kinetics of starch and protein digestion in cereals and...

JING HOOI TAN s4198650

1

Kinetics of starch and protein

digestion in cereals and pulses with

special reference to field peas

Final Report

By

TAN JING HOOI1

Supervisor - DR PETER A SOPADE

1Centre for Nutrition and Food Sciences

Queensland Alliance for Agriculture and Food Innovation

The University of Queensland

St Lucia 4072


2

Declaration of Authorship

I, TAN JING HOOI confirm that the work presented in this research report has been

performed and interpreted solely by myself except where explicitly identified to the contrary.

I confirm that this work is submitted in partial fulfilment for the degree of BSc Hons in

Biomedical Science and has not been submitted elsewhere in any other form for the

fulfilment of any other degree or qualification.

Dated: 24 October 2012

Signature:

I, Dr Peter SOPADE, confirm that I have seen a copy of the work presented in this research

proposal/research report as the supervisor of Mr Jing Hooi Tan.

Date: 24 October 2012

Signature:


3

Table of Contents

Declaration of Authorship ....................................................................................................................... 2

List of abbreviations ................................................................................................................................ 5

Abstract ................................................................................................................................................... 6

Acknowledgements ................................................................................................................................. 7

CHAPTER 1 Introduction .................................................................................................................... 8

CHAPTER 2 Materials and methods ................................................................................................. 14

2.1 Cereals and pulses................................................................................................................. 14

2.2 Physical properties of grains ................................................................................................. 14

2.3 Particle size reduction ........................................................................................................... 15

2.4 Particle size analysis .............................................................................................................. 16

2.5 Moisture content .................................................................................................................. 16

2.6 Ash Content ........................................................................................................................... 16

2.7 Fat Content ........................................................................................................................... 17

2.8 Total starch analysis .............................................................................................................. 17

2.9 In-vitro starch digestion ........................................................................................................ 17

2.10 In-vitro protein digestion ...................................................................................................... 20

2.11 Statistical analysis ................................................................................................................. 21

CHAPTER 3 Results and Discussions................................................................................................. 22

3.1 Physical properties of wheat, barley, field peas and lupins ................................................. 22

3.2 Chemical properties of wheat, barley, lupins and field peas ................................................ 24

3.3 Particle size distribution ........................................................................................................ 25

3.4 Starch digestion kinetics ....................................................................................................... 28

3.5 Protein digestion kinetics ...................................................................................................... 41

CHAPTER 4 Conclusions ................................................................................................................... 57

CHAPTER 5 Further research needs and recommendations ........................................................... 58

References ............................................................................................................................................ 59

Appendices ............................................................................................................................................ 62

Appendix A: Cryo-milling condition and particle size distribution of the samples. .......................... 62

Appendix B: Hammer-milling condition and particle size distribution of the samples. .................... 64

Appendix C: Parameter of the first order kinetic model for starch digestion .................................. 66

Appendix D: Parameter of the first order kinetic model for starch digestion .................................. 67

Appendix E: The parameters of the modified first order kinetic model for protein digestion in cryo-

milled field peas ................................................................................................................................ 68


4

Appendix F: The parameters of the modified first order kinetic model for protein digestion in

hammer-milled field peas ................................................................................................................. 70


5

List of abbreviations

Do Digested starch at time t = 0 (g/100 g dry starch)

Dt Digested starch at time t (g/100 g dry starch)

D∞ Digested starch at infinite time (g/100 g dry starch)

IVPD In-vitro protein digestibility (g/100 g dry protein)

K Rate of constant (min-1

); subscript ST is for starch and PR is for protein

pHo pH of protein digesta at time t = 0

pHt pH of protein digesta at time t

pH∞ pH of protein digesta at infinite time

t Time (min.)

Af Accuracy factor

Diff Diffusion coefficient (cm2 s

-1)

r2 Coefficient of determination (r = correlation coefficient)

p Average particle size (cm)

SUMSQ Sum of squares of residuals

MRDM Mean relative deviation modulus


6

Abstract

Field peas (Pisum sativum), wheat (Triticum spp.), barley (Hordeum vulgare L.) and lupins

(Lupinus L.) were cryo- and hammer-milled at different mill settings, and the effects of

particle size (45 – 630 µm) on the kinetics of starch and protein digestion were investigated

by using in-vitro procedures. Starch digestion was studied using a glucometry method with

amylase enzymes, while protein digestion was studied using a modified pH drop

multienzyme (trypsin, chymotrypsin and protease) procedure. Irrespective of the particle size,

the grain samples exhibited essentially monophasic patterns for both starch and protein

digestion. A modified kinetic model suitably described the starch (r2 > 0.991; p < 0.001) and

protein (r2 > 0.991; p < 0.001) digestograms. Starch and protein digestion in milled grains

were found to be higher with hammer- than with cryo-milling. Presumably, the quench-

freezing effect in cryo-milling affected the starch and protein structures less than the

mechanical and frictional heat effects in hammer-milling. Also, the reciprocal of the rate of

starch and protein digestion of the cryo- and hammer-milled grains were related (p < 0.05)

related to the square of the particle size to suggest that starch and protein digestion in the

milled grains proceeded by diffusion, irrespective of the mills and their settings. Protein

digestion proceeded at a much faster (33 X) rate than starch digestion in the milled grains

with implications for nutrient asynchrony. Being the first study on the dependence of starch

and protein digestion on grain particle size in field peas, wheat, barley, and lupins, the study

concluded that particle size and type of mill affected starch and protein digestion. The results

have implications for feed processing, where field pea, wheat, barley, and lupins are notable

ingredients, and mills such as hammer, disc and roller mills are used.


7

Acknowledgements

First and foremost, I would like to thank Dr. Peter A Sopade from the School of Agriculture

and Food Sciences, and Centre for Nutrition and Food Sciences, the University of

Queensland, for his endless effort, time and dedication of supervising, and leading me to

accomplish this fine work. His patience, moral support, encouragement, advice and guidance

throughout this research project are much appreciated. He is always accessible and willing to

help me with my research. The weekly meetings and long discussions were crucial to keep

me on track and keep me motivated. I sincerely appreciate him for being such a wonderful

supervisor.

Many thanks to all members of CNAFS (Centre for Nutrition and Food Science) and all

graduate, postgraduate students and friends for sharing their knowledge and experience. It has

been such an unforgettable experience as we exchanged knowledge and ideas as well as

shared our cultural differences. Besides that, I would like to thank Dr. Lesleigh Force and

Dr Honest Madziva for the provision of laboratory inductions as well as all the available

instruments and chemicals, enabling all the experiments to be done in a timely manner.

Also, I would like to express my gratitude to the Pork CRC, represented by Dr. John Black

and Dr. Roger Campbell, for their generous financial and scientific support for this research

project. In addition, special thanks to QAAFI, represented by Professor Robert Henry for

the generous financial and scientific support for this research project.

Finally, my deepest gratitude goes to my family for their unflagging love and encouragement

throughout my life. This research is simply impossible without their constant support. I am

also thankful for all the caring friends who have helped and inspired me, besides supporting

me during the stressful moments in completing this research project.


8

CHAPTER 1 Introduction

“We are what we eat” is a common proverb used in the olden days. Food indeed

plays an essential role in determining our health, physical and mental faculties in our

everyday life (Mahasukhonthachat et al., 2010). There are various nutrient elements which

can be found in food, such as protein, carbohydrates, fats, minerals etc. In order to maximise

the utilization of nutrients in daily food consumption, these nutrients needed to be fully

absorbed and utilised by human or animal bodies through a system called, digestive system

(Byrd-Bredbenner et al., 2009). Food sources such as cereal grains and legumes or commonly

known as pulses are used as an important source of dietary carbohydrates and proteins

(Whitney and Rolfes, 2011). In Australia, cereal grains such as wheat (Triticum spp.) and

barley (Hordeum vulgane L.) are named among the “big three” cereal crops besides sorghum

(Duodu et al., 2003). On the other hand, there are six major pulse groups grown in Australia,

such as Faba bean (Vicia faba), Chickpea (Cicer arietinum), Lentil pea (Lens culinaris),

Lupins (Lupinus L.), Field pea (Pisum sativum) and Vetch (Vicia spp.) (Siddique and Sykes,

1997). Both cereals and pulses contribute to the main energy yielding components of food

and feed (Lawrance, 2007).

In the past, interests in starch and protein digestion within the gastrointestinal tract

have increased dramatically to include ways to increase the efficiency of starch and protein

utilisation (Gidley et al., 2010, Mahasukhonthachat et al., 2010). There are many possible

factors that are capable of resisting digestibility of starch and protein in the gastrointestinal

tract, such as particle size distribution, feed particle form, the presence of anti-nutritional

substances, heat treatments, and generally processing methods (Laurinen et al., 2000).

Amongst these factors, the effect of particle size distribution is the primary factor in affecting

the energy utilization properties. This can be explained that the rate of enzymatic digestion of


9

starch and protein in the small intestine will determine the availability of energy for growth

from the consumption of processed feeds or food (Weurding et al., 2001). The longer it takes,

the more are the non-digestible components that enter the large intestine, where bacterial

fermentation, rather than enzymatic digestion, predominates ileal digestibility is recognized

as a key marker of growth, while the succeeding large intestine bacterial fermentation

enhances gut health (Goñi et al., 1997, Mahasukhonthachat et al., 2010, Weurding et al.,

2001). Both are valuable for humans, while for animal, maximum energy delivery from feed

is largely determined by ileal digestion (Gidley et al., 2010). Therefore, understanding starch

and protein digestion will benefit from knowledge of the mechanisms of these digestion

processes, as influenced by unit operations and processes during food and feed manufacture.

Milling is a common unit operation, in which particle size is reduced by increasing

the surface area per unit volume (Amerah et al., 2007). Also, it is capable of disrupting

internal structures and molecular orderliness as well as material fracturability. Studies in the

past have revealed that the molecular orderliness in potato and maize starches were affected

by milling differently, which lead to an increase in damaged starch, decrease in starch

crystallinity and decrease in functional properties (Dhital et al., 2010, Huang et al., 2008).

The various types of mills in food and feed processing include hammer-, disc-, cryo-, ball-

and roller-mills, and these differ in the effective operating force and the extent of frictional

heat generation during the milling operation (Bond, 1963). Milling will cause the exposure of

the internal components of the materials and enhance the enzyme-substrate interactions,

which will affect the digestion of the components. In order to investigate the starch and

protein digestion in cereals and pulses, these grains are usually milled into different particle

sizes for analysing the dependence of the two digestion processes on particle size.

Starch and protein digestion, specifically can be studied using in-vitro and in-vivo

procedures, but the in-vivo procedures are less preferable because of time, cost and ethical


10

issues. Most studies have investigated starch and protein digestion based on single-point

measurement rather than time-course measurement. With single-point measurement, data on

digestion profile to understand kinetics of digestion are not available (Singh et al., 2010).

