1980). Another early application of food
extrusion to food processing was in pasta production following the invention of a
hydraulically operated, cylindrical ram macaroni press. In 1935, the first single-screw
extruder, the pasta press was applied in the food industry as a continuous system. The
pasta press served the purposes of ingredient mixing, dough forming and forcing it
through dies creating the desired pasta shapes.

By the late 1930’s the ready-to-eat (RTE) breakfast cereal industry was using
extrusion to form bite-size cereal shapes from hot, precooked cereal dough. General
Mills, Inc. was the first to introduce an extruded RTE cereals using this process (Harper,
1981). Other extruders having both cooking and forming capabilities were introduced in
the late 1940’s. These included the cooker extruder, developed to pre-cook corn and
soybean for use in animal feeds, and the collet extruder. Collet extruders were introduced
in 1946 for producing highly expanded snack products from grain based ingredients
(Hess, 1973). In the sixties, single-screw extruders became popular for pre-cooking
starches, texturizing soya proteins and for direct expansion of breakfast cereals.

One of the important characteristics of cooker extruder is the possibility of high

temperature short time (HTST) cooking (Harper, 1978). Smith and Ben-Gera ( 1980)

10

described HT ST extrusion cooking as the most versatile and most economical thermal
processing system. This can be justified because of many conversions that could be
carried out at lower moisture contents which lowers the drying costs for gelatinized or
texturized products at shorter residence times. In HTST cooking, controlled condition of
cooking are provided by keeping lower processing temperature during processing the
dough and applying elevated product temperature during the few seconds of the dwell
time.

Examples of HTST product applications include texturized vegetable proteins, RTE
cereals, beverage powders, biscuits, weaning and baby foods, dry pet foods. Other
applications include inhibition of microorganisms without overcooking which otherwise
promote spoilage, inactivation of antinutritional factors such as trypsin inhibitor in
soybeans, and denaturation of enzymes which are responsible for rancidity.

Johnston (1979) has discussed the technical fundamentals of HTST extrusion cooking
equipment. The methodologies of HTST extrusion cooking were discussed by Smith and

Ben-Gera (1980).

2.1.2 Advantages of Extrusion Cooking

Extrusion cooking has become one of the most important food processing methods
because of its many advantages (Harper, 1978) which include:
Energy efficiency - continuous extrusion cooking completes a number of unit operations
such as mixing, heating, shearing, texturizing in a single machine and the cost of drying

the extrudate is also lower since extrusion can be carried out at low moisture content.

ll

Flexibility - with appropriate changes in process and system parameters of the extrusion
process, various kinds of food products having a wide range of forms, shapes, densities,
and textures can be made with the same extruder.

Cost effectiveness - includes savings in energy, labor, investment cost and floor space.
Improved product functional characteristics - extruders are used to improve functionality
such as gelatinization of starches and the modification of protein textures and structures
to control optimal growth inhibitors and anti-nutritional factors to produce cleaner,
microbiologically safer and more stable food products.

Improved sanitation - extrusion cookers can be easily disassembled for cleaning, so that

poor quality flavors are not to be developed in processed foods.

2.1.3 Single-Screw Extruders

In order to understand the twin-screw extrusion process, one must begin with
single-screw extruders. The single screw extruder has been widely used in the production
of various food products. Basically a single-screw extruder may be regarded as a friction
pump, as it relies entirely on friction between the material being processed and the barrel
wall to convey material (Clark, 1978).

The screw is the key element of the single screw extruder, as its geometry
influences the unit operation of the extruder (Harper, 1978). It consists of a helical flight
wound around a metal shaft enclosed within a cylinder barrel. The geometry of the screw
barrel assembly is shown in Figure 2.1. Db is the internal diameter of the barrel. The

flight height (H) is the distance between the screw root and internal surface of the barrel,

12

 

 

 

 

 

 

 

Figure 2.1 Single Flighted Extruder Screw (White, 1991)

the radial clearance (5,) is the distance between the crest of the screw flight and the inner
surface of the barrel, the flight width perpendicular to the flight is e, the axial distance
between the flights is B. The perpendicular distance between the flights along the helical
path of the screw is W. The angle of helix is o , and it is the angle the helical flight
makes with the vertical. The axial distance of one full turn of the screw (screw lead or
pitch) is S. The helix angle 4) will vary with radius because the pitch S is constant and

4) is defined by relating S to the circumference through:

S=2Hr tan¢(r) (l)

13

The helix angle decreases as one proceeds from the screw root to the barrel. The
pitch S can vary along the length of the screw and is related to e and B (White, 1991) as

follows:

|s| =P(B+ 9) (2)

C085

Where P is the number of parallel flights in a screw. Absolute value is used because
backward pumping screws have negative helix angles and pitch S.

The typical single-screw extruder is shown in Figure 2.2. The single-screw
extruder has three processing zones: the feed zone, the kneading zone, and the final
metering zone (Matson, 1982; Hauck, 1985a). The feed zone has deep flights or channels
to receive and mix the incoming feed ingredients; the kneading zone, having a decreased
depth of flights, applies compression, mild shear and thermal energy to the feed; the final
cooking zone has very shallow flights which generate high shear causing the temperature
of the material to increase rapidly, reaching a maximum before the product is forced
through the die (Harper, 1978).

Heat input to the product in extrusion comes from different sources which include
steam injection into the extruder barrel, frictional heat developed at the barrel wall and
in the die area during rotation of the screw, or heat transfer from a steam jacket encasing
the barrel or from a steam quill in a hollow-cored screw. The desired product properties
can be produced by controlling several parameters including screw geometry, length of

barrel; clearance between the screw and the barrel; die geometry, screw speed, flow rate,

14

GENE . GEAR r550
R ouczn a HOPPER
THRUST BEARING COOLING BARREL

    
   
  
   
 
 
    

WATER STEAM
JACKET PRESSURE

TRANSDUCER
THERHOCOUPLES
DIE

DISCHARGE
THERMOCDUPLE

BREAKER
PLATE

BARREL WITH
HARDENED LINER

  
 
 

   
 

FEED
SECTION
SCREW WITH

INCREASING
ROOT DIAMETER

COMPRESSION METE RING
SECTION SECTION

    

Figure 2.2 Typical Components of a Single-Screw Extruder (Harper, 1978)

15

moisture content of the feed; and apparent viscosity of the material (Clark, 1978).

2.1.4 Twin-Screw Extruders

Twin-screw extruders include a variety of machines with different processing and
mechanical characteristics. Interest in twin-screw extruders is growing mainly because
of its greater flexibility in controlling product and process parameters which include:
tighter control on product variability; changes in formulation, screw speed, die
configuration, narrow residence time distribution, efficient pumping and self wiping
features, ability to release steam and/or vent other volatiles, higher process stability, more
uniform size and shape of the finished product, improved product color and density; easy
access to the screws without disassembling the extruder; ability to process a broad range
of formulations; greater thermal and mechanical mixing efficiency; and uniform shear rate

profile.

2.1.4.1 Types of Twin-Screw Extruders

Twin-screw extruders can be categorized according to the position of the screws
in relation to one another, and to the direction of screw rotation. Extruder screws can be
either co-rotating when both screws turn in the same direction; or counter-rotating when
the screws turn in opposite directions. Regarding position of the screw, extruders can
have intermeshing screws (partially or fully intermeshing) in which the flights of one
screw engage the channels of the other screw, or non-intermeshing screws in which the

screws do not engage each other’s threads.

l6

Twin-screw extruders may also be sub divided based on the flow path of the
material. In lengthwise-open extruders, the material moves axially along the barrel
toward the die. In crosswise-open extruders, the material moves across the barrel from
the channel of one screw to two different channels of the other (Martelli, 1983). Figure
2.3 shows such a classification.

Non-intenneshing twin-screw extruders are described as two single-screw extruders
sitting side by side with only a small portion of the barrels in common (Clark, 1978).
Neither pumping nor mixing is positive. These extruders rely on friction for extrusion.
On the other hand, intermeshing twin-screw extruders are characterized as positive
pumping, efficient mixing, and self cleaning. They generally act as positive displacement
pumps, forcing material enclosed between the screws to move toward the die by rotation
of the screws. The pumping action depends on the screw geometry and occurs

independent of the operating conditions.

2.1.4.2 Counter-rotating Twin-Screw Extruders

These extruders are not widely used in the food industry. They are characterized
by poor mixing, low capacity and greater positive displacement as compared to co-
rotating extruder. Counter-rotating twin—screw extruders are similar to single-screw
extruders in that they have high shear stresses at the barrel walls and low stresses in the
middle of the channel.

Fully intermeshing counter-rotating twin-screws carry small volumes of material

down the barrel with in C-shaped chambers (Martelli, 1983). The material is forced to

17

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

SCREW -
ENGAGEMENT SYSTEM COUNTERRC IATING COROTATING
LENGTHWISE AND m?” ~._:—- THEORETICALLY
‘2’ CROSSWISE CLOSED I _._';’__;___.’__7_'_ L.“ 2 NOT POSSIBLE
I
S :5 “‘0 NOT POSSIBLE S m
TL a CROSSWISE CLOSED 3 .. 4 W
W
‘2’ 5 LENGTHWISE AND THEORET'CALLY F’OSS'BLE 4?;—
E ' CROSSWSE OPEN BUT PRACTICALLY 3: ‘fi .__
{3 5 NOT REALIZED : 6 ~
5 ._
E LENGTHXSE OPEN ”:17... i ~~ THEORETICALLY
- AW.
5 , g CROSSWISE CLOSED 7 ‘t—J—J—u— 3 NOT POSSIBLE
- .1 2
3‘3 m - 5%.-:3-
~= m .
5 g LENGTHWISE AND 9‘ 10A
c. .2. CROSSWISE OPEN
99
O O
E .2.
I I
I- g I- g} LENGTHWISE AND -—-‘~ ,_.‘=‘- ‘W‘
2 g g g CROSSWISE OPEN "’ W33
LU DJ
E E I I I2

 

Figure 2.3 Screw Configurations of Twin-Screw Extruder (Ziminski and Eise, 1980)

 

18

flow through small mechanical clearances around the flights at the point where the two
screws intennesh. The calender effect, created when the crest of one screw rolls off the

root of the other, takes place with poor mixing of material from one screw with that of
the other. Pressure attributed to the motion of the screws and the calender effect pushes
the screws apart causing wear on both the screws and the barrel wall. Therefore, unlike
co-rotating screws, counter-rotating screws must be operated at low speeds (Harper, 1992)
because the wear increases with the relative speed of the shafts. These extruders are
particularly suited for processing relatively non-viscous materials requiring low speeds
and long residence times. Examples of such products are gum, jelly, and licorice

confectionery (Elsner and Wiedmann, 1985).

2.1.4.3 Co-rotating Twin-Screw Extruders

These are the more commonly used extruders in the food industry. Advantages
of this system are its pumping efficiency, good control over residence time distribution,
self cleaning mechanisms, and uniformity of processing (Schuler, 1986). The co-rotating
screws are better suited for applications which require a high degree of heat transfer but
not forced conveyance (Elsner and Wiedmann, 1985).

Interrneshing co—rotating screws transport four to five times more volume of
material in open V-shaped chambers as compared to intermeshing counter-rotating screws.
Because of the engagement of the screws they also result in a better mixed product. The
exchange of the material also contributes, in part, to a uniform shear stress distribution

which in turn enhances the mixing and energy efficiency of the process (Hartley, 1984).

l9

Co-rotating screws have a transitional motion in which one crest edge wipes a screw flank
tangentially with a constant relative velocity. Because this occurs at a relatively high
speed and without producing a calender effect, the screws achieve a more efficient and
uniform self-cleaning (Hartley, 1984). Besides, the extruder can be Operated at higher
screw speeds since no pressure develops to push the screws apart.