However, the data on digestion profile to understand the kinetics digestion of starch

dependence on processing factor such as particle size distribution can be accessible based on

time-course measurement. There are various in-vitro digestion procedures, and for starch

digestion, the rapid procedure of Sopade and Gidley (2009) that uses a glucometer to measure

glucose at set times has been shown to be versatile and applicable to all food and feed

materials. The starch digestograms obtained from this procedure have been modelled using a

modified first order kinetic model (Eqn. [1.1]). Sopade and co-workers have revealed that the

rate of starch digestion in sorghum, sweet potato, potato, and cowpea is inversely dependent

on the particle size. Furthermore, starch digestion has been revealed to proceed by diffusion

mechanism. There are many others materials (e.g. wheat and field peas) that are used in food

and feed, whose starch digestion behaviours have not been studied. Studying these and

understanding their digestion behaviours would advance knowledge on digestion of food and

feed materials to engineer appropriate diets for humans and animals.

Starch digestion:

Dt = D0 + D∞-0 (1 - exp (- KST t), (1.1)

D = D0 + D∞-0

where D0 = digested starch at time t = 0 (g/100g dry starch), Dt = digested starch at time t

(g/100g dry starch), D = digested starch at infinite time (g/100g dry starch), k = rate

constant (min-1

).


11

On the other hand, studies on the kinetics of in-vitro protein digestion are

comparatively limited as compared to studies on the kinetics of in-vitro starch digestion.

There are various in-vitro protein digestion procedures, which include single protease, such

as pepsin or trypsin, or based on multi-proteases, such as pepsin-trypsin, pepsin-pancreatin,

trypsin-chymotrypsin, trypsin-chymotrypsin-peptidase or trypsin-chymotrypsin-protease as

summarised in Table 1.1. These procedures either measure the digested protein or undigested

proteins if proteolysis using a batch or continuous approach, which based on procedures that

include amino acid analysis, chromatography and electrophoresis.

The pH drop three-enzyme method of Hsu. et al. (1977) is widely used in estimating apparent

in-vitro protein digestibility, where the drop in pH results from the release of amino acids,

protein building blocks, as protein is digested. The release of amino acids during proteolysis

is not expected to be linear or of a zero order. Hsu et al. (1977) have observed that power-law

model was the best to describe protein digestograms. However, these authors did not obtain

any significance correlation between parameters of the power-law model and in-vivo protein

digestion. Thus, Sopade and co-workers used a modified first order kinetic to investigate and

understands the kinetics of protein digestion. In terms of pH, a modified first order kinetic

model can be written as (Eqn. [1.2]):

pHt = pH0 + pH -0 (1-exp (-KPR t) (1.2)

pH = pH0 + pH -0

where pH0 = pH at time t = 0, pHt = pH at time t, pH = pH at infinite time, k = rate of

constant (min-1

).


12

Table 1.1 Different In-Vitro Protein Digestion Methods

Number Types of in-vitro protein

digestion

Enzyme/s used Sample/s used Analysis Method Reference

1 Single-protease Pepsin 200 mg grain

samples

Determine residual

nitrogen in a Technicon

analyser

Mertz et al. (1984)

Hamaker et al. (1987)

2 Single-protease Pepsin 500 mg dried

protein

Spinco model 120

amino acid

Akeson and Stahmann

(1964)

3 Single-protease Trypsin 40 ml protein

suspension

(2 mg N/ml)

pH drop against

incubation time

Maga et al. (1973)

4 Multi-protease Pepsin-pancreatin 100 mg protein Ion exchange method Akeson et al. 1964

5 Multi-protease Pepsin-pancreatin 250 mg samples Sodium dodecyl sulfate

polyacrylamide gel

electrophoresis (SDS-

PAGE)

Kim and Maga (1987)

6 Multi-protease Pepsin-trypsin 1% w/v legume

samples

High performance size

exclusion

chromatography with

multi-angle laser light

scattering (HPSEC-

MALLS)

Tang et al. (2009)

7 Multi-protease Trypsin-

chymotrypsin-

peptidase

50 ml 6.625 mg/ml

protein suspension

pH drop against

incubation time

Hsu et al. 1997

Zhang et al. (2010)


13

In the present study, the hypothesis tested was that starch and protein digestion in

milled cereal grains and pulses are dependent on particle size. Therefore, the objectives were,

to:

1) Investigate the effect of particle size distribution on starch and protein digestions of

cereals and pulses.

2) Examine the kinetics of protein and starch digestion of cereals and pulses.

3) Model protein and starch digestograms.

4) Obtain digestion parameters to examine the relative digestibility of starch and protein.

5) Study the relationship between digestion and particle size parameters.


14

CHAPTER 2 Materials and methods

2.1 Cereals and pulses

Wheat and barley, as well as pulses such field peas and lupins were obtained from the

project collaborators, local supplier and Cooperative Research Centre for High Integrity

Australian Pork (Pork CRC) in Australia. The grains were chosen because of their nutritional

importance in food and feed. These grains were cleaned and stored in a freezer for further

analysis.

2.2 Physical properties of grains

The physical properties, such as major diameter, minor diameter and thickness of all

cereals and pulses were measured using Vernier callipers and the 1000-grain weight of these

grains were determined as well. The analysis of the physical properties for all samples were

randomised and replicated. Grains are generally irregular in shapes, but certain regular bodies

can be used to approximate their appearances (Sopade and Okonmah, 1993). The regular

bodies that can be used to represent the grains (wheat, field peas, barley and lupins), and their

respective volumes are shown in Table 2.1.

Table 2.1 Approximate regular bodies for grains and the formulas for their volumes

Grain Assumed regular shape Volume formula

Field peas Spherical

Lupins Cuboid a b c

Barley Ellipsoidal

Wheat Ellipsoidal

*a = major radius, b= minor radius and c= half thickness


15

2.3 Particle size reduction

The grains were hammer-milled (MFC type DCFH48, John Morris Scientific Pty. Ltd.,

Eagle Farm QLD 4009, Australia) and cryo-milled (6850 SPEX Freezer/Mill, SPEX,

Metuchen, NJ 08840, USA) using 10 different settings of retention sieves and number of

passes in the hammer mill, and grinding times or cycles in the cryo-mill as shown in Figure

2.1. The cryo-mill was operated at fixed pre-cooling (5 min.) and resting times (2 min.) an

im actor stain ess stee iameter mm ength 70 mm) speed (10 s-1

). For retention sieves

of 1.0 and 0.5 mm in the hammer-mill, the samples were initially ground using 2-mm sieve.

The milling conditions (time, cycle and sieve) were varied to obtain different particle size

distributions and particle sizes. The grindings were randomised and replicated.

Figure 2.1. Preparation of samples grains


16

2.4 Particle size analysis

Particle size analysis was carried out using a Malvern Mastersizer Hydro 2000MU

(Malvern Instruments Ltd, Malvern WR14 1XZ, UK) as described by (Mahasukhonthachat et

al., 2010). The volume weighted mean (d[4,3]) was used as the average particle size.

2.5 Moisture content

his was etermine in an o en at or hr bo t g o the sam es were

weighe into reheate C) and cooled moisture dishes. The samples were thereafter

dried, and the change in weight was used to calculate the moisture content (MC) of the

samples (Eqn. [2.1]):

where wt = weight (g).

2.6 Ash Content

This was determine in an o en at or hr bo t g o the sam es were

weighe into reheate C) and cooled crucible dishes. The samples were thereafter dried,

and the change in weight was used to calculate the ash content (AC) of the samples (Eqn.

[2.2]):

where wt = weight (g).


17

2.7 Fat Content

The Soxhlet method was used with petroleum spirit as the solvent. Samples were

weighed into thimbles, covered with cotton wool and the fat was extracted in a Soxhlet

apparatus. Upon completion of extracted, the solvent was evaporated and the extracted fat

was weighed, and related to the weight of the sample. The fat content (FC) of the samples

was calculated using (Eqn. [2.3]):

where wt = weigh (g).

2.8 Total starch analysis

The total starch content of the samples was analysed by using a method derived from

Megazyme (Megazyme International Ireland Ltd, Wicklow, Ireland) based on dimethyl

s hoxi e DMSO) thermostab e α-amylase and amyloglucosidase (Fig. 2.2).

2.9 In-vitro starch digestion

In-vitro starch digestion was assayed using the procedure (Fig. 2.3) described by

Sopade and Gidley (2009), whereby known weight of samples was treated with artificial

saliva containing porcine α-am ase Sigma - ) an e sin Sigma - ) at ow

inc bate at C for 30 min (salivary-gastric digestion) before neutralisation with sodium

hydroxide. A sodium acetate buffer was added to the digesta, which was then treated with

pancreatin Sigma ) an am og cosi ase Sigma - ) an inc bate at C for

240 min. (pancreatin digestion). Accu-Check ® Performa ® glucometer (Roche Diagnostics

Aust. Pty. Ltd., Caste Hill NSW 2154, Australia) was used to measure the glucose

concentration and converted to digested starch.


18

Figure 2.2. The steps required in the total starch analysis.

Sample

Mix, grind, weigh (50 mg)

Wet with 0.4 mL 80% ethanol

Prepare 5% and

20% starch

standards (50

mg)

Prepare reagent blank

Add 2 mL Dimethyl Sulphoxide (DMSO)

Incubate in boiling water for 5 min.

Add 3 mL thermostable α-amylase (1 mL) in MOPS buffer (30 mL)

For 12 min.

(stir every 3

min. interval)

ace in C water bath Add 4 mL sodium acetate buffer

Equilibrate (2 min.) Add 0.1 mL amyloglucosidase

nc bate at C water bath for 30 min. (stir on vortex every 10 min. interval)

Centrifuge at 2000 x g for 10 min.

Transfer aliquot (50 µL), add glucose reagent (1 mL)

nc bate in the ar C for 18 min.)

Read absorbance at 505 nm Calculate percentage of starch


19

Figure 2.3. The steps in the in-vitro starch digestion procedure

Mix, grind, weight (500 mg)

Add 1mL artificial saliva (α-amylase)

Add 5mL pepsin in 0.02M HCI within 15-20 sec after saliva

Incubate at 37°C in a reciprocating water bath for 30 min.

Neutralize with 5mL of 0.02M NaOH

Add 25mL acetate buffer, and 5mL pancreatin/amyloglucosidase in acetate buffer

Incubate at 37°C in a reciprocating water bath

Record glucose concentration with Glucometer at (min.)

0 10 20 30 45 65 90 120 150 180 210 240

Sample


20

2.10 In-vitro protein digestion

In-vitro protein digestion was assayed using the modified pH-drop procedure as

summarized in Fig. 2.4. Milled sample was weighed by using the formulae, (W= (62.5/p) ×

100 mg, p the percentage of proteins) and rehydrated in 10 mL of milli-Q water at C for 1

hr, after which the pH was adjusted to about 8.0 with 0.1M NaOH and/or HCl. 10 mL of a

multi-enzyme solution consisting of (T0303 Trypsin from porcine pancreas Type IX-S,

lyophilized powder, 13,000-20,000 BAEE units/mg protein), 31 mg of chymotrypsin (C4129

α-Chymotrypsin from bovine pancreas C4129 type II, lypholized powder, 40 units/mg

protein) and 13 mg protease (P5147) Protease from Streptomyces griseus Type XIV,

units/mg solids). Protease was used to replace the discontinued peptidase (Sigma P7500

Peptidase from porcine intestinal mucosal, 50-100 units/g solids). The multi-enzyme solution

was resh re are on the a o ana sis an e t at C, where the pH was adjusted to

about 8.0 prior to being added to the rehydrated sample dispersion. The pH of the digesta was

recorded every 5 sec. for 15 min. exactly using a programmable pH meter. The change in pH

at min o igestion Δ 10 min.) was used to calculate (Eqn. [2.4]):

V D = + Δ 10 min (2.4)


21

Figure 2.4. Procedure of modified pH drop method

2.11 Statistical analysis

Eqn. (1.1) and (1.2) were used to model the starch and protein digestograms of the

milled grains with digestion time expressed in min. For the starch digestograms, the

constraints (D∞ ≤ 100 g per 100 g dry starch; Do ≥ g er g r starch) were i e

For protein digestograms, the constraints were pHo ≤ an ∞ > 0 were satisfied. The

suitability of the models was assessed by using mean relative deviation modulus (MRDM),

sum of squares, and coefficient of determination (r2). The tests of the significance of in-vitro

starch and in-vitro protein digestion were analysed by a randomised experimental design

from MinitabTM

ver. 16.

Dissolve sample (6.25 mg protein/ mL) in 10 mL distilled water

eh rate at C for 1h with intermittent stirring

t sam es into water bath C with pH adjust to 8, using HCI/NaOH

Add 10 mL of multi-enzyme solutions (1.6 mg Trypsin; 3.1 mg

chymotrypsin; 1.3 mg protease per mL) into sample (adjust pH to 8.0)

Record pH changes at 5 sec for 15 min

% Protein Digestibility = + Δ


22

CHAPTER 3 Results and Discussions

3.1 Physical properties of wheat, barley, field peas and lupins

Different types of wheat, barley, pea and lupin grains were studied, and examining

their physical characteristics would enable differences in their dimensions to be compared.

Within a grain type, field peas in this context, there are differences in terms of major, minor

diameters, thickness (mm) and 1000-grain weight (g) as shown in Table 3.1. For example,

MAKI AGT Increase (field peas 1), its major and minor diameter, and thickness were

respectively 6.7, 6.3 and 6.0 (mm), while its 1000-grain weight was 187.2 g. On the other

hand, CRC Walana AGT Increase (field peas 2), its major and minor diameter, and thickness

were respectively 7.4, 6.7, 6.5, while its 1000-grain weight was 242.5 g.

The regular bodies that can be used to represent the grains (wheat, pea, barley and

lupin), and their respective volumes are shown in Table 2.1. With the given mass, g (1000-

grain weight) in Table 3.1, and volume formula in Table 2.1, density, p of the grains can be

calculated (Eqn. [3.1]):

p (kg/m3) = mass (kg) / volume (m

3) (3.1)

The density of all the grains was tabulated in Table 3.1 and shown in Figure 3.1. The density

of all the grains can be compared within the grain types and among the grain types. Among

the grain types, Gunyidi lupins (12.4 kg/m3) are shown to be denser as compared to pea 1 (3.5

kg/m3). Within the grain types, field peas 4 (4.6 kg/m

3) is shown to be denser as compared to

field peas 1 (3.5 kg/m3).


23

Table 3.1. Selected physical characteristics of whole grains

*Values with the same letters are non-significant (p > 0.05)

Sample

Sample

code

Major diameter

(mm)

Minor diameter

(mm)

Thickness

(mm)

1000-grain weight

(g)

Density

(kg/m3)

Gunyidi Lupins GL 7.4d 6.1cde 5.1d 141.6f 664.6a

Coromup Lupins CL 8.3bc 6.6ab 5.5c 192.4c 636.2a

Mandelup Lupins ML 7.9cd 6.4abc 5.4cd 168.9de 623.3a

Hard Wheat 1895 HW 2.8g 2.6h 2.5h 34.6h 446.1b

Soft Wheat 1894 SW 3.6f 3.1g 2.9fg 48.6g 360.1b

Sprouted Wheat 1896 SPW 3.2fg 2.9gh 2.6gh 36.4h 405.7b

MAKI AGT Increase (Field peas 1) FP 1 6.7e 6.3bcd 6.0b 187.2c 14.2c

CRC Walana AGT Increase (Field peas 2) FP 2 7.4d 6.7a 6.5a 242.5a 13.7c

Parafield P3WNA09 (Field peas 4) FP 4 6.5e 5.8e 5.5c 203.5b 18.0c

MAKI S409 Phenology (Field peas 7) FP 7 6.6e 6.2bcde 6.0b 174.1d 13.8c

Parafield S409T1 PBIN (Field peas 12) FP 12 6.6e 5.9e 5.5c 159.4e 12.8c

Kaspa S410 PBIN T2 (Field peas 17) FP 17 6.6e 5.9de 5.5c 149.0f 11.6c

Barley 3764 B 37 9.0a 4.0f 3.9e 51.5g 90.1c

Barley 3933 B 39 8.5b 3.7f 3.3f 37.7h 93.9c

Barley 3873 B 38 8.6ab 3.2g 2.1i 31.3h 109.2c


24

Figure 3.1. Grains are varied in terms of density

3.2 Chemical properties of wheat, barley, lupins and field peas

Besides investigating the physical characteristics of wheat, barley, lupins and field

peas, their chemical properties, such as moisture, fat, ash, starch and protein contents are

equally important to be investigated. Table 3.2 shows that different types of grains have

different chemical properties. It is important to note that any of the lupin species has virtually

no starch (Nalle et al., 2011). This is in marked contrast to grains such as field peas, barley

and wheat. Lupin species are relatively rich in proteins as opposed to the three other grain

species, which are rich in starch (Nalle et al., 2011).

Types of grains

Den

sity

, p

(kg/m

3)

560

580

600

620

640

660

680

700

Gunyidi Lupins Coromup Lupins Mandelup Lupins

0

50

100

150

200

250

300

350

400

450

500

Hard Wheat Soft Wheat Sprouted Wheat

0

2

4

6

8

10

12

14

16

18

20

Fieldpeas 1 Fieldpeas 2 Fieldpeas 4 Fieldpeas 7 Fieldpeas12

Fieldpeas17

0

20

40

60

80

100

120

140

Barley 3764 Barley 3933 Barley 3873


25

Table 3.2. Selected chemical properties of grains (%)

Sample

Moisture

content

Crude Fat

content Ash

Starch

content

Protein

content

Hard Wheat 11.73cde 2.47c 1.55g 52.79c 18.92f

Soft Wheat 11.04efg 2.75c 1.37gh 62.50a 11.28gh

Sprouted Wheat 12.35bcd 1.75ef 1.30h 57.47b 11.94gh

Gunyidi Lupins 11.41def 5.55b 2.47cd - 32.18a

Mandelup Lupins 9.95gh 5.47b 2.92a - 31.75a

Coromup Lupins 9.20h 6.14a 2.63bc - 31.94a

MAKI AGT Increase 12.57bcd 1.57efg 2.20ef 48.17d 19.94ef

CRC Walana AGT Increase 12.02cde 1.27fg 2.58bcd 43.26e 22.96d

Parafield P3WNA09 13.89a 1.27fg 2.60bcd 41.27e 24.94cd

MAKI S409 Phenology 12.38bcd 1.47efg 2.64bc 42.11e 22.35de

Parafield S409T1 PBIN 13.44ab 1.22g 2.79ab 37.96f 28.73b

Kaspa S410 PBIN T2 12.77abc 1.63efg 2.92a 36.72f 26.01bc

Barley 3764 10.99efg 2.35cd 2.38de 53.56c 12.20g

Barley 3873 10.23fgh 2.67c 2.02f 46.76d 18.77f

Barley 3933 11.73cde 1.95de 2.45cd 54.00c 8.97h

*Values with the same letters are non-significant (p > 0.05)

3.3 Particle size distribution

Figure 3.2 shows the typical particle size distributions of barley, field pea, soft wheat

and lupin grains. Irrespective of the milling technique and condition, all the grains exhibited

bimodal distribution with the fine modal class being less than or equal to about 100 µm,

while the coarse class is greater than 100 µm. Previous studies reported that cereals, legumes

and sweetpotato have bimodal particle size distributions (Becker et al., 2001, Kerr et al.,

2000). As expected, the milling techniques and conditions significantly (p < 0.05) affected

the particle size distribution of the grains with particle size generally increasing with

decreases in the cryo-mill cycle and the number of grinds or reduction in the size of the

retention sieve in hammer-mill.


26

Figure 3.2. Particle size distribution of different grains.

Differences in term of types of milling and extent of milling are expected to cause

high degree of damaged starch. Becker et al. (2001) have revealed differences in the protein

contents of fine and coarse fractions. In Figure 3.2, the fine samples had particle less than 100

µm. This suggests that the finer samples might contain mechanically separated starch

granules and protein bodies. With coarse sample showing more distribution between 100 µm

and 1000 µm, these would include multicellular structures containing cells with complete

encapsulating cell walls, with implications for their functional properties (Kerr et al., 2000,

Mahasukhonthachat et al., 2010). However, relatively more studies have been conducted on

the effects of milling on starch than on protein, even though starch and protein are associated.

Hence, it is expected that milling is capable of affecting protein, but the differences in

structure, chemistry and/or molecular architecture are expected to define how these

macromolecules affected, and subsequently, their various properties, including digestion

0

1

2

3

4

5

6

1 10 100 1000 10000

Barley 3764 CG 1

0

1

2

3

4

5

6

1 10 100 1000 10000

Maki AGT Increase HM 5

Sample code

0

1

2

3

4

5

6

1 10 100 1000 10000

Mandelup Lupin HM 4

Sample code

0

1

2

3

4

5

6

1 10 100 1000 10000

Soft Wheat CG 2

Sample code

Sample code

Particle size (µm)

Vo

lum

e (

%)


27

(Huang et al., 2008). In the present study, further analysis and discussion will be concentrated

on the volume weighted mean (d[4,3]), as a measure of the average particle size parameter to

be used in investigating the digestion properties of the samples. The particle size parameters

of all the field peas are tabulated in Tables 3.3 and 3.4. The particle size parameters for the

other grains (barley, wheat and lupin) are summarised in Appendices A and B.

Table 3.3. Cryo-milling condition and particle size distribution of the field peas.