The co-rotating twin screws can be fitted with different types of kneading discs
to improve the mixing function of the extruder (Hartley, 1984). Figure 2.4 shows food
materials being passed from one disk to another to accomplish the kneading action and
mixing. The discs can have a forward conveying effect, a neutral effect, or a reverse
conveying effect. Forward-conveying discs increase the pressure profile within the
channel by pushing the material toward the die. Reverse conveying discs reduce the
pressure by delaying the passage of material through the extruder, allowing it to undergo
additional processing with improved efficiency of heat transfer through the barrel wall.
The pressure can be reduced to atmospheric levels to allow the release or venting of
steam or other volatile or to introduce another feed in the downstream portion of the

machine (Hartley, 1984).

2.1.4.3.1 Screw Geometry

Interrneshing co-rotating twin screw extruders generally have self wiping profiles
for both their screw and kneading disc elements (Figure 2.5). Booy (1978) showed that
the channel depth in self wiping screw profile which is continuous and maintains a

monotonic second increasing derivative must vary according to:

20

 

H(9) =Rs(l+COSG)-JC2-R§sin29 (3)

where R, is the outer radius of the screw, 0 is the angle of rotation of the screw and C
is the centerline distance between the screws. For screw elements the angle 0 may be

expressed in forms of the axial distance along the length of the screw Z:

9 = 21%???) (4)

where S is the pitch and P is the number of thread starts.

2.1.4.3.2 Transport Phenomena

Twin screw extruders are geometrically complex machines. Studies about
extrusion technology in the synthetic polymer field has contributed to the understanding
of transport phenomena in food extrusion. Therefore, flow models are generally deduced
from polymer extrusion.

For co-rotating twin screw extruders, the channel from one screw to the other is
widely open (Martelli, 1983; Ollet et al., 1989): the closing of the channel in the
intermeshing zones is about 55% for deep screw channel and less (15%) for shallow ones.
In polymer extrusion, the model generally used considers a channel with varying

restrictions during passage of product from one screw to the other (Wyman, 1975; Booy,

21

 

 

M?
v
A;

Figure 2.4 Kneading Elements of a Twin-Screw Extruder (Harper, 1991)

22
1980; Masheri and Wyman, 1980; Denson and Hwang, 1980; Martelli, 1983; Eise et al.,

1983; Szydlowski and White, 1987; Wang et al., 1989). For co-rotating twin screw food
extruders, however, only a few models covering the whole extruder have been proposed
(Yacu, 1984; Tayeb et al., 1988, 1989). The main limitation pointed out in modeling
twin-screw food extruders is lack of adequate knowledge about the flow and thermal
characteristics of food products.

The following flow model was developed for co-rotating twin-screw extruders

(Todd, 1989), and the extruder output, Q, is defined as:

1::
D
'e

Q=orN- (5)

TIT

where N is screw speed, It is viscosity, AP is the pressure rise, L is the fill length, or and
B are constants based on screw geometry which needs to be determined experimentally.

Die geometry is an important factor of the volumetric expansion of extruded
products. Different types of die designs are available varying in sophistication from
single circular die holes to rotating and co-extrusion dies. For a circular die, the pressure-
throughput relationship may be computed by using the solution of the Stokes equation
(Rauwendaal, 1986), even for pseudoplastic product like corn starch, as studied by

Vergnes and Villemaire (1987):

23

___ n 1 AP In (3n+1I/n (6)
‘90 Tm [771—] R0

where QD is the flow rate in one die channel, AP is product pressure RD is the die channel
radius, Ln is the length of the die channel, K is the consistency coefficient and n is flow

behavior index for power fluids.

2.1.4.3.3 Residence Time Distribution

It is the measure of time the process material spends in the processing equipment.
The residence time reveals information about flow patterns, degree of mixing, design of
equipment, processing condition, and retention time of the material in the processing
device.

Although the study of residence time for extrusion processes has received
considerable attention (Pinto and Tadmor, 1970; Todd and Irving , 1969 ; Bigg and
Middleman, 1974; Todd, 1975; Janssen et al., 1979; Olkku et al., 1980; Davidson et al.,
1983; Colonna et al., Wolf et al., 1986; and Altomare and Ghossi, 1986) there have been
no generally satisfactory studies of residence time distribution (RTD) and mixing in
intermeshing co-rotating twin-screw extruders (White, 1991).

Levenspiel (1972) defined the exit age distribution E(t) function so that E(t)dt is
the fraction at the exit, of flow that has spent a time between t and (t + dt) in a system.

The cumulative exit age distribution F(t) function can be obtained by integration to give

24

 

Figure 2.5 Self Wiping Profiles of a Twin-Screw Extruder (Wiedmann, 1991)

25

a cumulative residence time distribution function.

C
F(t) =f5<t>dt (7)
O

The average residence time T of the material in the extruder is calculated as:

8V

= E (8)
T {a (t)dt

Residence time measurements of most published works were made with a radio
tracer with a known behavior. Jager etal., (1988, 1989) and van Zullichem et al., (1988)
presented a numerical RTD model to translate the RTD into a mass flow pattern for co-
and counter rotating twin-screw extruders.

In the converging section, average residence time is estimated according to the
movement of the main layer of material with a plug flow assumption in each section of
the screw of a given pitch, the residence time (t,) can be computed from section length

(L), screw speed (N), and screw pitch (S) as:

t.=__1' (9)

26

Where the subscript i refers to different screw elements. The total residence time in the
conveying section can be obtained by tc = Zti. In sections full of molten material, average
residence time can be estimated by using a plug flow model from volume (Va) and flow

rate (Q) as:

V (12)

2.1.4.3.4 Mixing

Ingen-Housz (1983) describes three types of mixing: axial, dispersive, and
distributive. Axial mixing, also called macro mixing is for the whole length of the
extruder in an axial direction. Dispersive mixing is the size reduction of the
agglomerations present by velocity gradients in the mass flow. In a twin screw extruder
dispersive mixing can be found in all sections in which shear is dissipated. Distributive
mixing also called laminar mixing is related with the distribution of small volumes of

material within the extruder. This is mainly dependent on the leakage flows.

2.1.5 Comparison Between Single- and Co-rotating Twin-Screw Extruders

The characteristics used to compare single- and twin-screw extruders are given by
Harper (1991). Single-screw extruders, although they lack the mixing efficiency, process
control and product uniformity acclaimed for twin-screw extruders, are more economical
to operate and maintain with lower initial capital costs.

Some of the advantages the twin-screw extruder offers include:

27

Process versatilig and control. The output of twin-screw extruder is independent of
screw Speed (Purvis, 1987) since it is generally operated in starve-fed mode (the screw
speed exceeds the feed rate). This enables to produce different product textures from the
same formulation with the same screw by changing the screw speed to vary the
mechanical shear rate. Twin-screw extruder has a tighter control over product variability
because of its ability to approach the desired shear. The efficient pumping action and self
wiping features of the twin screws ensure a unifome processed product and reduced
down time. According to Straka (1985), twin-screw extruders are designed to transport
material more efficiently in a uniform, continuous stream through the barrel. This
uniformity leads to a more uniform shape and size of the finished product.

Flexible machine de_sigp. The design of twin-screw extruders which permits easy access
to the screws without disassembling the extruder allows screws and barrels to be cleaned
in place or the screw configuration to be changed. The twin-screw extruder also has the
capability to continue operating when ingredient feed is interrupted, because the screws
are self cleaning, however, single-screw extruders shut down completely with short feed
interruptions, requiring machine disassembly. The twin-screw extruder can be restarted
quickly after shutdown (Rhodes and Olbertz, 1985) without the need to clean dies and
screws (Straka, 1985).

Abilig to process specialg formulations. The twin-screw extruder can process a broad
range of formulations because of its intermeshing screws including some that are difficult
for single-screw extruders, such as low-density powders that have low purging ability and

sticky material that have higher sugar contents (Straka, 1985). Products having up to

28

25% fat content can be processed using screw configurations that are not available in the
single-screw extruder. The thermal and mechanical mixing efficiency of the twin-screw
extruder results in a more consistent product which leaches less fat during handling and
storage (Hauck, 1988).
gpjgl Invest_ment. Considering cost, single screw extruders are superior since they are
more economical to operate and maintain. Twin-screw extruders cost 60-100% more than
single-screw extruders of equivalent production capacities, and their electrical operating
costs are about 1 to 2 times higher than for comparable single-screw extruders (Harper,
1991). These factors may attribute to the widespread use of single screw extruders in the
food industry. However, to get a product of consistent quality or to make more unique
products it may be important to consider the advantages of twin-screw extruder.
Because of its flexibility, ability to handle a broader range of ingredient moisture
and feed materials , the twin-screw extruder is an attractive alternative for food processes

requiring cooking and forming (Harper, 1991).

2.1.6 Extrusion Cooking of Cereals and Starch

The functional properties of extruded foods have also been extensively studied
leading to several reviews (Rosen and Miller, 1973; Harper, 1979, 1981, 1986; Linko et
al., 1981; Bjorck and Asp, 1983; Mercier et al., 1989; Colonna et al., 1987, 1989; Guy
and Home, 1988; Kokini et al., 1991). Influence of extrusion cooking on product
qualities have been generally studied and modelled by different researchers including

(Gomez and Augilera, 1984; Owusu-Ansah et al., 1984; Bhattacharya and Hanna, 1987a),

29

and some laboratory studies have been carried out on the influence of extrusion
processing variables on the texturing of soya protein (Cumming et al., 1972; Maurice et
al., 1976; Frazier et al., 1983).

The basic structures of extruded products are formed by transforming and
manipulating natural biopolymers, such as those of starch or of certain types of proteins.
In the former case the most commonly used materials are cereals like wheat, corn and
rice, and potato derivatives, such as flours and granules. Other cereals and starch-rich
materials in less common usage include rye, barley, oats, sorghum, cassava, tapioca, buck-
wheat, pea flours, and other related materials (Guy, 1994). Extrusion cooking of cereals
involves physical and chemical changes such as: mechanical mixing, shearing, and
disintegration of discrete entities in the micrometer size range, and molecular changes.
These in turn include hydration and swelling of the starch granules, and gelatinization of
the starch with loss of crystallinity. These changes have been identified by X-ray
diffraction (Mercier et al., 1980), by spectroscopy of starch-iodine complexes (Gomez and
Aguilera, 1983), and by enzyme susceptibility (Bhattacharya and Hanna, 1987b). Harper
(1981) found out that unbranched amylose diffuses out of the swollen and gelatinized
granules and forms complexes with lipid components as shown by the appearance of a
characteristic V-type X-ray diffraction pattern (Mercier et al., 1980). The extent of these
changes depends on the conditions of moisture content, temperature, and mechanical
energy input during extrusion. The molecular changes influence the qualities of the
extrudate such as compressive strength, bulk density, and brittleness. Other major

changes occurring during the process of starch extrusion are the disruption of the

30

crystalline regions in the granule and possible formation of amylose-lipid complexes
(Colonna et al., 1989).

Many analytical methods are being used to reasonably estimate starch damage
which can be used to control both the process and product. Starch damage is any
structural change giving reduced resistance to the action of amylases (Chiang and
Johnson, 1977) and/or thermal dispersion. Criteria based on the functional properties of
extrudates appearing after extrusion such as swelling, or increase in solubility are used

since extruded products loose their granule integrity and crystallinity.