Sample Sample Cryo-milling a

Particle size parameter (v/v, µm)

code Cycle [min.] d(v, 0.1)b

d(v, 0.5)b

d(v, 0.9)b

d (4, 3)b

Field peas 1 FP1CG3 1 8 63 785 263

Field peas 1 FP1CG2 2 7 31 309 101

Field peas 1 FP1CG4 3 5 25 227 73

Field peas 1 FP1CG5 4 6 25 200 65

Field peas 1 FP1CG1 6 5 23 146 51

Field peas 12 FP12CG3 1 10 60 589 204

Field peas 12 FP12CG2 2 6 33 314 104

Field peas 12 FP12CG4 3 5 28 258 85

Field peas 12 FP12CG5 4 6 28 201 70

Field peas 12 FP12CG1 6 4 24 183 61

Field peas 17 FP17CG3 1 12 146 796 293

Field peas 17 FP17CG2 2 8 43 415 144

Field peas 17 FP17CG4 3 5 26 230 79

Field peas 17 FP17CG5 4 6 28 236 76

Field peas 17 FP17CG1 6 5 26 201 68

Field peas 2 FP2CG3 1 12 102 895 279

Field peas 2 FP2CG2 2 11 45 797 246

Field peas 2 FP2CG4 3 7 32 301 100

Field peas 2 FP2CG5 4 6 26 196 65

Field peas 2 FP2CG1 6 5 25 161 55

Field peas 4 FP4CG3 1 11 96 772 271

Field peas 4 FP4CG2 2 8 38 380 128

Field peas 4 FP4CG4 3 6 32 263 86

Field peas 4 FP4CG5 4 6 29 200 69

Field peas 4 FP4CG1 6 5 25 164 59

Field peas 7 FP7CG3 1 7 39 488 162

Field peas 7 FP7CG2 2 7 33 293 96

Field peas 7 FP7CG4 3 5 26 229 75

Field peas 7 FP7CG5 4 6 28 218 72

Field peas 7 FP7CG1 6 7 29 178 63

Standard error mean, SEM 0.1 2.6 10.1 7.7 a The number of cycles with 5 min. pre-cooling, 2 min. intermediate (in-between cycles) cooling and 10 s-1 impactor speed

b 10th (d(v, 0.1), 50th (d(v, 0.5), 90th percentiles (d(v, 0.9), and volume weighted mean (d(4, 3)).


28

Table 3.4. Hammer-milling condition and particle size distribution of field peas.

Sample Sample Hammer-

milling a


code Sieve-Passa

d(v, 0.1)b

d(v, 0.5)b

d(v, 0.9)b

d (4, 3)b

Field peas 1 FP1HM 5 0.5 -1 9 30 160 71

Field peas 1 FP1HM 4 1.0 -1 12 51 804 220

Field peas 1 FP1HM 1 2.0 -1 12 57 1136 344

Field peas 1 FP1HM 2 2.0 -3 10 37 1036 283

Field peas 1 FP1HM 3 2.0 -6 10 36 811 249

Field peas 12 FP12HM 5 0.5 -1 11 35 229 82

Field peas 12 FP12HM 4 1.0 -1 13 53 568 181

Field peas 12 FP12HM 1 2.0 -1 16 161 1230 434

Field peas 12 FP12HM 2 2.0 -3 12 41 898 235

Field peas 12 FP12HM 3 2.0 -6 9 35 326 126

Field peas 17 FP17HM 5 0.5 -1 9 30 199 72

Field peas 17 FP17HM 4 1.0 -1 13 67 910 286

Field peas 17 FP17HM 1 2.0 -1 13 97 1197 399

Field peas 17 FP17HM 2 2.0 -3 12 65 1207 386

Field peas 17 FP17HM 3 2.0 -6 10 37 1051 297

Field peas 2 FP2HM 5 0.5 -1 9 34 194 76

Field peas 2 FP2HM 4 1.0 -1 13 47 604 185

Field peas 2 FP2HM 1 2.0 -1 12 59 1052 323

Field peas 2 FP2HM 2 2.0 -3 11 45 895 246

Field peas 2 FP2HM 3 2.0 -6 8 34 752 193

Field peas 4 FP4HM 5 0.5 -1 11 34 163 69

Field peas 4 FP4HM 4 1.0 -1 11 40 861 228

Field peas 4 FP4HM 1 2.0 -1 14 99 1204 403

Field peas 4 FP4HM 2 2.0 -3 12 50 1103 334

Field peas 4 FP4HM 3 2.0 -6 14 74 920 304

Field peas 7 FP7HM5 1.0 -1 10 33 120 65

Field peas 7 FP7HM4 2.0 -1 13 47 598 181

Field peas 7 FP7HM1 2.0 -3 16 73 959 294

Field peas 7 FP7HM2 2.0 -6 14 58 913 250

Field peas 7 FP7HM3 1.0 -1 13 45 835 238

Standard error mean, SEM 0.4 8.4 21.2 7.9 a The retention sieve and number of passes.


3.4 Starch digestion kinetics

Irrespective of milling types (cryo- and hammer-milling) and particle sizes, all the

grains exhibited a monosphasic digestogram as shown in Figure 3.3. As expected, more

fractions of starch in hard wheat, field peas and barley were digested as the digestion time

increased, or particle size decreased. This is due to the increased interactions between


29

substrate and enzymes that result in increased digested starch (Mahasukhonthachat et al.,

2010, Sopade and Gidley, 2009).

Figure 3.3. Monophasic digestograms of A) Hard Wheat, B) Barley 3873, C) Field

peas 7 and D) Field peas 12

Enzymatic digestion of starch can be affected by many factors such as granule size,

non-starch components, crystalline pattern and amylose to amylopectin ratio, presence of

lipids, presence of minerals, presence of proteins, digestion conditions and milling conditions

(Benmoussa et al., 2006, Vieira and Sarmento, 2008). In this study, further discussion and

analysis will be concentrated on the effect of particle sizes generated by cryo- and hammer-

0

20

40

60

80

100

0 40 80 120 160 200 240

CG 1

Predicted

HM 5

Predicted

Sample Code

0

20

40

60

80

100

0 40 80 120 160 200 240

CG 1

Predicted

HM 5

Predicted

Sample Code

B

Barley 3873

0

20

40

60

80

100

0 40 80 120 160 200 240

CG 1

Predicted

HM 5

Predicted

Sample Code

C

MAKI S409 Phenology 0

20

40

60

80

100

0 40 80 120 160 200 240

CG 1

Predicted

HM 5

Predicted

Sample Code

D

Parafield S409T1 PBIN

Time (min.)

Dig

este

d s

tarc

h (

g/1

00

g d

ry s

tarc

h)

A

Hard Wheat


30

mills on the rate of starch digestion. Figure 3.4 showed that the amount of starch digested in

barley 3873 (B38), soft wheat (SW) and field peas 4 (P4) varied along with the particle sizes

and types of milling. As reported in previous studies, differences in the effective operating

force and the extent of frictional heat generation between cryo- and hammer-mills have

significant impact on the amount of starch digested on sorghum, sweetpotato and potato

(Chen et al., 2009, Mahasukhonthachat et al., 2010).

Figure 3.4. Starch digestograms of barley, wheat and field peas of different particle sizes

Dig

este

d s

tarc

h (

g/1

00

g d

ry s

tarc

h)

Time (min.)

0

20

40

60

80

100

0 40 80 120 160 200 240

HM 1 (627 µm)

HM 2 (508 µm)

HM 3 (416 µm)

HM 4 (286 µm)

HM 5 (113 µm)

Predicted

Sample code

0

20

40

60

80

100

0 40 80 120 160 200 240

CG 1 (71 µm)

CG 2 (133 µm)

CG 3 (381 µm)

CG 4 (118 µm)

CG 5 (83 µm)

Predicted

Sample code

Barley 3873

0

20

40

60

80

100

0 40 80 120 160 200 240

HM 1 (491 µm)

HM 2 (465 µm)

HM 3 (379 µm)

HM 4 (255 µm)

HM 5 (143 µm)

Predicted

Sample code

Soft Wheat

0

20

40

60

80

100

0 40 80 120 160 200 240

CG 1 (70 µm)

CG 2 (149 µm)

CG 3 (295 µm)

CG 4 (107 µm)

CG 5 (73 µm)

Predicted

Sample code

Soft Wheat

0

20

40

60

80

100

0 40 80 120 160 200 240

HM 1 (403 µm)

HM 2 (334 µm)

HM 3 (304 µm)

HM 4 (228 µm)

HM 5 (69 µm)

Predicted

Sample code

Parafield P3WNA09

0

20

40

60

80

100

0 40 80 120 160 200 240

CG 1 (59 µm)

CG 2 (128 µm)

CG 3 (271 µm)

CG 4 (86 µm)

CG 5 (69 µm)

Predicted

Sample code

Parafield P3WNA09

Barley 3873


31

It can be concluded from Figure 3.4 that increasing particle size of the samples decreased the

rate of starch digestion. This can be explained that finer samples have relative higher surface

area or the access b igesti e enz mes s ch as α-amylase, pepsin, pancreatin and

amyloglucosidase (Mahasukhonthachat et al., 2010, Sopade and Gidley, 2009). The order of

the rate of starch digestion for hammer and cryo-milling:

Barley 3873 (B38):

HM 1 (627 µm) < HM 2 (508 µm) < HM 3 (416 µm) < HM 4 (286 µm) < HM 5 (113 µm)

CG 3 (381 µm) < CG 2 (133 µm) < CG 4 (118 µm) < CG 5 (83 µm) < CG 1 (71 µm)

Soft Wheat (SW):



Parafield P3WNA09 (FP4):


CG 3 (271 µm) < CG 2(128 µm) < CG 4 (86 µm) < CG 5 (69 µm) < CG 1 (59 µm)

As discussed before, a modified first order kinetic model was used to model and

describe starch digestograms as shown in Figures 3.3 and 3.4. In these figures, it showed that

the pattern of the graph for experimental is well fitted into the predicted graphs. Thus, it can

be concluded that the model gave relatively accurate prediction of the digestograms. To

further examine the predictive ability of the model, sum of squares (SUMSQ), coefficient of

determination (r2), mean relative deviation modulus (MRDM) and accuracy factor (AF) were

obtained, and they ranged from 0.991 – 0.999, r2; 0.010 – 0.093, SUMSQ; 5.5 9.7, MDRM;

and 0.6 – 1.0, Af.


32

Furthermore, Tables 3.5a and 3.5b show the rate of starch digestion in the milled field

peas with the other digestion parameters from the modified first order kinetic model. The

salivary-gastric digested starch or very rapidly digested starch, Do showed differences within

and between the mills, and generally, the hammer-milled field peas had higher Do values than

the cryo-milled ones. This is due to mill differences with respect to the severity of frictional

heat, which from mill characteristics, would be more with the hammer-milling. In addition, it

is worth noting that starch in both the cryo- and hammer-milled field peas could not be

completely digested due to the presence of non-digestible starch components in the field peas.

The hammer-milled field peas, also exhibited a higher rate of starch digestion and a larger

area under the digestogram (not shown) than the cryo-milled field peas.

In order to establish the mechanisms of starch digestion, previous studies and others

investigated the dependence of the reciprocal of the rate of digestion on the square of the

average particles size (Dhital et al., 2010). The theory proposed that the rate of starch

digestion (1/KST) is inversely proportional to the square of the particle size (p2) is further re-

affirmed in this study (Dhital et al., 2010, Mahasukhonthachat et al., 2010). Figure 3.5

showed that an inverse relationship holds for the effects of particle size on the rate of starch

digestion of the milled grains, but there were mill differences. This agrees with previous

studies on sorghum, sweetpotato, potato and cowpea or blackeye beans (Chen et al., 2009,

Mahasukhonthachat et al., 2010, Tinus et al., 2012). The hammer-milled grains exhibited a

higher rate of starch digestion than the cryo-milled grains. Presumably, the likelihood of

more frictional heat and mechanical energy during hammer-milling caused more digested

starch as compared to the cryo-milled grains. This is because grains are quench-frozen in

liquid nitrogen to well below glass transition temperatures in cryo-milling with negligible

frictional heat as opposed to hammer-milling. Again, this finding further reinforced the


33

theory, concluded in previous studies, that the rate of starch digestion exhibited significant

relationship (p > 0.05) with the square of particle size of either cryo- or hammer-milled grains.

Table 3.5a The parameters of the modified first order kinetic model for starch

digestion

Sample Do D∞ KST x 10-3

Cryo-Milling (g/100 g dry starch) (g/100 g dry starch) (min-1

)

FP1 CG 1 4.7 64.4 9.5

FP1 CG 2 4.7 63.0 5.3

FP1 CG 3 4.0 64.7 3.3

FP1 CG 4 5.3 72.9 6.1

FP1 CG 5 8.3 71.8 6.8

FP2 CG 1 5.6 90.5 9.2

FP2 CG 2 5.1 87.0 5.8

FP2 CG 3 4.7 88.4 5.2

FP2 CG 4 5.8 82.6 7.1

FP2 CG 5 5.1 87.1 8.9

FP4 CG 1 5.0 84.1 9.7

FP4 CG 2 4.1 82.7 6.0

FP4 CG 3 7.8 78.1 6.1

FP4 CG 4 4.8 81.1 6.6

FP4 CG 5 5.6 86.1 6.7

Hammer-

milling

FP1 HM 1 4.7 61.3 5.8

FP1 HM 2 5.2 62.2 6.1

FP1 HM 3 4.5 64.6 6.1

FP1 HM 4 4.5 63.9 6.4

FP1 HM 5 5.6 73.4 10.0

FP2 HM 1 3.5 73.0 6.5

FP2 HM 2 5.0 79.3 7.5

FP2 HM 3 5.1 76.0 8.6

FP2 HM 4 6.3 87.8 8.9

FP2 HM 5 6.4 87.3 10.1

FP4 HM 1 6.1 58.6 6.1

FP4 HM 2 4.7 69.1 6.2

FP4 HM 3 4.9 79.6 6.4

FP4 HM 4 5.1 86.7 6.9

FP4 HM 5 5.5 89.1 9.8


34

Table 3.5b The parameters of the modified first order kinetic model for starch

digestion

Sample code Do D∞ KST x 10-3


)

FP7 CG 1 5.6 96.0 8.1

FP7 CG 2 4.8 69.9 5.0

FP7 CG 3 4.5 84.5 4.2

FP7 CG 4 5.7 88.0 5.7

FP7 CG 5 6.3 80.8 6.7

FP12 CG 1 5.3 87.1 8.6

FP12 CG 2 6.3 94.4 5.0

FP12 CG 3 5.1 80.7 4.8

FP12 CG 4 5.9 94.0 5.1

FP12 CG 5 6.0 84.9 8.3

FP17 CG 1 6.6 82.5 8.8

FP17 CG 2 7.0 81.0 5.6

FP17 CG 3 5.8 73.6 5.0

FP17 CG 4 6.3 75.7 6.5

FP17 CG 5 6.1 97.2 8.7

Hammer-

milling

FP7 HM 1 4.5 56.9 4.7

FP7 HM 2 5.0 66.5 5.0

FP7 HM 3 5.6 88.8 6.1

FP7 HM 4 6.8 83.5 7.6

FP7 HM 5 6.0 91.8 11.6

FP12 HM 1 5.5 55.7 5.2

FP12 HM 2 5.7 72.1 5.8

FP12 HM 3 7.8 76.1 6.6

FP12 HM 4 5.9 80.3 6.9

FP12 HM 5 6.4 85.0 10.9

FP17 HM 1 7.5 58.0 6.2

FP17 HM 2 6.6 63.0 6.9

FP17 HM 3 6.5 83.2 7.0

FP17 HM 4 6.5 87.2 7.7

FP17 HM 5 6.5 88.9 10.0


35

Figure 3.5 The relationship between the reciprocal of the rate of constant or its

function and the square of the particle size

In order to establish if diffusion-controlled mechanisms apply to starch digestion in

barley, wheat and field peas, it is necessary to investigate the dependence of the reciprocal of

the rate of digestion on the square of the average particle size. Theory from previous studies

has revealed that the rate of starch digestion is inversely proportional to the square of the

particle size (Dhital et al., 2010, Mahasukhonthachat et al., 2010). Hence, a linear regression

analysis was carried out with and without intercepts to examine the relationships, and the

0

0.0005

0.001

0.0015

0.002

0.0025

0.003

0.0035

0.004

5000 10000 15000 20000

HM

CG

Barley 3873

0

0.0002

0.0004

0.0006

0.0008

0.001

0.0012

0.0014

0.0016

0.0018

5000 7000 9000 11000 13000 15000 17000

HM

CG

MAKI S409 Phenology

0

0.0002

0.0004

0.0006

0.0008

0.001

0.0012

5000 7000 9000 11000

HM

CG

CRC Walana Increase

0

0.0005

0.001

0.0015

0.002

0.0025

4000 5000 6000 7000 8000 9000

HM

CG

Soft Wheat

Average particle size 2 x 10-4 (cm2)

1/K

ST

x 1

03

(s)


36

reciprocal of the slope equates to the apparent diffusion coefficient, diffusion mechanisms

prevail if the apparent diffusion coefficient is of the order of 10-7

cm2 s

-1 (Tinus et al., 2012).

Tables 3.6a and 3.6b show the linear equations of the particle size-starch digestibility

obtained with and without the intercepts in both the cryo- or hammer-milled samples, and

indicate that starch digestion in both cryo- or hammer-milled samples proceeded by diffusion

mechanisms. Besides, the apparent diffusion coefficients obtained from Tables 3.6a and 3.6b

showed that different grains, such as barley, wheat and field peas have different diffusion

coefficients. The differences could be due to many factors including cell wall architecture,

the nature of protein matrices that surrounds starch granules and the presence of anti-

n tritiona actors s ch as tannin which can bin to the α-amylase enzyme and reduce its

accessibility through the grain fragments (Björck and Nyman, 1987; Hamaker et al., 1987).

Moreover, it is crucial to examine whether salivary digested starch or very rapidly

digested starch (Do) is only due to the fractions of the finest particles d (v,0.1) or 10th

percentiles. Previous studies have shown that starch granules and protein bodies are less than

150 µm in size. However, there was no evidence to prove that the physically-separated starch

granules and protein bodies are made up by the 10th

and 50th

percentiles (Becker et al., 2001,

Waramboi et al., 2012). The 10th

and 50th

percentiles were used to examine if they could be

used to explain the development of salivary digested starch, Do. In this study, regression

analysis revealed similar significant relationships between Do, and the 10th

, 50th

and 90th

percentiles (Figures 3.6a and 3.6b). Hence, it is plausible that salivary-gastric starch, Do, is

not determined by only 10th

percentile (fine materials), but determined by the whole materials

and their components.


37

Table 3.6a Linear equations between the square of the particle size and the

reciprocal of the rate of constant or its function in the cryo-milled samples

Sample 1/K against size2

Diffusion, Diff (x 10-7

cm2 s

-1)

(Cryo-

milling) With intercept Without intercept

With

intercept

Without

intercept

Barley

3764 1/K = 4.6 x 10

3 + 14.9 x 10

6 size

2 1/K = 4.7 x 10

6 size

2 0.67 0.21

r

2 = 0.36 * r

2 = 0.89 **

Barley

3873 1/K = 7.1 x 10

3 + 14.7 x 10

6 size

2 1/K = 2.6 x 10

6 size

2 0.68 0.38

r

2 = 0.91 * r

2 = 0.50 **

Barley

3933 1/K = 5.0 x 10

3 + 15.4 x 10

6 size

2 1/K = 2.1 x 10

6 size

2 0.65 0.47

r

2 = 0.81 * r2 = 0.56 *

Hard Wheat 1/K = 5.2 x 103 + 10.6 x 10

6 size

2 1/K = 2.3 x 10

6 size

2 0.94 0.43

r

2 = 0.74 * r

2 = 0.43 *

Soft Wheat 1/K = 5.2 x 103 + 12.8 x 10

6 size

2 1/K = 2.1 x 10

6 size

2 0.78 0.48

r

2 = 0.90 * r

2 = 0.60 *

Sprouted

Wheat 1/K = 6.2 x 10

3 + 13.7 x 10

6 size

2 1/K = 3.0 x 10

6 size

2 0.73 0.34

r

2 = 0.86 * r

2 = 0.63 **

FP 1 1/K = 8.1 x 10

3 + 13.0 x 10

6 size

2 1/K = 2.3 x 10

6 size

2 0.74 0.43

r

2 = 0.89 * r

2 = 0.66 *

FP 2 1/K = 6.9 x 103 + 13.0 x 10

6 size

2 1/K = 2.2 x 10

6 size

2 0.76 0.46

r

2 = 0.91 * r

2 = 0.69 **

FP 4 1/K = 8.2 x 10

3 + 13.9 x 10

6 size

2 1/K = 2.8 x 10

6 size

2 0.72 0.36

r

2 = 0.81 ** r

2 = 0.79 *

FP 7 1/K = 7.9 x 103 + 12.0 x 10

6 size

2 1/K = 2.4 x 10

6 size

2 0.83 0.41

r

2 = 0.83 * r

2 = 0.79 *

FP 12 1/K = 8.5 x 103 + 12.8 x 10

6 size

2 1/K = 2.4 x 10

6 size

2 0.78 0.42

r

2 = 0.47 * r

2 = 0.63 *

FP 17 1/K = 7.7 x 10

3 + 12.8 x 10

6 size

2 1/K = 2.7 x 10

6 size

2 0.78 0.36

r2 = 0.69 * r

2 = 0.79*

aLevel of significance,

* ≤

** ≤

*** ≤ NS = non-significance (p > 0.05)


38

Table 3.6b Linear equations between the square of the particle size and the

reciprocal of the rate of constant or its function in the hammer-milled

samples

Sample 1/K against size2

Diffusion, Diff (x 10-7

cm2 s

-1)