2.1.6.1 Expansion

Expansion is usually expressed as a ratio between the diameters of the extruded
product and the die, which is called expansion ratio (Van Zuilichem et al., 1975; Faubion
and Hoseney, 1982a; Vainionpaa, 1991; Bhattacharya and Prakash, 1994). Extrudate
expansion has been studied extensively. The two most important factors affecting the
expansion ratio are moisture content and extrusion temperature (Mercier and Feillet, 1975;
El-Dash et al.,1984; Park,1976). Screw geometry, screw speed, and shear within the
extruder are also shown to affect the expansion of starch (Bhattacharaya and Hanna,
1987b). Factors affecting the degree of expansion with single-screw extruders have also
been investigated by Van Zuilichem et al. (1975), Mega and Cohen (1978), Faubion and
Hoseney (1982a, 1982b), Alvarez-Martinez et a1. (1988), and Chinnaswamy and Hanna
(1988). Mercier and Feillet (1975), Launay and Lisch (1983), Antila et al. (1983),

Owusu-Ansah et al. (1984), and Guy and Home (1988) have studied expansion with twin-

31

screw extruders.

Starch content has also been related to the degree of expansion. According to
Horn (1977) maximum and minimum expansions were obtained for pure starches (500%
increase in product diameter), and oil seeds (ISO-200%), respectively. Tire amylose-
amylopectin ratio is also important in determining properties of starch-based extruded

products.

2.1.6.2 Solubility

Water absorption and water solubility are the main functional properties of starch
extrudates. Water absorption index (W AI) and water solubility index (W SI) may be used
in estimating the suitability of using extruded starchy products in suspensions or solutions.

Water absorption index is the weight of gel obtained per gram of dry sample. It
is determined by the method of Anderson et al. (1969) as briefly described in the
materials and methods of this paper. Because only damaged starch granules absorb water
at room temperature and swell, creating increased viscosity, WAI is found to correlate
well with cold-paste viscosity. After reaching a maximum, related to the degree of starch
damage, WAI decreases with the onset of dextrinization.

Water solubility index is the percentage of dry matter recovered after the
supernatant is evaporated from the water absorption determination (Anderson etal., 1969).
WSI is related to the quantity of soluble molecules, which is related to dextrinization.
The water solubility of starch increase with expansion, and the stickiness of the extruded

starches is related to increased solubility (Colonna et al., 1989). WSI increases with the

32

severity of the thermal treatment in the extruder. WSI has been shown to increase as
moisture content decreases for corn grits (Anderson et al., 1969; Conway, 1971), corn
starch (Mercier and Feillet, 1975; Gomez and Aguilera, 1984), wheat starch (Paton and
Spratt, 1984), and wheat, rye, barley and oat flour (Vainionpaa, 1991).

Variations in WA] and WSI may be interpreted based on starch-water interactions
that govern the solid-phase structure of the processed starch. Solubility may be related
to the lower molecular weight of starch components while a low WAI reflects the
restricted water accessibility of extruded starches ascribed to a compact structure (Colonna

et al., 1989).

2.2 Response Surface Methodology

The extrusion cooking process of biological materials is very complex because of
the many interdependent variables involved. However, valuable information on effects
to be expected from changes in processing variables may be obtained from relatively
simple models based on response surface methodology (RSM), first introduced by Box
& Wilson (1951). Ample literature is available on the principles and applications of the
methodology (for example, Box and Hunter, 1957; Cochran and Cox, 1957; Davies, 1963;
Myers, 1971, Murphy, 1977, Box and Draper, 1987). RSM is a mathematical and
statistical method based on regression analysis on quantitative data from appropriate
experimental designs to construct and solve multivariate equations describing the
relationship of the dependent variables to product quality characteristics and to process

and design parameters (Olkku et al., 1983). RSM was designed to reduce the number of

33

experiments while still obtaining the maximum information from them. The results are
presented by response surface mapping to describe graphically the relation of one property
versus two process parameters. According to Rose (1981), RSM has been especially
useful in the study of processes or phenomena involving many variables and when

reaction kinetics and/or underlying mechanisms are incompletely known.

2.2.1 Basic Concepts and Assumptions of RSM
RSM is based on the assumption that when k factors (independent variables) are
being studied in an experiment, the response (dependent variable) will be a function of

the levels at which these factors are combined (xi). Thus,

n=f(x1,x2,....,xk) (11)

where n is the response, and the form of the function f is unknown, and perhaps
extremely complicated. However, RSM approximates f by a low order polynomial in
some region of the independent variables. x’s are the coded variables. The reason for
coding the levels of the independent variables is to have homogeneous scales on the axes
to generate a spherical symmetry which makes the subsequent study of the response
surface much easier.

For the case the approximating function is linear in the variables, the response is

written in terms of the design variables, as first order model:

34

n=Bo+B1X1+Bzxz+°°°+BH<k (12)

In the above expression, the 13’s are coefficients or empirical parameters which
have to be estimated from the data. The first order model is often used in studying f in
narrow regions of the independent variables, for example when making a rough survey
of a target area in optimization.

The second order approximating functions have been most frequently applied in

the food field, and can be generally expressed as:

k k k
Tl = [30 + EBiXi + EZfiijxixj+E (13)

1'1 1'1 jkl

Where 7] is the response variable, x6 and xj are independent variable, k is the number of
independent variables, and E is the random error. The rationale for polynomial
approximation off is based on the Taylor series expansion of f around the point x1 = x2
=x3=...=xk=0.

The fundamental assumptions on which RSM is based are summarized as follows:
1. A structure or model 1] = f (x,, x2,..., x.) exists and is either very complicated or

unknown. The variables involved are quantitative and continuous.

2. The function f can be approximated in the region of interest by a low-order

polynomial such as Equation 11 or 12.

 

ch

EX

w;

re:

are

35

3. The independent variables x1, x2, , xk are controlled in the observational process and
measured with negligible error.

Selection of the appropriate independent variables and the choice of their actual

levels is of utmost important. The levels of the coded variables, used in computing the

response surface models are defined by:

x.= i c (14)

where xi is the coded value of the variable X, Xi is the real or natural value of the
variable, Xc is the real value of the experimental center point of Xi and AXi is the unit
change of X,.

Regarding the order in which the runs are performed, it is advisable to run
experiments in random order under steady conditions if a random sequence of the runs
does not raise any operating trouble. However, it is probably best to follow an order
which minimizes the troubles which might be sources of error. When all responses for
a dependent variable 11 have been obtained the next step is to find the constant B for the

response surface equation. This is usually done by regression analysis.

2.2.2 The Applications of RSM in Food Processing
Response Surface Methodology techniques have been successfully applied in many

areas such as agronomy, biology, chemistry, engineering, food industry, textiles, and

36

others. In 1951, Box and Wilson described a scientific approach to determining optimum
conditions which combined special experimental designs with Taylor first and second
order equations in a sequential testing procedure called "Path of Steepest Ascent."
Underwood (1962) applied RSM in designing extrusion screws. The dependent variables
in his experiment were rate of extrusion, melt temperature, net power required,
smoothness of operation, and thoroughness of mixing whereas the length of the metering
section, channel depth in the metering section, the channel depth in the feed section and
the screw speed were considered as independent variables.

In the complicated field of food extrusion RSM was used successfully in the
extrusion of cooking of corn starch (Lawton et al., 1972), triticale (Lorenz et al., 1974),
cottonseed meal (T aranto et al., 1975), soybean (Aguilera and Kosikowski, 1976; Maurice
and Stanley, 1978), starch-protein-sugar pastes (Olkku and Vainionpaa, 1980), barley
starch (Linko et al., 1980b), fish and soy mixtures (Murray et al., 1980). Olkku et al.
(1983) employed RSM in the steady-state modeling of extrusion cooking. RSM has been
further applied to analyze the extrusion cooking of wheat based materials (Olkku and
Vainionpaa, 1980; Meuser et al., 1982; Paton and Spratt, 1984; Vainionpaa et al., 1984;
Linko et al., 1985; Vainiopaa and Malkki, 1987; Vainiopaa, 1989; and Vainiopaa et al.,
1989), rice (V ainiopaa, 1992), and rice and chick pea blends (Bhattacharya and Prakash,
1994).

Halek and Chang (1991) employed RSM to study extrusion processing of cornmeal
in a co-rotating twin-screw extruder. In their experiment three-variable, three-level RSM:

feed moisture content (20, 25 and 30%), barrel temperature (100, 150 and 200°C), and

37

screw speed (100, 200 and 300 rpm) was investigated. From its many applications it can
be concluded that RSM has been proved to be a powerful tool in providing solutions of
a large variety of problems such as optimization of complex products and process

variables in food processing.

2.3 Factorial Design

This design is characterized by the fact that the effect of changing one variable
can be assessed independently of the others. It is accomplished by using each of the
possible combinations of the levels of each factor. The advantages of this design over
the one-factor-at-a—time experimental technique are that it lends itself to assessing the
interaction among factors, and it is more precise in terms of the effect of a factor used.
A factorial experiment with k factors each at three levels is referred to as 3" factorial.
The class of 3" factorial experimental designs can be useful when the regression model

is best represented by a second order polynomial (Dey, 1985).

2.3.1 Fractional Factorial Design
It is a design consisting of a fraction of a complete factorial experiment. The
concept of this design was introduced by Finney (1945). Consider a Taylor’s series

expansion:

ill 1'1 1'31

“Vi 3“ x.+ lii‘ 32" x.x.+ (15)
'l 0 33:17. ax—a'x—jj

38

Where 1] is a function of k coded independent variables, x,, x2, xk, coded so that the
center of the region of interest is at the origin. If we truncate terms of order higher than

the second degree, the expression yields the second degree approximation, thus

k k k
n = a. + 25.x. + 225..x.x. M

i=1 jzi

For a complete understanding of this design the reader is referred to Davies (1956)
or other books on experimental design (for example, Dey, 1985). Fractional factorial are
useful in the following areas:

1. Situations where some of the high order interactions can be tentatively assumed to be
zero;

2. In screening from a large set of factors, some factors with large effects;

3. In a sequential program of experiments;

4. When the amount of experimentation required by the complete factorial is more than
the experimenter can afford.

As mentioned above, while using a fractional factorial design, an assumption
regarding the absence of certain higher order interactions has to be made in order to
obtain unbiased estimates of lower order effects. This, however, should not be taken as
a drawback of the design since fractional factorial design has been used effectively in
many areas such as: agriculture, chemistry, genetic improvement, food processing, and

others. In situations where the absence of higher order interactions (for example, 3-factor

39

and higher) can be validly assumed, fractional factorials provide useful information on

lower order effects at a considerable saving (Dey, 1985).

CHAPTER III

MATERIALS AND METHODS

The general plan of this experiment was to use a fractional- factorial experimental
design with three independent variables (feed moisture, barrel temperature and screw
speed). The resulting dependent variables were then recorded and used in calculating the
specific mechanical energy of the system. The details of the overall experimental set up,
data collection and analysis processes are presented as detail as possible.

Raw material: Teff flour obtained from Workinesh Spice Blends, Inc. of Oshtemo,
Michigan was used as a feed material for all extrusion conditions. The compositions of
the teff flour are shown in Table 3.1. The water used was tap water. After the extrusion
cooking process, the collected teff extrudate products were dried for 20 hours at 60t2°C
in a forced-draft oven and sealed in plastic bags after cooling so as to obtain a relatively
uniform moisture in the samples. The final moisture content of the cylinderical rods

(extrudates) was 3-4%.

41

Table 3.1 Compositions of Teff Flour

 

 

Component Average compositions‘ Dry Basis Compositions
Moisture content 12 %

Dry Matter 88 %

Crude Protein 13.59 % 15.44 %
Crude Fat 0.62 % 0.70 %
Acid Detergent Fiber 9.30 % 10.60 %
Ash 3.41 % 3.87 %
Sulfur 0.20 % 0.23 %
Phosphorus 0.48 % 0.54 %
Potassium 0.35 % 0.40 %
Magnesium 0.18 % 0.20 %
Calcium 0.22 % 0.25 %
Sodium 0.01 % 0.01 %
Iron 298 ppm 339 ppm
Aluminum 275 ppm 313 ppm
Manganese 67 ppm 76 ppm
Zinc 36 ppm 41 ppm
Copper 8 ppm 9 ppm
Boron 1 ppm 1 ppm

 

' Teff flour analysis as reported by A&L Great Lakes Laboratories, Inc., Fort Wayne,
Indiana.