(Hammer-

milling) With intercept Without intercept

With

intercept

Without

intercept

Barley 3764 1/K = 3.2 x 103 + 14.7 x 10

6 size

2 1/K = 4.3 X 10

6 size

2 0.68 0.23

r

2 = 0.53 ** r

2 = 0.91 **

Barley 3873 1/K = 4.4 x 10

3 + 13.9 x 10

6 size

2 1/K = 4.7 X 10

6 size

2 0.72 0.21

r

2 = 0.97 ** r

2 = 0.94 **

Barley 3933 1/K = 4.2 x 10

3 + 13.2 x 10

6 size

2 1/K = 3.3 x 10

6 size

2 0.76 0.3

r

2 = 0.94 ** r

2 = 0.88 *

Hard Wheat 1/K = 4.2 x 103 + 11.9 x 10

6 size

2 1/K = 2.4 x 10

6 size

2 0.84 0.41

r

2 = 0.90 * r

2 = 0.81*

Soft Wheat 1/K = 4.1 x 103 + 11.4 x 10

6 size

2 1/K = 3.3 x 10

6 size

2 0.88 0.31

r

2 = 0.94 ** r

2 = 0.86 *

Sprouted

Wheat 1/K = 6.1 x 10

3 + 11.8 x 10

6 size

2 1/K = 3.2 x 10

6 size

2 0.84 0.31

r

2 = 0.96 ** r

2 = 0.85 *

FP 1 1/K = 6.6 x 10

3 + 11.4 x 10

6 size

2 1/K = 2.6 X 10

6 size

2 0.88 0.39

r

2 = 0.72 * r

2 = 0.85 *

FP 2 1/K = 5.7 x 103 + 12.7 x 10

6 size

2 1/K = 2.0 X 10

6 size

2 0.79 0.49

r

2 = 0.98 ** r

2 = 0.81 *

FP 4 1/K = 6.7 x 10

3 + 13.7 x 10

6 size

2 1/K = 2.3 x 10

6 size

2 0.73 0.43

r

2 = 0.84 * r

2 = 0.83 *

FP 7 1/K = 4.6 x 103 + 12.3 x 10

6 size

2 1/K = 2.7 x 10

6 size

2 0.81 0.36

r2 = 0.95** r

2= 0.94 **

FP 12 1/K = 7.6 x 103 + 13.7 x 10

6 size

2 1/K = 3.1 x 10

6 size

2 0.73 0.32

r

2 = 0.58 * r

2 = 0.60 **

FP 17 1/K = 6.1 x 10

3 + 13.0 x 10

6 size

2 1/K = 2.9 x 10

6 size

2 0.77 0.35

r2 = 0.87 ** r

2 = 0.86 **


* ≤

** ≤



39

Figure 3.6a. Particle size parameters against the salivary-gastric digested starch, Do in

cryo-milled grains.

0

5

10

15

20

25

0

200

400

600

800

1000

1200

1400

1600

2 2.5 3 3.5 4 4.5

d(0.

1),

d(0.

5)

d(0.

9),

d(4.

3)

d(0.5)

d(0.9)

d(4.3)

d(0.1)

Barley 3764

0

1

2

3

4

5

6

7

8

9

0

100

200

300

400

500

600

700

800

900

4 4.5 5 5.5 6 6.5 7 7.5 8 8.5 9

d(0.

1),

d(0.

5)

d(0.

9),

d(4.

3)

d(0.5)

d(0.9)

d(4.3)

d(0.1)

Field peas 1

0

1

2

3

4

5

6

7

8

0

100

200

300

400

500

600

4 4.5 5 5.5 6 6.5

d(0.

1),

d(0.

5)

d(0.

9),

d(4.

3)

d(0.5)

d(0.9)

d(4.3)

d(0.1)

Field peas 7

0

2

4

6

8

10

12

14

16

0

100

200

300

400

500

600

700

4 4.5 5 5.5

d(0.

1),

d(0.

5)

d(0.

9),

d(4.

3)

d(0.5)

d(0.9)

d(4.3)

d(0.1)

Sprouted Wheat

R² = 0.5737 0

2

4

6

8

10

12

14

16

18

20

0

100

200

300

400

500

600

700

800

900

4 4.5 5

d(0.

1),

d(0.

5)

d(0.

9),

d(4.

3)

d(0.5)

d(0.9)

d(4.3)

d(0.1)

Soft Wheat

0

2

4

6

8

10

12

0

50

100

150

200

250

300

350

400

450

500

1 2 3 4 5 6 7 8

d(0.

1),

d(0.

5)

d(0.

9),

d(4.

3)

d(0.5)

d(0.9)

d(4.3)

d(0.1)

Barley 3933

Salivary-gastric digested starch, Do (g/100 g dry starch)

Pa

rtic

le s

ize

pa

ram

eter

(µm

)


40

Figure 3.6b Particle size parameters against the salivary-gastric digested starch, Do in

hammer-milled grains.

0

5

10

15

20

25

0

200

400

600

800

1000

1200

1400

3 4 5

d(0

.1),

d(0

.5)

d(0

.9),

d(4

.3)

d(0.5)

d(0.9)

d(4.3)

d(0.1)

Barley 3764

0

2

4

6

8

10

12

14

16

18

0

200

400

600

800

1000

1200

4 5 6 7

d(0

.1),

d(0

.5)

d(0

.9),

d(4

.3)

d(0.5)

d(0.9)

d(4.3)

d(0.1)

Field peas 7

0

2

4

6

8

10

12

14

0

200

400

600

800

1000

1200

3 4 5 6 7

d(0

.1),

d(0

.5)

d(0

.9),

d(4

.3)

d(0.5)

d(0.9)

d(4.3)

d(0.1)

Field peas 2

0

5

10

15

20

25

0

200

400

600

800

1000

1200

3 4 5

d(0

.1),

d(0

.5)

d(0

.9),

d(4

.3)

d(0.5)

d(0.9)

d(4.3)

d(0.1)

Soft Wheat

0

5

10

15

20

25

0

200

400

600

800

1000

1200

4 5 6

d(0

.1),

d(0

.5)

d(0

.9),

d(4

.3)

d(0.5)

d(0.9)

d(4.3)

d(0.1)

Sprouted Wheat

0

5

10

15

20

25

30

35

40

45

50

0

200

400

600

800

1000

1200

1400

5 6 7

d(0

.1),

d(0

.5)

d(0

.9),

d(4

.3)

d(0.5)

d(0.9)

d(4.3)

d(0.1)

Hard Wheat

Salivary-gastric digested starch, Do (g/100 g dry starch)

Pa

rtic

le s

ize

pa

ram

eter

(µ

m)


41

3.5 Protein digestion kinetics

Irrespective of milling types and particle sizes, there is a reduction in the pH of the

protease-digested barley, wheat, field peas, lupins and casein (control) with the incubation

time. As expected, the speed of the pH-drop curve was different for casein and samples, such

as barley, wheat, field peas and lupin as shown in Figure 3.7. For example, the reduction in

the pH of casein was more than that of other samples. This can be explained that casein is

essentially protein, while there are non-protein (e.g. starch and cell wall) components in the

other samples that are capable of affecting protein digestion. Hence, it can be deduced that

casein is certainly digested more quickly than all other samples.

In this study, further discussion and analysis will be concentrated on the effect of

particle sizes generated by cryo- and hammer-mills on the rate of protein digestion. Figure

3.8 showed that the amount of protein digested in barley 3933 (B39), sprouted wheat (SPW),

coromup lupin (CL) and field peas 7 (FP7) varied along with the particle sizes and types of

milling. As discussed before, differences in the effective operating force and the extent of

frictional heat generation between cryo- and hammer-mills can have significant impact on the

amount of protein digested.

As shown in Figure 3.8, increasing particle size of the milled samples decreased the

rate of protein digestion. This can be explained that smaller samples have relative higher

surface area per unit volume for the access by digestive enzymes, such as trypsin,

chymotrypsin and protease. The order of the rate of protein digestion for hammer and cryo-

milling:


42

Figure 3.7 Monophasic digestograms of A) Barley 3873, B) Gunyidi Lupin, C)

Sprouted Wheat, D) Field peas 7, E) Field peas 1 and F) Casein

Time (min.)

pH

A

Casein

4

4.5

5

5.5

6

6.5

7

7.5

8

8.5

0 5 10 15

F

Casein

7

7.2

7.4

7.6

7.8

8

0.00 5.00 10.00 15.00

CG 1

HM 5

Predicted-CG

Predicted-HM

6.8

7

7.2

7.4

7.6

7.8

8

0.00 5.00 10.00 15.00

CG 1

HM 5

Predicted-CG

Predicted-HM

Gunyidi Lupins

6.8

7

7.2

7.4

7.6

7.8

8

0.00 5.00 10.00 15.00

CG 1

HM 5

Predicted-CG

Predicted-HM

Sprouted Wheat

7

7.2

7.4

7.6

7.8

8

0.00 5.00 10.00 15.00

CG 1

HM 5

Predicted-CG

Predicted-HM

MAKI S409 Phenology

6.8

7

7.2

7.4

7.6

7.8

8

0.00 5.00 10.00 15.00

CG1

HM5

Predicted-CG

Predicted-HM

MAKI AGT Increase

Barley 3873


43

Figure 3.8 Protein digestograms of barley 3933, sprouted wheat, coromup lupin and

field peas 7 of different particle sizes

7

7.1

7.2

7.3

7.4

7.5

7.6

7.7

7.8

7.9

8

0.00 2.00 4.00 6.00 8.00 10.00 12.00 14.00 16.00

HM 1

HM 2

HM 3

HM 4

HM 5

Predicted pH

MAKI S409 Phenology

6.8

7

7.2

7.4

7.6

7.8

8

0.00 2.00 4.00 6.00 8.00 10.00 12.00 14.00 16.00

HM 1

HM 2

HM 3

HM 4

HM 5

Predicted pH

Sprouted Wheat

6.8

7

7.2

7.4

7.6

7.8

8

0.00 2.00 4.00 6.00 8.00 10.00 12.00 14.00 16.00

CG 1

CG 2

CG 3

CG 4

CG 5

Predicted pH

Barley 3933

6.8

7

7.2

7.4

7.6

7.8

8

0.00 2.00 4.00 6.00 8.00 10.00 12.00 14.00 16.00

CG 1

CG 2

CG 3

CG 4

CG 5

Predicted pH

MAKI S409 Phenology

6.6

6.8

7

7.2

7.4

7.6

7.8

8

0.00 2.00 4.00 6.00 8.00 10.00 12.00 14.00 16.00

HM 1

HM 2

HM 3

HM 4

HM 5

Predicted pH

Coromup Lupins

6.6

6.8

7

7.2

7.4

7.6

7.8

8

0.00 2.00 4.00 6.00 8.00 10.00 12.00 14.00 16.00

B39HM 1

B39HM 2

B39HM 3

B39HM 4

B39HM 5

Predicted pH

Barley 3933

6.6

6.8

7

7.2

7.4

7.6

7.8

8

0.00 2.00 4.00 6.00 8.00 10.00 12.00 14.00 16.00

CG 1

CG 2

CG 3

CG 4

CG 5

Predicted pH

Sprouted Wheat

6.8

7

7.2

7.4

7.6

7.8

8

0.00 2.00 4.00 6.00 8.00 10.00 12.00 14.00 16.00

CG 1

CG 2

CG 3

CG 4

CG 5

Predicted pH

Coromup Lupins

Time (min.)

pH


44

Barley 3933 (B39):



Sprouted Wheat (SPW):



Coromup Lupins (CL):



MAKI S409 Phenology (FP 7):



As discussed in the kinetics of starch digestion, a modified first order kinetic model

was used to model and describe protein digestograms as shown in Figure 3.7. In Figure 3.7, it

showed that the pattern of the graph for experimental is well fitted into the predicted graphs.

Thus, it can be concluded that the model gave relatively accurate prediction of the

digestograms. To further examine the predictive ability of the model, sum of squares

(SUMSQ), coefficient of determination (r2), mean relative deviation modulus (MRDM) and

accuracy factor (AF) were obtained, and they ranged from 0.991 – 0.998, r2; 0.009 – 0.091,

SUMSQ; 0.1 – 0.4, MDRM; and 0.7 – 1.0, Af. The closer the SUMSQ and MDRM values to

0 and the closer the Af values to 1, which are considered an indicator of good predictability.


45

The protein digestograms of all the samples can be well described by modelling the

time course protein digestion of the samples with the modified first-order kinetic model in

(Eqn. [1.2]). Tables 3.7a and 3.7b summarize the parameters from the model and the apparent

in-vitro protein digestibility (IVPD) (Eqn. [3.1]):

V D = + Δ 10 min. (3.1)

where Δ 10 min. is the change in pH 10 in 10 min. from the initial pH of about 8.0.

Again, irrespective of the mills and milling condition, in-vitro protein digestion

(IVPD) of hammer-milled samples were digested more than 80%, exhibiting nominally

higher protein digestion than the cryo-milled samples as recorded in Tables 3.7a and 3.7b. It

can also be observed (Table 3.7a and 3.7b) that, the rate of protein digestion in the hammer-

milled samples was nominally higher than that of the cryo-milled samples. As discussed for

starch, the frictional heat and mechanical energy in the hammer mill are capable of disrupting

the structure and organisation of the sample protein to make them more susceptible to

digestion (Chen et al., 2009, Mahasukhonthachat et al., 2010).

Besides that, the theory proposed that the rate of protein digestion (1/KPR) is inversely

proportional to the square of the particle size (p2) can be further re-affirmed in this study.

Figure 3.9 showed that an inverse relationship holds for the effects of particle size on the rate

of protein digestion of the milled samples, but there were mill differences; a result that agreed

with the previous studies on the starch digestion of sorghum, sweetpotato and potato (Chen et

al., 2009, Mahasukhonthachat et al., 2010, Tinus et al., 2012). This further proven the results

obtained by calculating the IVPD in Tables 3.7a and 3.7b. Presumably, the likelihood of more

frictional heat and mechanical energy during hammer-milling caused more digested starch as

compared to the cryo-milled grains. This is because samples are quench-frozen in liquid


46

nitrogen to well below glass transition temperatures in cryo-milling with negligible frictional

heat as opposed to hammer-milling (Tinus et al., 2012).

Figure 3.9. The relationship between the reciprocal of the rate of constant or its

function and the square of the particle size

0

0.0002

0.0004

0.0006

0.0008

0.001

0.0012

0 100 200 300 400

CG

HM

CRC Walana Increase

0

0.0005

0.001

0.0015

0.002

0.0025

0.003

0 50 100 150 200 250 300

CG

HM

Soft Wheat

0

0.0005

0.001

0.0015

0.002

0.0025

0.003

0.0035

0.004

0 100 200 300 400 500

CG

HM

Gunyidi Lupins

0

0.0005

0.001

0.0015

0.002

0.0025

0.003

0.0035

0.004

0.0045

0 100 200 300 400

CG

HM

Barley 3873

Average particle size 2 x 10-4 (cm2)

1/K

PR

x 1

04

(s)


47

Table 3.7a. The parameters of the modified first order kinetic model for protein

digestion in cryo-milled field peas

Sample Sample code

Average particle

size

IVPD

(g/100 g dry protein) KPR x 10-3

(min-1

)

d [4,3], µm

Field peas 1 FP1CG 3 263.3 80.7 228.7

Field peas 1 FP1CG 2 101.0 82.2 290.3

Field peas 1 FP1CG 4 72.5 82.4 254.0

Field peas 1 FP1CG 5 65.3 84.3 295.0

Field peas 1 FP1CG 1 51.1 84.5 391.1

Field peas 12 FP12CG 3 204.4 78.6 176.5

Field peas 12 FP12CG 2 104.3 80.4 198.7

Field peas 12 FP12CG 4 84.7 80.7 199.8

Field peas 12 FP12CG 5 70.0 81.0 231.7

Field peas 12 FP17CG 1 61.5 83.5 243.6

Field peas 17 FP17CG 3 293.3 78.4 114.2

Field peas 17 FP17CG 2 143.8 79.0 172.2

Field peas 17 FP17CG 4 78.8 79.4 172.7

Field peas 17 FP17CG 5 75.9 79.7 253.9

Field peas 17 FP17CG 1 68.0 80.4 300.5

Field peas 2 FP2CG 3 279.1 80.2 186.1

Field peas 2 FP2CG 2 246.1 82.0 214.3

Field peas 2 FP2CG 4 99.6 82.3 232.4

Field peas 2 FP2CG 5 64.7 82.6 277.5

Field peas 2 FP2CG 1 55.4 82.8 307.6

Field peas 4 FP4CG 3 271.0 77.5 155.6

Field peas 4 FP4CG 2 128.2 77.9 179.1

Field peas 4 FP4CG 4 86.3 78.5 193.0

Field peas 4 FP4CG 5 69.1 78.9 196.4

Field peas 4 FP4CG 1 58.8 79.2 210.2

Field peas 7 FP7CG 3 161.9 76.2 273.4

Field peas 7 FP7CG 2 96.4 77.7 282.8

Field peas 7 FP7CG 4 74.7 79.9 302.1

Field peas 7 FP7CG 5 72.3 80.1 330.8

Field peas 7 FP7CG 1 63.5 80.3 348.1


48

Table 3.7b. The parameters of the modified first order kinetic model for protein

digestion in hammer-milled field peas

Sample Sample code

Average

particle size IVPD KPR x 10-

3 (min-1

)

d [4,3], µm (g/100 g dry protein)

Field peas 1 FP1HM 5 263.3 85.1 333.9

Field peas 1 FP1HM 4 101.0 84.8 327.7

Field peas 1 FP1HM 1 72.5 81.0 275.4

Field peas 1 FP1HM 2 65.3 82.9 279.0

Field peas 1 FP1HM 3 51.1 84.1 285.0

Field peas 12 FP12HM 5 204.4 84.2 233.4

Field peas 12 FP12HM 4 104.3 81.2 234.3

Field peas 12 FP12HM 1 84.7 79.5 185.1

Field peas 12 FP12HM 2 70.0 80.3 236.9

Field peas 12 FP12HM 3 61.5 80.3 189.5

Field peas 17 FP17HM 5 293.3 83.7 184.1

Field peas 17 FP17HM 4 143.8 83.2 183.9

Field peas 17 FP17HM 1 78.8 79.8 210.1

Field peas 17 FP17HM 2 75.9 80.5 230.6

Field peas 17 FP17HM 3 68.0 81.1 174.6

Field peas 2 FP2HM 5 279.1 86.1 516.0

Field peas 2 FP2HM 4 246.1 84.2 362.2

Field peas 2 FP2HM 1 99.6 79.4 297.9

Field peas 2 FP2HM 2 64.7 81.8 300.4

Field peas 2 FP2HM 3 55.4 83.7 339.4

Field peas 4 FP4HM 5 271.0 80.8 217.9

Field peas 4 FP4HM 4 128.2 80.7 415.2

Field peas 4 FP4HM 1 86.3 79.2 268.4

Field peas 4 FP4HM 2 69.1 79.4 228.7

Field peas 4 FP4HM 3 58.8 80.6 299.3

Field peas 7 FP7HM 5 161.9 81.5 260.8

Field peas 7 FP7HM 4 96.4 81.0 256.6

Field peas 7 FP7HM 1 74.7 79.3 164.7

Field peas 7 FP7HM 2 72.3 80.4 185.5

Field peas 7 FP7HM 3 63.5 80.5 282.9


49

As highlighted above, a review of the literature revealed most studies investigated

starch rather than protein digestion. Studies about rate of protein digestion dependence on

processing factors such as particle size are relatively limited. It appears the present study is

the first to examine the dependence of the kinetic of protein digestion of cereals and pulses on

particle size. As mentioned for starch digestion, there were significant linearly relationships

between the reciprocal of the rate of protein digestion and the square of the particle size as

shown in Tables 3.8a and 3.8b. If the reciprocal of the rate of protein digestion, which is the

apparent diffusion coefficient is of the order of 10-7

cm2 s

-1, it is plausible to suggest that the

digestion of protein in the samples also proceeded by diffusion mechanisms. The reciprocal

of the slope of the relationship was in the order of 10-7

cm2 s

-1, and as shown in starch

digestion kinetics, it suggests that the digestion of proteins in these milled samples also

proceeded by diffusion mechanisms.


50

Table 3.8a. Linear equations between the square of the particle size and the

reciprocal of the rate of constant or its function in hammer-milled grains.