42

3.1 Extruder

A Werner & Pfleiderer, model ZSK-30, co-rotating twin-screw extruder (Werner
& Pfleiderer Corp., Ramsey, NJ) was used (Figure 3.1) to extrude the teff flour. The two
screws are intermeshing with a self-wiping profile. The extruder barrel has five
individually controlled temperature zones. The barrel cooling system consists of a supply
line with solenoid valves and a retum line with check valves to regulate flow and
temperature in each zone. The temperature is monitored by steel thermocouples in the
barrel sections and controlled by the temperature controllers in the electrical panel. Water
was used as the cooling medium. The barrel length-to-diameter ratio (UD) of the
extruder was 32.5. A food die head and holder with two circular die holes of each 2 mm
in diameter (Figure 3.2) was hinged to the extruder in such a way that the die holes were
aligned with the screw tips. The die head was cleaned, closed and tightened to the end
of the extruder barrel before each run. The screw configuration used is shown in Table
3.2. Actual photograph of the screw profile used for this experiment is also shown in

Figure 3.3.

43

 

 

 

Figure 3.1 ZSK-30 Twin-Screw Extruder

Table 3.2. Extruder Screw Configuration used in Teff Extrusion

 

Screw element type Number of
X/Z' , I(B/A°/Y/Zb screw elements

 

S/ 10°

20/ 10

42/42

28/28

28/ 14

20/20

20/ 10

20/20
KB/45°/5l14
20/20
KB/45°/5I 14
20/ 20
KB/45°/5I 14
KB/45°/5/ 14 LH
20/10

20/ 20

20/ 10

20/20

O

AI—nHI—tI—nNHt—tHNmI—tNI—oONh—rI—nI—I

 

'X = distance in mm to make one complete revolution, 2 = length of screw element
(mm).

bKB = kneading block, A° = angle of flight, Y = number of disks, and Z = length of
screw element (mm).

‘8 indicates the starting element

45

 

 

 

Figure 3.2 Food Die used in Teff Extrusion

46

 

 

 

 

Figure 3.3 Screw Profile used in Teff Extrusion

47

3.2 Extrusion Conditions

The teff flour was fed into the extruder by weight-loss differential weigh feeder
with acri-lok (Acrison, Inc., Moonachie, New Jersey). Tap water at ambient temperature
was added to the flour inside the extruder via an inlet port in the top of the barrel, 8.2 cm
downstream from the center of the flour feed port, using a variable speed drive pump with
speed controller (Cole-Parmer Instrument Co, Chicago, Illinois). The pump was
calibrated to give a total moisture content of feed material as 20, 25 and 30%. The dry
feed rate was kept constant at 9.07 kg/h (20 lb/h). During the preliminary runs, a dry
feed rate of 4.54 kg/h (101blh) was used. However, this low dry feed rate combined with
higher barrel temperature, resulted in instabilities such as clogging of the teff extrusion
process. The ranges of independent variables were chosen so as to represent practical and
stable operation of the extruder.

All barrel and product temperatures, percent torque, screw speed, die pressure, and
feed rate shown in the control panel were recorded at five regular time intervals. Steady-
state extrusion conditions were assumed to have been reached when there were no visible
drifts in product temperatures and percent torque for at least 5 min. Samples were then
collected in zipper plastic bags, sealed and stored at room temperature (= 25°C) for
further analyses. Specific Mechanical Energy (SME) input was calculated using the

following formula (Hsieh et al., 1990b):

rpm(run) X%Torque(run) XkW(rated) (17)

SME =
rpm(rated) 100 Feed rate

 

48

3.3 Functional Testing of Extrudates
3.3.1 Product Moisture Content

Wet basis moisture contents of the extrudates were determined as the weight loss
of triplicate samples of extrudates after drying in an oven for 1 h at 130°C (AOAC,

1990).

3.3.2 Expansion Ratio
The expansion ratio was measured as the ratio of the diameters of teff extrudates
to that of the die. The diameters of the samples were measured with a digital caliper

accurate to 0.01 mm. Fifteen replicates were made to obtain the average.

3.3.3 Bulk Density

The bulk density was determined by weighing pieces of cylindrical extrudates and
measuring their length and average diameter. This method of measuring bulk density was
also used by other workers in the field of extrusion (Gujska and Khan, 1991). The
number of replications was five.

These data were used to calculate the density as follows:

piece weight ,where (18)

Density = -
apparent piece volume

 

49

' 2
Apparent volume = 1t(d1arZeter) X length (19)

 

3.3.4 Water Absorption Index (WAI) and Water Solubility Index (WSI)

WAI and WSI were determined as described by Anderson et al. (1969). A 2.5 g
sample of ground product (60 mesh) was suspended in 30 ml of water (pH 6-7) at room
temperature, stirred intermittently over a 30 minute period, and centrifuged at 1000 X g
for 15 minutes (Sorvall RC-5B refrigerated superspeed centrifuge, Dupot Company,
Newtown, CT). The gel pellet was weighed and WA] was calculated as gram gel/gram
dry sample. The supernatant was evaporated on aluminum dish in air oven and WSI

calculated as percentage dry solid/2.5 g sample.

3.3.5 Color

The color of raw teff flour and ground extrudates was measured in a Hunter Lab
D 25L Colorimeter (Hunter Associates Lab, Inc., Reston, VA). L (lightness), a (redness),
and b (yellowness) values are reported. Color readings were taken after the sample
was placed into a 100 mm dish. The dish was then rotated 90 degrees and readings were
taken again. The average of the two readings of the same sample was reported for each
sample. Duplicate samples were used for each experimental run. Similar procedure of

color determination was used for rice flour by Grenus et a1. (1993); beans (Gujska and

50
Khan, 1991) and Fletcher et al. (1985) for maize grits.

3.4 Experimental Design and Statistical Analysis

Changing one extrusion variable while keeping others constant gives no insight
into the interactions among the process variables unless many combinations are examined
(Colonna et al., 1989). Employing a statistically designed multiple-factor experiment for
economy of experimental points together with response surface methodology has been
used as a solution to overcome this problem.

A fractional factorial experimental design with three variable, three-level response
surface methodology with three replicates at the center point (Henika, 1972) was used.
The three major extrusion variables investigated were total feed moisture (20-30%), barrel
temperature in the last two heating zones (125-175°C), and screw speed (100-300 rpm).
The actual experimental conditions are shown in Table 3.3. The decision for the levels
of the independent variables was based on the preliminary extrusion runs and observations
made by the author and his major Professor. Two replications were made for each run.

Data obtained were analyzed by the response surface regression (RSReg)
procedure in MINITAB (Release 10.1, Minitab Inc. State College, PA). The actual
extrusion process conditions were coded to the levels (x) of -1, 0 and +1 according to the

following equations:

51

T-lSO

”2 T ‘21)
X3 = N- 200 (22)

Where x1 is the dimensionless moisture content, MC the moisture content (%wet basis);
x2 is the dimensionless barrel temperature, T the actual barrel temperature (°C); and x3
is the dimensionless screw speed, N the actual screw speed (rpm).

The second-order polynomial equation for the response function (11) against three
independent variables (x1 = moisture content, x2 = barrel temperature, and x3 = screw

speed) in coded terms can be represented as:

Tl = Bo + [31x1 + Bzxz +B3X3 + B4Xi + [35x3 +B6X§ + B7X1X2 + B8X1X3 +‘39X2X3 (25)

The response surface regression procedure estimates parameters of a complete
quadratic response surface by least-squares regression and gives information about the fit
in the form of analysis of variance (ANOVA). From the ANOVA, the contribution of
linear, quadratic and cross product effects to the statistical fit was obtained. An IBM-
compatible Personal Computer was used to perform the statistical calculations and all the

analyses.

52

Table 3.3 Experimental Design for Teff Extrusion

 

Test Moisture Screw speed Barrel temperature profile (°C)

 

run content (%) (rpm)
zone 1 zone 2 zone 3 zone 4 zone 5

 

1 30 200 30 50 75 175 175
2 25 100 30 50 75 175 175
3 25 300 30 50 75 175 175
4 20 200 30 50 75 175 175
5 25 200 30 50 75 150 150
6 30 200 30 50 75 125 125
7 25 300 30 50 75 125 125
8 25 100 30 50 75 125 125
9 20 200 30 50 75 125 125
10 25 200 30 50 75 150 150
11 30 100 30 50 75 150 150
12 30 300 30 50 75 150 150
13 25 200 30 50 75 150 150
14 20 100 30 50 75 150 150

15 20 300 30 50 75 150 150

 

CHAPTER IV

RESULTS AND DISCUSSION

4.1 Model Fitting

The experimental data and response surface layout are shown in Appendix A.
After the raw data were analyzed by the response surface regression procedure, a stepwise
regression procedure was used to select the "best" model equation under the specified
conditions for each dependent variable. Stepwise regression procedure inserts the
variables in turn until the regression equation is satisfactory using the partial correlation
coefficient as a measure of the importance of variables not yet in the equation. It is also
one of the best of the variable selection procedures recommended its use. Computer
printout of the stepwise regression procedure is given in Appendix B. Coded or
”standardized" xi, values were used to provide proper preference among the variables in
the calculation of the models.

To assess the significance of each effect (linear, quadratic and cross product), the
parameter estimates, t-ratios, R-square (the percentage square of the multiple correlation
coefficient), and P values obtained from the regression outputs were considered. F-to-

enter and F-to-remove value of 4 was used for the stepwise regression procedure. The

53

54

non-significant terms, judged by the tests (P < 0.05) from the second order polynomial
were deleted and the equation was recalculated to give the final model for each dependent
variable. Sensible examination of the model through examination of residuals was also
made.

All the models discussed in this work are statistically significant at the 5% level.
Models for water absorption index, product moisture content, %Torque, die temperature,
and die pressure, however, show considerable lack of fit. For such models with
significant lack of fit, the nature of inadequacy was sought by analyzing the residuals.
The lack of fit shows that there is variation in the model other than random error (such
as cubic effects of the independent variables). However, models with a correlation
coefficient as high as 90% or more can give an adequate overall view over the
multifactorial field of extrusion cooking (V aniopaa, 1991). Besides, the residual analyses

showed that these deficient models can still be used with proper caution.

4.2 Expansion Ratio

In the teff extrusion experiments, the expansion ratio (ER) of the dry extrudate
rods were between 2.37 and 2.99. From the raw data, the lowest ER value was obtained
with a feed moisture content of 30 %, barrel temperature of 150°C and at a screw speed
of 100 rpm, whereas the highest ER value was with 20 %, 150°C, and 100 rpm,
respectively. This observation indicated that ER was significantly affected by feed
moisture only among the three independent variables considered.

Table 4.1 shows the estimated regression coefficients, t-ratios and P values for ER.

55

The t-ratio is a statistic that tests the null hypothesis that the model parameter is zero.
The P value is the probability of getting a greater t-ratio, its value being less than 0.05
is often considered as significant evidence that the parameter is not zero. Based on the
P values, it was found that among the linear and second order effects, only that of
moisture was significant (P<0.05). The results also indicated that all of the interaction

terms were significant (P<0.05).

Table 4.1. Estimated Regression Coefficients for Expamion Ratio Using Coded
Variables (x1 = feed moisture, x2 = barrel temperature and x3 = screw speed)

 

 

Term Coef Stdev t-ratio P
Constant 2.6977 0.02300 117.278 0.000
x1 -0.1792 0.01409 -12.721 0.000
x2 —0.0169 0.01409 -1.197 0.245
x3 -0.0068 0.01409 -0.479 0.637
x1*x1 —0.0842 0.02073 -4.062 0.001
x2*x2 0.0259 0.02073 1.247 0.227
x3*x3 0.0348 0.02073 1.679 0.109
x1*x2 0.0809 0.01992 4.060 0.001
x1*x3 0.0627 0.01992 3.146 0.005
x2*x3 0.0830 0.01992 4.167 0.000
s = 0.05634 R—sq = 92.0% R—sq(adj) = 88.4%

 

The resulting second order polynomial using coded variables, after removing the

non-significant terms and recalculating becomes:

56

ER = 2.732 - 0.179;:l - 0.089x12 + 0.081x1x2 + 0.063x,x,

+ 0.083x2x3 (24)

The final model equation was obtained from the stepwise regression output
(Appendix B). Equation 24 accounted for 89.69 % of the total variation in ER and from
which 64.8 % of this variability was accounted by moisture content with in the extrusion
processing variables considered in the experiment.

Figure 4.1 shows the response surface for ER against feed moisture and barrel
temperature. Eventhough the effect of temperature on ER was not significant, it had
greater effect than that of screw speed as evidenced by comparing coefficients of x1 and
x3.

It should be noted that due to the unavailability of published data on expansion
ratio of teff, it was not possible to directly compare this work with the others. However,
attempts are made to compare it with other cereals. Even in this case the differences in
product compositions and extrusion conditions used by other researchers made it still
difficult to compare results. Nevertheless, such general comparisons should give general
idea how teff behaved in extrusion cooking process compared to other cereals with in the
range of variables.

Increasing feed moisture content of teff flour caused a decrease in ER. The
highest ER was obtained at 20 % moisture and decreased significantly as moisture content

increased up to 30 %. Similar results were described by Faubion and Hoseney (1982a)

57
for wheat starch, and by Chinaswamy and Hanna ( 1988) and Owusu-Ansah et al. (1984)

for corn starch. This was commonly observed in many snack foods (Harper, 1981) which
may be because the amount of expansion occurring in food material depends on the
pressure differential between the die and atmosphere. Foods with lower moisture tend to
be more viscous than those with higher moisture and, therefore, the pressure differential
would be smaller for higher moisture foods leading to a less puffed product.
Chinnaswamy and Hanna (1988) described that low moisture content in starch may
restrict flow of material inside the extruder barrel, increasing shear rate and residence
time, which might increase the degree of starch gelatinization and expansion.
Diameteral expansion of starch materials is known to depend on elasticity of the
melt (Launay and Lisch, 1983) and a decrease in moisture content results in improved
moisture distribution and gives a higher melt elasticity that will increase ER. This may
also explain why the effect of barrel temperature was not significant on ER since melt
elasticity is not strongly dependent on temperature (Launay and Lisch, 1983). According
to Figure 4.1 higher temperatures at higher feed moisture content resulted in a slight
increase in ER. This may be because at higher moisture the higher temperature produced
higher vapor pressure to favor expansion. However, at low moisture content higher
temperature resulted in a decrease in ER, and the maximum ER was obtained at the
lowest temperature considered in the experiment. A possible explanation for such
phenomenon could be higher barrel temperature especially at low moisture content
decreases melt viscosity to reduce expansion. Similar results were reported for

degerminated corn meal extruded at 20-30 % moisture at 100-200°C (Halek and Chang,

 

Figure 4.1 Expansion Ratio versus Feed Moisture and Barrel Temperature
at 200 rpm Screw Speed

59

1991). Another explanation for the lower radial expansion at high temperature could be
the decreased die pressure exerted by the dough at higher temperature.

The composition of the raw materials has a significant effect on the texture, color,
and taste of the extruded product. Mercier and Feillet (1975) reported that among
different types of starches, only a few expanded better than others. Apparently, starch
content and quality factors (amylose, amylopectin) most significantly affected expansion
properties (Chinnaswamy and Hanna, 1988). The maximum expansion obtained was 291
%. In general, this value was higher than the expansion ratio values reported for wheat
flour extruded at 14% moisture and 135°C barrel temperature (Mannonen, 1979); barley
flour extruded at 18% moisture and 160°C barrel temperature, and oat flour extruded at
16% moisture and 135°C temperature (Hagqvist, 1981).

According to Horn ( 1977), the maximum expansion obtained with whole grains
was 400% (starch content in raw materials was 65—78%). The lower ER of teff extrudates
could be because of the lower content of starch in the raw material. Because starch has
been known to play an important role as the major component that builds a matrix of
extrudate and a key element responsible for expansion. In earlier studies, radial
expansion decreased with increasing fiber content (Anderson et al., 1981; Breen et al.,
1977; Lawton et al., 1985; Lue et al., 1991). Therefore, when examining the ER of teff
extrudates, the fiber content of teff flour needs consideration. Teff flour used in the
experiment had relatively higher fiber content (9.3%) as compared to wheat flour (1-2%);

corn flour (0.507%); rice flour (030.35%), and oat flour (2—12%).

60
4.3 Bulk Density

The Bulk Density (BD) of the dry extruded rods varied from 0.126 to 0.316 glcm’.
Table 4.2 shows the regression coefficients for BD. Figure 4.2 shows the response
surface for BD as a function of feed moisture content barrel temperature. These two
independent variables jointly accounted for 75.89% of the total variation in BD. All the
linear terms, square terms and one of the interactions (moisture * temperature) were

significant (P<0.05).

Table 4.2 Estimated Regression Coefficients for Bulk Density Using Coded Variables
(x1 = feed moisture, x2 = barrel temperature and x3 = screw speed)

 

 

Term Coef Stdev t-ratio P
Constant 0.19405 0.003015 64.362 0.000
x1 0.05601 0.001846 30.337 0.000
x2 -0.03373 0.001846 -18.270 0.000
x3 -0.03132 0.001846 -16.963 0.000
x1*x1 0.02000 0.002718 7.360 0.000
x2*x2 -0.00647 0.002718 -2.382 0.027
x3*x3 0.00765 0.002718 2.813 0.011
x1*x2 —0.01075 0.002611 —4.116 0.001
x1*x3 -0.00538 0.002611 —2.059 0.053
x2*x3 —0.00185 0.002611 —O.707 0.488
s = 0.007385 R—sq = 98.8% R-sq(adj) = 98.2%

 

Barrel temperature and screw speed had a negative effect on BD. As could be
expected moisture had the highest effect followed by temperature and screw speed. The

resulting polynomial using coded variables after removal of non-significant terms on

61

recalculation gives:

BD = 0.194 + 0.0ml - 0.034x2 - 0.031x3 + ().02x,2 - 0.007x,2 + 0.00m,2

- 0.011x1x2 (25)

The less dense product result obtained with an increase in expansion ratio was in
agreement with the results of Harmann and Harper (1973); Anderson et al., (1969);
Launay and Lisch (1983), and Fletcher et al., (1985) for extrusion cooked corn grits. BD
decreased with screw speed. One possible explanation given (Hoseney et al., 1991) was
that air was trapped in the melt because of the action of the screws. At higher screw

speed, more air bubbles were trapped which resulted in less dense teff extrudate

62

 

 

 

 

 

A/
f‘ ‘ / / 217]]
III
3 w ’ / ,5 22555557.
[I] '
>. 025 / / [fill/IIIIIIII‘
i III/IIIIIIII’
, III/IIIIIIIIII
am lll/IIIII’I’ 4
3 42:51:00”?
””00 "'I .
es ,;;I’Z;'II’$"II”
5 I, ’ I, ”I’ ”I’
In are '0’,‘ ”I"'I/-- m
10 m o 0

Figure 4.2 Bulk Demity versus Feed Moisture and Barrel Temperature
at 200 rpm Screw Speed

63
4.4 Water Absorption Index

The Water Absorption Index (W A1) of ground extruded samples varied from 4.869
to 6.815 g/g. This range of WAI values obtained in this experiment are in agreement
with the range of 3-10 g/g values reported for extruded starches (Anderson et al., 1969;
Conway, 1971; Mercier and Feillet, 1975; Gomez and Augilera, 1984). In general, the
WAI values obtained for teff flour are higher than the values reported for wheat flour
(Mannonen, 1979), barley flour and oat flour (Hagqvist, 1981). Table 4.3 shows the
regression coefficients for WAI. Figure 4.3 shows the response surface for WAI as a
function of barrel temperature and screw speed. These two variables accounted for

46.68% of the total variation in WAI.

Table 4.3. Estimated Regression Coefficients for Water Absorption Index Using
Coded Variables (x1 = feed moisture, x2 = barrel temperature, x3 = screw speed)

 

 

Term Coef Stdev t-ratio P
Constant 6.0624 0.11490 52.761 0.000
x1 0.2635 0.07036 3.745 0.001
x2 0.3354 0.07036 4.767 0.000
x3 -0.3467 0.07036 -4.927 0.000
x1*x1 -0.0084 0.10357 -0.082 0.936
x2*x2 -0.3436 0.10357 -3.318 0.003
x3*x3 0.0453 0.10357 0.438 0.666
xl*x2 -0.0580 0.09951 —0.583 0.567
xl*x3 0.0060 0.09951 0.060 0.953
x2*x3 0.2780 0.09951 2.794 0.011

s = 0.2815 R-sq = 80.1% R—sq(adj) = 71.2%

 

64

All the linear terms, one of the square terms (temperature * temperature) and one
of the interaction terms (temperature * screw speed) were significant (P<0.05).The

resulting polynomial, after removal of non-significant terms and on recalculation gives:

WAI = 6.083 + 0.264x, + 0.335x2 - 0.347x3 - 0346):; + 0.278x2x3 (26)

WA] increased with moisture and temperature. This direction of influence of
process conditions on WAI of extruded teff flour is consistent with the findings of

Owusu-Ansah et al. (1982) on corn starch and Olkku and Hagqvist (1983) on barley

extruded with twin-screw extruder.

65

 

Figure 4.3 Water Absorption Index versus Barrel Temperature
and Screw Speed at 25% Feed Moisture

66
4.5 Water Solubility Index

The water solubility index (WSI) of the ground extruded samples varied from
4.523 to 17.031%. Table 4.4 shows the regression coefficients for WSI. The effect of
feed moisture and barrel temperature on WSI is presented in Figure 4.4. These two
variables accounted for 55.58% of the variability in WSI. All the linear, square and
interaction terms were significant (P < 0.05). When forming the final polynomial
equation, however, the square term for screw speed (x3 * x3) and the interaction term for
moisture and temperature were omitted based on the results of the stepwise regression.

The resulting polynomial after recalculation gives:

ws1= 6.526 - 2.75xl + 3.33x2 + 2.69x3 + 1.84x,2 + 2.55x,2

-1.66x,x, + 0.91x2x3 (27)

Higher WSI values were obtained at low moisture and high temperature where the
process conditions are the most severe. The increase in WSI as moisture decreases is in
agreement with earlier works for corn grits (Anderson et al., 1969; Conway, 1971); corn
starch (Mercier and Feillet, 1975) and wheat starch (Paton and Spratt, 1984). Greater
shear degradation of starch during low moisture extrusion was given as the main reason
for such result (Anderson et al., 1969). Dextrinization as a predominant mechanism of
starch degradation during low-moisture, high-shear extrusion was also noted by Gomez

and Augilera (1984).

67

Table 4.4 Estimated Regression Coefficients for Water Solubility Index Using Coded
Variables (x1 = feed moisture, x2 = barrel temperature, x3 = screw speed)

 

 

Term Coef Stdev t-ratio p
Constant 5.945 0.3428 17.345 0.000
x1 -2.748 0.2099 -13.092 0.000
x2 3.327 0.2099 15.849 0.000
x3 2.685 0.2099 12.793 0.000
x1*x1 1.917 0.3090 6.204 0.000
x2*x2 2.622 0.3090 8.485 0.000
x3*x3 0.944 0.3090 3.055 0.006
x1*x2 -0.760 0.2968 -2.559 0.019
x1*x3 -1.656 0.2968 -5.578 0.000
x2*x3 0.911 0.2968 3.068 0.006
s = 0.8396 R-sq = 97.4% R-sq(adj) = 96.2%

 

 

 

Figure 4.4 Water Solubility Index versus Feed Moisture
and Barrel temperature at 200 rpm Screw Speed

69

4.6 Product Moisture Content

The wet basis product moisture content (PMC) of the extrudates varied from 5.14
to 14.08%. Table 4.5 shows the regression coefficients for PMC. All the linear terms,
one of the interactions (moisture * temperature) and two of the square terms except

(temperature "‘ temperature) were significant (P < 0.05).

Table 4.5 Estimated Regression Coefficients for Product Moisture Content Using
Coded Variables (x1 = feed moisture, x2 = barrel temperature, x3 = screw speed)

 

 

Term Coef Stdev t-ratio p
Constant 8.6024 0.12051 71.381 0.000
x1 3.2358 0.07380 43.846 0.000
x2 -0.8087 0.07380 —10.958 0.000
x3 —0.7656 0.07380 —10.374 0.000
x1*x1 0.8455 0.10863 7.783 0.000
x2*x2 -0.0603 0.10863 -0.555 0.585
x3*x3 0.6295 0.10863 5.795 0.000
xl*x2 —0.3548 0.10437 —3.400 0.003
x1*x3 0.0060 0.10437 0.058 0.955
x2*x3 0.1889 0.10437 1.810 0.085
s = 0.2952 R-sq = 99.1% R-sq(adj) = 98.7%

 

Higher feed moisture and lower extrusion temperature produced extrudates with

higher moisture content (Figure 4.5), as could be expected.

70

 

 

Figure 4.5 Product Moisture Content versus Feed Moisture and Barrel
Temperature at 200 rpm Screw Speed

71

The resulting polynomial after removal of non-significant terms on recalculation
gives:
PMC = 8.565 + 3.236xl - 0.809x2 - 0.766x3 + 0.85x,2

+ 0.63x32 - 0.35x1x2 (28)

Similar trends were observed for rice flour using a Clextral BC 45 twin-screw
extruder by Sun Pan et al. (1991) and for corn meal flour using a ZSK-30 twin-screw

extruder by Halek and Chang (1991).

4.7 Torque

The percentage torque (%T) developed during teff extrusion varied from 24.24 to
60.29 %. The regression coefficients and response surface for %T are shown in Table
4.6 and Figure 4.6, respectively. All the linear terms and two of the square terms except
(moisture * moisture) and two of the interactions except (moisture * temperature) were
significant (P < 0.05).

The resulting polynomial after removal of non-significant terms which accounts

for 97.5% of the total variation in torque, on recalculation gives:

%T = 32.83 -5.68x, - 8.08x2 - 6.34x3 + 2.4x,2 + 4.69x,2

+ 2.89x1x3 + 4.23x2x3 (29)

72

Table 4.6 Estimated Regression Coefficients for Percentage Torque Using Coded
Variables (x1 = feed moisture, x2 = barrel temperature, x3 = screw speed)

 

 

Term Coef Stdev t-ratio P
Constant 32.825 0.7496 43.792 0.000
x1 ~5.678 0.4590 -12.370 0.000
x2 -8.083 0.4590 -17.610 0.000
x3 —6.339 0.4590 —13.811 0.000
x1*x1 0.008 0.6757 0.012 0.991
x2*x2 2.402 0.6757 3.555 0.002
x3*x3 4.688 0.6757 6.939 0.000
x1*x2 0.152 0.6491 0.234 0.818
x1*x3 2.886 0.6491 4.446 0.000
x2*x3 4.231 0.6491 6.517 0.000
s = 1.836 R-sq = 97.5% R-sq(adj) = 96.3%

 

A minimum torque value can be obtained by employing higher moisture, barrel
temperature and screw speed. The lower torque at higher screw speed could be explained
by the length of filled flights in the extruder barrel (Ollet et al., 1989). In this
experiment, the dry feed rate was fixed at 9.07 kg/h and the extruder was starve-fed. An
increase in the screw speed would cause a decrease in the length of filled section (T ayeb
et al., 1989) and thus the torque was reduced. The shorter the filled length in the barrel,
the smaller the load of the extruder motor drive and hence the lower the extruder torque

at higher screw speed. Similar results were reported by Guy and Home (1988).

73

 

 

 

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Figure 4.6 Percent Torque versus Barrel Temperature and Screw Speed
at 25 % Feed Moisture

74

4.8 Die Temperature

The die temperature (DT) as a function of feed moisture and barrel temperature
is illustrated in Figure 4.7. The DT exceeds the set barrel temperature at most process
conditions. As shown in Table 4.7, all the linear terms, two of the square terms except
(moisture * moisture) and two of the interactions except (moisture * temperature) were

significant (P < 0.05). the resulting polynomial after recalculation gives:

DT = 165.9 - 6.83xl + 14.76x2 + 6.57x3 + 0.94x,2 - 1.9x,2

- 1.56x1x3 - 0.69x2x3 (30)

Table 4.7 Estimated Regression Coefficients for Die Temperature Using Coded
Variables (x1 = feed moisture, x2 = barrel temperature, x3 = screw speed)

 

 

Term Coef Stdev t-ratio P
Constant 165.833 0.3036 546.187 0.000
x1 —6.830 0.1859 -36.736 0.000
x2 14.764 0.1859 79.406 0.000
x3 6.566 0.1859 35.317 0.000
x1*x1 0.056 0.2737 0.205 0.840
x2*x2 0.944 0.2737 3.449 0.003
x3*x3 -l.083 0.2737 -3.958 0.001
x1*x2 —0.083 0.2629 -0.317 0.755
x1*x3 -l.556 0.2629 -5.919 0.000
x2*x3 -0.694 0.2629 —2.640 0.016

s = 0.7437 R—sq = 99.8% R-sq(adj) = 99.7%

 

75

 

Figure 4.7 Die Temperature versus Feed Moisture and Barrel Temperature
at 200 rpm Screw Speed

76

4.9 Die Pressure

A response surface for die pressure (DP) as a function of barrel temperature and
screw speed is presented in Figure 4.8. All the linear and interaction terms were
significant (Table 4.8) at P < 0.05. The only significant square term, however, was
(screw speed x screw speed). The resulting polynomial using coded variables on

recalculation gives:

DP = 3574 - 1030xl - 1259x2 - 2021x3 + 610x,2 + 564x1x2

+ 953x1x3 + 599x2x3 (31)

Table 4.8 Estimated Regression Coefficients for Die Pressure Using Coded variables
(x1 = feed moisture, x2 = barrel temperature, x3 = screw speed)

 

 

Term Coef Stdev t-ratio P
Constant 3534 147.21 24.004 0.000
XI —1030 90.15 -11.428 0.000
x2 -1259 90.15 -13.969 0.000
x3 -2021 90.15 —22.419 0.000
x1*x1 162 132.69 1.221 0.236
x2*x2 -91 132.69 -0.687 0.500
x3*x3 616 132.69 4.639 0.000
x1*x2 564 127.49 4.425 0.000
x1*x3 953 127.49 7.476 0.000
x2*x3 599 127.49 4.698 0.000

s = 360.6 R-sq = 97.9% R-sq(adj) = 97.0%

 

77

 

 

Figure 4.8 Die Pressure versus Barrel Temperature and Screw Speed
at 25 % Feed Moisture

78

Die pressure (DP) decreased with increasing feed moisture, barrel temperature and
screw speed. The DP, according to Martelli (1983), is related to the feed rate and
viscosity of dough as DP = er/Kf where Q, It and Kf are the output rate, melt viscosity
and die conductance, respectively. In this experiment, the output rate and die conductance
were constant Therefore, the DP was proportional to the melt viscosity of the dough.
Higher feed moisture and die temperature will certainly decrease the viscosity of the
dough which may result in a lower DP. A higher screw speed caused a higher product
temperature in the melting zone which, in tum, decreased the dough viscosity and resulted
a lower DP. Decreasing DP with increasing screw speed has also been observed by other

researchers such as Hsieh et al. (1991).

4.10 Specific Mechanical Energy

The specific mechanical energy (SME) input ranges from about 212 to 833 kJ/kg.
This range of SME is in agreement with the range of 200-1000 kJ/kg reported for most
co-rotating twin-screw extruders (Mange and Boissonnat, 1986). Figure 4.9 shows the
effect of feed moisture and screw speed on SME. The regression coefficients for SME
are given in Table 4.9. All linear, square and interaction terms were significant

(P < 0.05). The resulting polynomial using coded values gives:

SME = 459.5 - 117.4x, - 98.7x2 + 183.1x3 + 14.7x12 + 27.9x22 + 21.6x32

+ llxlx2 - 24.5x,x3 - 8.5x2x3 (32)

79

Table 4.9 Estimated Regression Coefficients for Specific Mechanical Energy Using
Coded Variables (x1 = feed moisture, x2 = barrel temperature, x3 = screw speed)

 

 

Term. Coef Stdev t-ratio P
Constant 459.5 3.409 134.822 0.000
x1 -117.4 2.087 -56.243 0.000
x2 -98.7 2.087 -47.283 0.000
x3 183.1 2.087 87.702 0.000
x1*x1 14.7 3.072 4.768 0.000
x2*x2 27.9 3.072 9.097 0.000
x3*x3 21.6 3.072 7.045 0.000
x1*x2 11.0 2.952 3.719 0.001
x1*x3 -24.5 2.952 —8.289 0.000
x2*x3 —8.5 2.952 -2.880 0.009
s = 8.349 R—sq = 99.9% R-sq(adj) = 99.8%

 

The SME increased with screw speed and it was the most important parameter
affecting SME. Similar results were reported by Tsao et al. (1978); Fletcher et al. (1985);
Della Valle et al. (1989); Hsieh et al. (1989, 1990) and Grenus et al. (1993).

The decrease of SME with increasing feed moisture could be explained by the
lubricating effect of water. This result is in agreement with the findings of van Zuilichem
et a1. (1975) and Bruin et al. (1978) using corn grits, Faubion et al. (1982a) on starch, and
Bhattacharya and Hanna (1987c) using corn gluten meal.

The increase in barrel temperature caused a decrease in SME requirement. This
can be explained by the fact that higher temperature decreases dough viscosity which, in
turn, decreases torque and SME. Similar trends of increasing SME when barrel
temperature decreases were observed in twin-screw extruders (Antila et al., 1983; Meuser

and van Lengerich, 1984a, and Meuser et al., 1987, 1989).

80

 

Figure 4.9 Specific Mechanical Energy versus Feed Moisture
and Screw Speed at 150°C Barrel Temperature

8 l
4.11 Color

The regression coefficients for Hunter color parameters (’L’- lightness’, ’a -
redness’ and ’b - yellowness’) of teff flour extrudates are presented in Tables 4.10, 4.11
and 4.12 for ’L’, ’a’ and ’b’, respectively. The only significant variables (P < 0.05) that
affected color of the extrudates were feed moisture and screw speed. The response
surfaces for ’L’ and ’b’ are shown in Figures 4.10 and 4.11, respectively. For color ’a’,
however, no response surface is given because of the low R2 value (R2 = 0.47) obtained
from this work which showed that this response can not be adequately predicted by the
parameters considered in the model. The resulting polynomials, after removal of non-

significant terms and on recalculation using coded values gives:

L = 39.94 - 5.62x1 + 1.89:1, (33)

a = 3.695 + 0.306xl - 0.29xlx3 (34)

b = 10.59 - 0.859xl + 0.353x3 (35)

82

Table 4.10 Estimated Regression Coefficients for Color ’L’ Using Coded Variables
(x1 = feed moisture, x2 = barrel temperature, x3 = screw speed)

 

 

Term Coef Stdev t-ratio P
Constant 40.450 0.6932 58.352 0.000
x1 —5.622 0.4245 -13.243 0.000
x2 0.584 0.4245 1.377 0.184
x3 1.894 0.4245 4.461 0.000
x1*x1 —1.122 0.6249 —1.795 0.088
x2*x2 0.603 0.6249 0.965 0.346
x3*x3 —0.428 0.6249 -0.685 0.501
x1*x2 0.306 0.6003 0.510 0.616
x1*x3 1.063 0.6003 1.770 0.092
x2*x3 -0.363 0.6003 -0.604 0.553
s = 1.698 R—sq = 91.1% R-sq(adj) = 87.2%

 

Table 4.11 Estimated Regression Coefficients for Color ’a’ Using Coded Variables
(x1 = feed moisture, x2 = barrel temperature, x3 = screw speed)

 

 

Term Coef Stdev t-ratio P
Constant 3.6333 0.13084 27.769 0.000
XI .3062 0.08012 3.822 0.001
x2 .0656 0.08012 -0.819 0.422
x3 .0344 0.08012 0.429 0.672
x1*x1 .0198 0.11794 —0.168 0.868
x2*x2 .0115 0.11794 0.097 0.924
x3*x3 .1240 0.11794 1.051 0.306
x1*x2 .1250 0.11331 —1.103 0.283
x1*x3 .2875 0.11331 -2.537 0.020
x2*x3 .0313 0.11331 0.276 0.786
s = 0.3205 R—sq R—sq(adj) = 6%

 

83

Table 4.12 Estimated Regression Coefficients for Color ’b’ Using Coded Variables
(x1 = feed moisture, x2 = barrel temperature, x3 = screw speed).

 

 

Term Coef Stdev t-ratio P
Constant 10.6000 0.14715 72.034 0.000
x1 —0.8594 0.09011 —9.537 0.000
x2 0.0250 0.09011 0.277 0.784
x3 0.3531 0.09011 3.919 0.001
x1*x1 —0.0687 0.13264 —0.518 0.610
x2*x2 0.1000 0.13264 0.754 0.460
x3*x3 -0.0562 0.13264 —0.424 0.676
x1*x2 0.2063 0.12744 1.618 0.121
x1*x3 0.1375 0.12744 1.079 0.293
x2*x3 -0.0562 0.12744 -0.441 0.664
s = 0.3604 R-sq = 84.8% R-sq(adj) = 77.9%

 

The lightness (L) and yellowness (a) values decreased with increased feed moisture
indicating that at higher moisture level, extrudates were darker as observed visually. The
decrease in ’L’ value of teff extrudates with the increase of moisture is consistent with
the findings of Gujska and Khan (1991) for extruded bean high starch fractions, and
Hsieh et al. (1990) on cottonseed using twin-screw extruder. The increase in darkness
(lower L values ) could be attributed to the compact and denser structure which resulted
with increasing feed moisture (Hsieh et al., 1990). Another possible reason could be the
occurrence of browning reactions between reducing sugars and water which resulted
brown pigments. Increasing screw speed produced a lighter color (higher L value).

Similar trends were reported for corn grits (Fletcher et al., 1985) extruded with twin-

84

screw extruder. Higher screw speed resulted in a shorter residence time of teff extrudates
in the extruder and therefore lighter extrudates were obtained from higher screw speed.
The temperature of the barrel generally did not exert significant effect on product color.

Similar conclusion was made by Hsieh et al. (1990).

85

 

Figure 4.10 Color ’L’ versus Feed Moisture and Screw Speed
at 150°C Barrel Temperature

 

Figure 4.11 Color ’b’ versus Feed Moisture and Screw Speed
at 150°C Barrel Temperature

87

 

 

 

 

 

 

 

 

 

 

 

 

 

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CHAPTER V

SUMMARY AND CONCLUSIONS

Extrudate properties were influenced differently by different extrusion variables
considered. An increase in feed moisture resulted in increased bulk density, water
absorption index, and product moisture content, but in decreased expansion ratio, water
solubility index, percent torque, die temperature, die pressure, specific mechanical energy,
and lower ’L’ and ’b’ color values. An increase in barrel temperature resulted in
decreased bulk density, product moisture content, percent torque, die pressure, and
specific mechanical energy but in increased water absorption index, water solubility index,
die temperature, and values of color ’L’ and ’b’. Increasing screw speed resulted
increased water solubility index, die temperature, and specific mechanical energy, but
caused a decrease in bulk density, water absorption index, product moisture content,
percent torque, and die pressure.

The square terms of feed moisture content significantly affected bulk density,
water solubility index, product moisture content, and specific mechanical energy. The
square terms of barrel temperature significantly influenced all the dependent variables

considered except expansion ratio and die pressure. The square terms of screw speed

88

89

influenced bulk density, percent torque, die temperature, die pressure, and specific
mechanical energy.

The interaction term of moisture and temperature was significant for expansion
ratio, bulk density, product moisture content, die pressure, and specific mechanical energy
whereas, the interaction term of moisture and screw speed was significant for expansion
ratio, water solubility index, die temperature, die pressure, and specific mechanical
energy. The dependent variables significantly influenced by the interactions of
temperature and screw speed were expansion ratio, water absorption index, water
solubility index, percent torque, die temperature, die pressure, and specific mechanical
energy.

The overall results show that teff flour can successfully be processed using a twin-
screw extruder to produce product properties similar to other extruded products currently
used in the food industry. The high correlation of the models with the measured data
shows that the calculated second order polynomials are sufficiently accurate and valuable
for predicting properties of teff extrudates and extruder response with in the range of

variables considered.

CHAPTER VI

SUGGESTIONS FOR FURTHER RESEARCH

Product characteristics in extrusion cooking are known to be determined by many
additional factors than considered in this study. Therefore, in order to gain complete
understanding and appreciation of teff extrusion process, other variables including
physical characteristics and physicochemical changes of the raw material as a result of
the process such as particle size, rheology, quality factors of starch (amylose,
amylopectin, extent of starch damage) and sensory assessment (texture, flavor, overall
acceptability), and additional extruder characteristics such as different screw
configurations need to be studied.

Another area of interest could be to produce teff products by extrusion cooking
to a suitable form to be used by humans. The product may well be blended with other
cereals like wheat or rice resulting a nutritionally improved product. The extruded
product in this work had a mild flavor and attractive color. The measured functional
product properties of teff extrudates also indicated that teff flour has the capability to be
used as breakfast cereal or snack food. This may help to meet the diverse and particular

taste of a range of cultures and individuals.

APPENDICES

91

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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APPENDIX B

ANOVA AND STEPWISE REGRESSION TABLES FOR DEPENDENT

Analysis of variance for ER

Source DF
Regression 9
Linear 3
Square 3
Interaction 3
Residual Error 20
Lack-of-Fit 3
Pure Error 17
Total 29

Stepwise Regression

F-to-Enter: 4.
Response is ER
Step 1
Constant 2.685
x1 -0.179
T-Ratio -7.18
x4
T-Ratio
x9
T-Ratio
x7
T-Ratio
x8
T-Ratio
S 0.0998
R-Sq 64.80
Note: x4 = x1*x1, x5
x7 = x1*x2, x8

00000000

00
on

2
2.732

-0.179
—7.93

-0.089
-2.68

= x2*x2, x6
= x1*x3,

VARIABLES

Seq SS
.729362
.519005
.071503
.138855
.063492
.008636
.054857
.792855

3
2.732

-0.179
-8.99

-0.089
-3.03

0.083
2.94

0.0798
79.13

x9

OOOOOOO

F-to-Remove:

Adj ss

.729362
.519005
.071503
.138855
.063492
.008636
.054857

9 predictors, with N

4
2.732

-0.179
-10.66

-0.089
-3.60

0.083
3.49

0.081
3.40

0.0673
85.73

x3*x3
x2*x3

92

Adj MS
.081040
.173002
.023834
.046285
.003175
.002879
.003227

0000000

.00

= 30

5
2.732

-0.179
-12.28

-0.089
~4.15

0.083
4.02

0.081
3.92

0.063
3.04

0.0583
89.69

25.
54.

7.
14.

0

F
53
50

58

.89

O

0000

Analysis of variance for ED

93

Source DF Seq SS
Regression 9 0.089043 0
Linear 3 0.084096 0
Square 3 0.003765 0
Interaction 3 0.001182 0
Residual Error 20 0.001091 0
Lack-of-Fit 3 0.000358 0
Pure Error 17 0.000733 0
Total 29 0.090134

Stepwise Regression

Adj 53
.089043
.084096
.003765
.001182
.001091
.000358
.000733

9 predictors, with N

4
0.1947

0.0560
20.20

-0.0337
-12.17

-0.0313
-11.30

0.0199
4.91

0.0111

F-to-Enter: 4.00 F-to-Remove:
Response is BD on

Step 1 2 3
Constant 0.2053 0.2053 0.2053
x1 0.0560 0.0560 0.0560
T-Ratio 5.93 7.90 14.70
x2 -0.0337 -0.0337
T-Ratio -4.76 -8.85
x3 —0.0313
T-Ratio -8.22
x4
T—Ratio
x7
T-Ratio
x6
T-Ratio
x5
T-Ratio
8 0.0378 0.0284 0.0152
R-Sq 55.69 75.89 93.30

96.59

Adj MS F
0.009894 181.40
0.028032 513.96
0.001255 23.01
0.000394 7.23
0.000055
0.000119 2.77
0.000043

.00

= 30
5 6
0.1947 0.1901
0.0560 0.0560
23.66 26.38
-0.0337 -0.0337
-14.25 -15.89
-0.0313 -0.0313
-13.23 -14.75
0.0199 0.0205
5.75 6.58
-0.0107 -0.0107
-3.21 -3.58
0.0081
2.61
0.00947 0.00849
97.61 98.16

P
.000
.000
.000
.002

O 0000

.074

7
0.1941

0.0560
28.61

-0.0337
-17.23

-0.0313
-16.00

0.0200
6.94

-0.0107
—3.88

0.0076
2.65

-0.0065
-2.25

0.00783
98.50

94

Analysis of variance tor‘wAI

Source
Regression
Linear
Square

Interaction
Residual Error

Lack-of-Fi
Pure Error
Total

t

DF

Stepwise Regression

F-to-Enter:
Response is

Step
Constant

x3 -
T-Ratio

x2
T-Ratio

x1
T-Ratio

x5
T-Ratio

x9
T-Ratio

S
R-Sq

WAI
1
5.899
0.347
-2.98
0.465
24.11

4.

Seq SS
6.3914
.8343
.9116
.6456
.5843
.9073
.6770
.9757

\JOOHOOIF

00 F-to-Remove:

on 9 predictors,

2 3
5.899 5.899

-0.347 —0.347

-3.49 -3.99
0.335 0.335
3.38 3.86
0.264
3.03

0.397 0.348
46.68 60.61

Adj ss Adj MS
6.3914 0.71016
4.8343 1.61142
0.9116 0.30386
0.6456 0.21520
1.5843 0.07922
0.9073 0.30245
0.6770 0.03982
.00
with N = 30
4 5
6.083 6.083
-0.347 -0.347
—4.63 —5.33
0.335 0.335
4.48 5.15
0.264 0.264
3.52 4.05
-0.346 —0.346
—3.16 —3.63
0.278
3.02
0.300 0.260
71.84 79.59

0000

O

P

.000
.000
.026
.072

.002

95

Analysis at variance for "31

Source DF Seq ss Adj ss Adj MS F P
Regression 9 521.899 521.899 57.989 82.26 0 000
Linear 3 413.248 413.248 137.749 195.42 0.000
Square 3 75.468 75.468 25.156 35.69 0 000
Interaction 3 33.183 33.183 11.061 15.69 0.000
Residual Error 20 14.098 14.098 0.705
Lack-of-Fit 3 2.056 2.056 0.685 0.97 0.431
Pure Error 17 12.042 12.042 0.708
Total 29 535.997

Stepwise Regression

F-to-Enter: 4.00 F-to-Remove: 4.00
Response is WSI on 9 predictors, with N = 30

Step 1 2 3 4 5 6 7
Constant 8.869 8.869 8.869 7.580 6.526 6.526 6.526
x2 3.33 3.33 3.33 3.33 3.33 3.33 3.33
T—Ratio 3.72 4.48 6.12 7.48 8.88 11.29 12.41
x1 -2.75 -2.75 —2.75 -2.75 -2.75 -2.75
T-Ratio -3.70 -5.06 —6.18 -7.34 -9.33 -10.25
x3 2.69 2.69 2.69 2.69 2.69
T-Ratio 4.94 6.04 7.17 9.12 10.02
x5 2.42 2.55 2.55 2.55
T—Ratio 3.71 4.64 5.90 6.48
x4 1.84 1.84 1.84
T-Ratio 3.36 4.27 4.69
x8 -1.66 —1.66
T-Ratio —3.97 -4.37
x9 0.91
T-Ratio 2.40
S 3.58 2.97 2.17 1.78 1.50 1.18 1.07
R-Sq 33.04 55.58 77.10 85.24 89.95 94.04 95.28

Analysis of variance for PIC

Source

Regression
Linear
Square

Interaction
Residual Error
Lack-of—Fit

Pure Error

Total

DF
9
3
3
3

20
3

17

29

Stepwise Regression

F-to-Enter:
Response is

Step
Constant

x1
T—Ratio

x2
T—Ratio

x3
T-Ratio

x4
T—Ratio

x6
T—Ratio

x7
T-Ratio

S
R-Sq

PMC
1
9.357
3.236
12.36
1.05
84.50

4.

Seq SS

19

6.

187

19

00
on

2
9.357

3.236
14.94

-0.809
-3.73

0.866
89.78

7
1
1
0
0
8

511

.368
.850
.293
.743
.951
.792
.254

3
9.357

3.236
20.00

-0.809
-5.00

-0.766
-4.73

0.647
94.51

Adj SS
.511
.368
7.850
.293

I951
.792

F-to-Remove:

9 predictors, with N

4
8.928

3.236
26.31

-0.809
-6.58

-0.766
-6.23

0.80
4.47

0.492
96.95

Adj MS
21.8346
62.4561
.6166
.4310
.0871
.3170
.0466

OOOON

.00

= 30

5
8.565

3.236
36.23

—0.809
-9.05

-0.766
—8.57

0.85
6.49

0.63
4.84

0.357
98.46

250.
716.
30.

8.5

3.2
43.

-0.8
-10.

—0.7
-10

0
7

0.
5

-0
-3

0.2
98.

F
56

03
.95

.80

6
65

36
30

09
82

66

.24

.85
.75

63

.78

.35
.36

99
96

O 0000

P

.000

:000
.010

.003

Analysis of Variance for %T

Source

Regression
Linear
Square

Interaction
Residual Error
Lack-of-Fit

Pure Error

Total

DF

Stepwise Regression

F-to-Enter:
Response is

Step
Constant

x2
T-Ratio

%T

1
36.61

-8.08
-4.24

7.63
39.06

4.00

on

36.

6.
63.

Seq

2609.
2204.
195.
209.
67.
66.
1.
2676.

97

F-to-Remove:

9 predictors, with N

2
61

.08
.34

.34
.19

05
08

36.

4.
82.

3
61

.08
.58

.34
.95

.68
.33

26
35

34

3.
88.

4

.20

.08
.03

.34

58
04

Adj MS
289.934
734.766

65.037

69.998

3.371

22.120

0.063

30

34.20

-8.08
-11.90

-6.34
-9.34

-5.68
~8.36

4.52
4.54
4.23
4.41

2.72

93.38

34.20

—8.08
-l4.75

-6.34
-11.57

-5.68
-10.36

4.52
5.63

4.23
5.46

2.89
3.72

2.19
95.87

P
.000
.000
.000
.000

O 0000

.000

32.83

-8.08
-18.44

-6.34
-14.46

-5.68
-12.96

DUN IbN Chub \lrb
as N
0" \O

to
HP
.p.
\1

Analysis of Variance tor DT

Source DF Seq

Regression 9 4963
Linear 3 4923.
Square 3 16.
Interaction 3 23

Residual Error 20 11.
Lack-of-Fit 3 8.
Pure Error 17 2

Total 29 4974

Stepwise Regression

F-to-Enter: 4.00
Response is DT on
Step 1 2

Constant 165.8 165.8

x2 14.76 14.76

T—Ratio 8.10 11.27

x1 -6.83

T-Ratio —5.22

x3

T-Ratio

x8

T-Ratio

x6

T-Ratio

x5

T-Ratio

x9

T-Ratio

S 7.29 5.24

R-Sq 70.10 85.11

3
165.8

14.76
42.21

-6.83
-19.53

6.57
18.78

1.40
98.98

98

F-to-Remove:

9 predictors, with N

4
165.8

14.76
52.60

~6.83
-24.34

6.57
23.40

-1
-3

.56
.92

1.12
99.37

Adj MS
551.52
1641.27
.51
.76
.55
.86
.15

ONOQUT

.00

= 30

5
166.4

14.76
62.32

-6.83
-28.83

6.57
27.72

-1.56
-4.64

-1.15
-3.33

0.948
99.57

F
997.12
3E+03
9.96
14.03

19.54

165.9

14.76
73.14

-6.83
-33.84

32.53

—1.
-5.

56
45

.09
-3.67
.94
.17

WC

0.807
99.70

P
0.000
0.000
0.000
0.000

0.000

165.9

14.76
82.

-6
-38

.83
.39
.91

56
19

-1.
—6.

.09
-4.16

-0.69
-2.76

0.712
99.78

Analysis of Variance for DP

Source

Regression

Linear
Square

Interaction
Residual Error
Lack—of-Fit

Pure Error

Total

DF

Stepwise Regression

F-to-Enter:

Response is

Step
Constant

x3
T-Ratio

x2
T-Ratio

x1
T-Ratio

x8
T-Ratio

x9
T-Ratio

x6
T-Ratio

x7
T-Ratio

S
R-Sq

DP

1
3900

-2021
-5.49

1472
51.85

4.

Seq SS
123446256
107706816
3056308
12683130
2600552
2222800

377751

1260468088

99

Adj SS
123446256
107706816
3056308
12683130
2600552
2222800

377751

F-to-Remove:

on
2 3
3900 3900
-2021 -2021
-7.07 -9.63
-1259 -1259
-4.40 -6.00
~1030
—4.91
1144 840
71.98 85.45

9 predictors, with N

4
3900

-2021
-12.15

-1259
-7.57

-1030
-6.19

953
4.05

666
91.22

Adj MS
13716251
35902272

1018769
4227710
130028
740933
22221

.00

= 30

3900

-2021
-13.83

-1259
-8.62

~1030
-7.05

953
4.61

599
2.90

585
93.49

F
105.49
276.11

7.84
32.51

33.34

3574

-2021
-16.65

-1259
-10.38

-1030
-8.49

953
5.55

599
3.49

610
3.44

485
95.70

P5
.000
.000
.001
.000

0000

O

.000

3574

-2021
-22.37

-1259
-13.94

~1030
-11.40

953
7.46

599
4.69

610
4.61

564
4.41

361
97.72

100

Analysis of variancs for sun

Source DP Seq SS
Regression 9 928391
Linear 3 912546
Square 3 9514
Interaction 3 6332
Residual Error 20 1394
Lack-of-Fit 3 1199
Pure Error 17 195
Total 29 929786

Stepwise Regression

F-to-Enter: 4.00 F—to—Remove:
Response is SME on

Step 1 2 3
Constant 493.8 493.8 493.8
x3 183.1 183.1 183.1
T-Ratio 6.18 9.15 28.44
x1 -117.4 —117.4
T-Ratio -5.86 -18.24
x2 -98.7
T-Ratio -15.33
x5
T-Ratio
x8
T—Ratio
x6
T-Ratio
x4
T—Ratio
S 119 80.1 25.7
R-Sq 57.67 81.38 98.15

Adj SS
928391
912546
9514
6332
1394
1199
195

9 predictors, with N

4
480.3

183.1
32.83

-117.4
~21.05

“98.7
—17.70

25.4
3.11

22.3
98.66

Adj MS
103155
304182
3171
2111
70

400

11

.00

= 30

5
480.3

183.1
41.02

-117.4
-26.30

-98.7
-22.11

25.4
3.88

—24.5
—3.88

17.9
99.18

F
1E+03
4E+03
45.49
30.28

34.82

6
468.6

183.1
52.23

-117.4
-33.49

-98.7
-28.16

26.8
5.21

-24.5
-4.94

20.5
3.99

14.0
99.51

P
.000
.000
.000
.000

O 0000

.000

7
459.5

183.1
63.38

-117.4
-40.65

~98.7
-34.17

27.9
6.57

-24.5
-5.99

21.6
5.09

14.7
3.45

11.6
99.68

101

Analysis of variancs for 'L'

Source
Regression
Linear
Square

Interaction
Residual Error
Lack-of-Fit

Pure Error
Total

DF
9
3
3
3

20
3

17

29

Stepwise Regression

F-to-Enter:
Response is

Step
Constant

x1
T-Ratio

x3
T-Ratio

S
R-Sq

L

39.

-5.62
-9.87

2.28
.68

4.

Analysis of Variancs

Source
Regression
Linear
Square
Interactio
Residual Err
Lack-of—Fi
Pure Error
Total

n
or
t

DF

Stepwise Regression

F-to-Enter:
Response is

Step
Constant

x1
T-Ratio

x8
T—Ratio

R-Sq

a

1
3.695
0.306
3.71
0.330
32.93

4

00

on

2
.94

.62
.46

1.89
4.20

.80
.49

for '

.00

on

2
3.695

0.306
4.11

-O.29
-2.73

0.298
47.44

F-to-Remove:

4.00

9 predictors, with N =

‘1

Seq SS
.5025
.5884
.1200
.7941
.0543
.1322
.9221
.5567

DOHNOOHN

F—to-Remove:

9 predictors,

Adj SS
.5025
.5884
.1200
.7941
.0543
.1322
.9221

OHNOOHN

4.00

with N =

0000000

Adj MS
.27805
.52948
.03999
.26469
.10271
.37740
.05424

Ch

NOU‘IN

F

.87
.73

.25
.41

O 0000

0000

O

P

.000
.000
.218
.317

.275

P

.031
.008
.762
.082

.003

102

Analysis of Variancs for 'b'

Source
Regression
Linear
Square
Interaction
Residual Error
Lack-of-Fit
Pure Error
Total

DF
9
3
3
3

20
3

17

29

Stepwise Regression

F-to-Enter:
Response is b
Step 1
Constant 10.59
x1 -0.859
T-Ratio -7.93
x3
T—Ratio
S 0.434
R-Sq 69.18

4.

1
1

1

00
on

2
10.59

-0.859
-9.88

0.353
4.06

0.348
80.87

Seq SS Adj SS
4.4812 14.4812
3.8216 13.8216
0.1428 0.1428
0.5169 0.5169
2.5984 2.5984
0.5534 0.5534
2.0450 2.0450
7.0797

F-to—Remove:

9 predictors,

4.00

with N =

OOOOOBH

Adj MS
.60903
.60719
.04760
.17229
.12992
.18448
.12029

.33

F

.38
.46

.53

O 0000

P

.000

:778
.294

.242

APPENDIX C

MOTOR NAMEPLATE DATA FOR ZSK-30 EXTRUDER

Model: GB DC 300

Hp: 15

Rpm: 2500

Max Torque: 90 NM per output shaft
Overall Gear Ratio: 5 : 1

Screw Rpm: 500

103

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105

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