Sample 1/K against size2 Diffusion, Diff (x 10

-7 cm

2 s

-1)

(Cryo-

Milling) With intercept Without intercept

With

intercept

Without

intercept

Barley

3764 1/K = 178.6 + 120.5 x 10

3 size

2 1/K = 434.8 x 10

3 size

2 83.0 23.0

r2 = 0.80 * r2 = 0.90 **

Barley

3873 1/K = 192.3 + 133.3 x 10

3 size

2 1/K = 188.7 x 10

3 size

2 75.1 53.0

r2 = 0.66 * r2 = 0.82 *

Barley

3933 1/K = 113.7 + 136.9 x 10

3 size

2 1/K = 208.3 x 10

3 size

2 73.0 48.0

r2 = 0.91 ** r2 = 0.87 *

Coromup

Lupins 1/K = 90.6 + 117.6 x10

3 size

2 1/K = 232.6 x 10

3 size

2 85.0 43.0

r2 = 0.77 * r2 = 0.94 **

Gunyidi

Lupins 1/K = 149.3 + 126.6 x 10

3 size

2 1/K = 208.3 x 10

3 size

2 79.0 48.0

r2 = 0.98 ** r2 = 0.93 **

Hard

Wheat 1/K = 160.3 + 122.0 x 10

3 size

2 1/K = 196.1 x 10

3 size

2 82.0 51.0

r2 = 0.92 ** r2 = 0.82 *

Mandelup

Lupins 1/K = 119.1 + 133.3 x 10

3 size

2 1/K = 243.9 x 10

6 size

2 75.0 41.0

r2 = 0.77 * r2 = 0.91 **

Soft

Wheat 1/K = 167.6

+ 122.0 x 10

3 size

2 1/K = 196.1 x 10

3 size

2 81.9 51.0

r2 = 0.90 * r2 = 0.84 *

Sprouted

Wheat 1/K = 144.7 + 129.9 x 10

3 size

2 1/K = 238.1 x 10

3 size

2 77.0 42.0

r2 = 0.79 * r2 = 0.93 **

FP 1 1/K = 177.1

+ 131.6 x 10

3 size

2 1/K = 244.0 x 10

3 size

2 76.0 41.0

r2 = 0.77 * r2 = 0.80 *

FP 2 1/K = 132.7 + 131.6 x 10

3 size

2 1/K = 212.8 x 10

3 size

2 76.0 47.0

r2 = 0.74 * r2 = 0.81 *

FP 4 1/K = 225.3

+ 123.5 x 10

3 size

2 1/K = 222.2 x 10

3 size

2 81.0 45.0

r2 = 0.90 * r2 = 0.67 *

FP 7 1/K = 195.8 + 122.0 x 10

3 size

2 1/K = 256.4 x 10

3 size

2 81.9 39.0

r2 = 0.84 * r2 = 0.86 *

FP 12 1/K = 264.9 + 133.3 x 10

3 size

2 1/K = 222.2 x10

3 size

2 75.0 45.0

r2 = 0.82 * r2 = 0.71 *

FP 17 1/K = 132.7 + 119.0 x 10

3 size

2 1/K = 227.3 x10

3 size

2 84.0 44.0

r2 = 0.74 * r2 = 0.81 *


* ≤

** ≤



51

Table 3.8b. Linear equations between the square of the particle size and the

reciprocal of the rate of constant or its function in cryo-milled grains.

Sample 1/K against size2 Diffusion, Diff (x 10

-7 cm

2 s

-1)

(Hammer

-Milling) With intercept Without intercept

With

intercept

Without

intercept

Barley

3764 1/K = 177.3 + 128.8 x 10

3 size

2 1/K = 212.8 x 10

3 size

2 77.7 47.0

r2= 0.91 * r2 = 0.93 **

Barley

3873 1/K = 223.6 + 123.5 x 10

3 size

2 1/K = 263.4 x 10

3 size

2 80.9 38.0

r2 = 0.45 * r2 = 0.82 *

Barley

3933 1/K = 175.7 + 127.2 x 10

3 size

2 1/K = 212.8 x 10

3 size

2 78.6 47.0

r2 = 0.99 *** r2 = 0.72 *

Coromup

Lupins 1/K = 116.3 + 128.6 x 10

3 size

2 1/K = 217.2 x 10

3 size

2 77.7 46.0

r2 = 0.96 * r2 = 0.94 *

Gunyidi

Lupins 1/K = 236.6 + 115.0 x 10

3 size

2 1/K = 232.5 x 10

3 size

2 87.0 43.0

r2 = 0.74 ** r2 = 0.93 *

Hard

Wheat 1/K = 215.6 + 128.3 x 10

3 size

2 1/K = 208.1 x 10

3 size

2 77.9 48.0

r2 = 0.65 * r2 = 0.82 *

Mandelup

Lupins 1/K = 250.7 + 125.0 x 10

3 size

2 1/K = 222.3 x 10

3 size

2 80.0 45.0

r2 = 0.44 NS r2 = 0.91 *

Soft

Wheat 1/K = 172.4 + 128.4 x 10

3 size

2 1/K = 238.1 x 10

3 size

2 77.9 42.0

r2 = 0.82 * r2 = 0.72 *

Sprouted

Wheat 1/K = 205.3 + 117.3 x 10

3 size

2 1/K = 208.9 x 10

3 size

2 85.2 47.9

r2 = 0.70 * r2 = 0.78 *

FP 1 1/K = 197.8 + 121.6 x 10

3 size

2 1/K = 256.4 x 10

3 size

2 82.2 39.0

r2 = 0.76 * r2 = 0.75 *

FP 2 1/K = 213.7 + 127.5 x10

3 size

2 1/K = 243.9 x 10

3 size

2 78.4 41.0

r2 = 0.74 * r2 = 0.84 *

FP 4 1/K = 298.6 + 131.8 x 10

3 size

2 1/K = 212.6 x10

3 size

2 75.9 47.0

r2 = 0.90 * r2 = 0.68 *

FP 7 1/K = 180.1 + 128.3 x 10

3 size

2 1/K = 243.5 x 10

3 size

2 77.9 41.1

r2 = 0.59 * r2 = 0.66 *

FP 12 1/K = 262.4 + 123.7 x 10

3 size

2 1/K = 256.1 x 10

3 size

2 80.8 39.0

r2 = 0.71 * r2 = 0.57 *

FP 17 1/K = 250.7 + 124.7 x 10

3 size

2 1/K = 232.8 x 10

3 size

2 80.2 42.9

r2 = 0.81 * r2 = 0.86 **


* ≤

** ≤



52

Furthermore, it is necessary to examine whether the rate of protein digestion (KPR) is

only due to the fractions of the finest particles d(v, 0.1) or 10th

percentiles. Previous studies

have shown that starch granules and protein bodies are less than 150 µm in size. However

there was no evidence to prove that the physically-separated starch granules and protein

bodies are made up by 10th

and 50th

percentiles. The 10th

and 50th

percentiles were used to

examine if they could be used to explain the development of the rate of protein digestion. In

this study, regression analysis revealed similar significant relationships between the rate of

protein digestion and the 10th

, 50th

and 90th

percentiles (Figures 3.10a and 3.10b). Thus, it is

plausible to conclude that the rate of protein digestion is not related to only 10th

percentile

(fine materials), but determined by the whole materials and their components.


53

Figure 3.10a. Particle size parameters against the rate of protein digestion, KPR in

cryo-milled grains

0

5

10

15

20

25

0

200

400

600

800

1000

1200

1400

100.0 150.0 200.0 250.0 300.0 350.0

d(0

.1),

d(0

.5)

d(0

.9),

d(4

.3)

d(0.5)

d(0.9)

d(4.3)

d(0.1)

Barley 3764

0

2

4

6

8

10

12

14

0

100

200

300

400

500

600

700

800

900

1000

350.0 400.0 450.0 500.0 550.0

d(0

.1),

d(0

.5)

d(0

.9),

d(4

.3)

d(0.5)

d(0.9)

d(4.3)

d(0.1)

Coromup Lupins

0

2

4

6

8

10

12

0

100

200

300

400

500

600

700

150.0 200.0 250.0

d(0

.1),

d(0

.5)

d(0

.9),

d(4

.3)

d(0.5)

d(0.9)

d(0.4)

d(0.1)

Field peas 12

0

2

4

6

8

10

12

14

0

100

200

300

400

500

600

700

800

900

1000

150.0 200.0 250.0 300.0 350.0

d(0

.1),

d(0

.5)

d(0

.9),

d(4

.3)

d(0.5)

d(0.9)

d(4.3)

d(0.1)

Field peas 2

0

2

4

6

8

10

12

0

100

200

300

400

500

600

700

800

900

140.0 180.0 220.0

d(0

.1),

d(0

.5)

d(0

.9),

d(4

.3)

d(0.5)

d(0.9)

d(4.3)

d(0.1)

Field peas 4

0

2

4

6

8

10

12

14

16

18

20

0

100

200

300

400

500

600

700

800

900

250.0 300.0 350.0 400.0

d(0

.1),

d(0

.5)

d(0

.9),

d(4

.3)

d(0.5)

d(0.9)

d(4.3)

d(0.1)

Soft Wheat

Rate of protein digestion, KPR x 10-3 (min-1)

Pa

rtic

le s

ize

pa

ram

eter

(µm

)


54

Figure 3.10b. Particle size parameters against the rate of protein digestion, KPR in

hammer-milled grains

0

5

10

15

20

25

0

200

400

600

800

1000

1200

1400

150 200 250 300 350

d(0

.1),

d(0

.5)

d(0

.9),

d(4

.3)

d(0.5)

d(0.9)

d(4.3)

d(0.1)

Barley 3764

0

10

20

30

40

50

60

0

200

400

600

800

1000

1200

1400

1600

150 200 250 300 350 400

d(0

.1),

d(0

.5)

d(0

.9),

d(4

.3)

d(0.5)

d(0.9)

d(4.3)

d(0.1)

Gunyidi Lupins

0

2

4

6

8

10

12

14

0

200

400

600

800

1000

1200

250 300 350

d(0

.1),

d(0

.5)

d(0

.9),

d(4

.3)

d(0.5)

d(0.9)

d(4.3)

d(0.1)

Field peas 1

0

2

4

6

8

10

12

14

0

200

400

600

800

1000

1200

250 300 350 400 450 500 550

d(0

.1),

d(0

.5)

d(0

.9),

d(4

.3)

d(0.5)

d(0.9)

d(4.3)

d(0.1)

Gunyidi Lupins

0

5

10

15

20

25

0

200

400

600

800

1000

1200

1400

200 250 300 350

d(0

.1),

d(0

.5)

d(0

.9),

d(4

.3)

d(0.5)

d(0.9)

d(4.3)

d(0.1)

Soft Wheat

0

5

10

15

20

25

0

200

400

600

800

1000

1200

1400

1600

200 240 280 320

d(0

.1),

d(0

.5)

d(0

.9),

d(4

.3)

d(0.5)

d(0.9)

d(4.3)

d(0.1)

Sprouted Wheat

Rate of protein digestion, KPR x 10-3 (min-1)

Pa

rtic

le s

ize

pa

ram

eter

(µm

)


55

Besides that, it is also important to note that the rate of protein digestion was about 33

(refer to Tables 3.5a and 3.5b for starch digestion and Tables 3.8a and 3.8b for protein

digestion) times higher than the rate of starch digestion in both cryo- and hammer-milled

samples. This can be explained that enzymatic activities of protein and starch digestion are

dependent on the concentration and specificity for the substrates. Unlike starch, the structure

and organisation of protein can exist in different forms with varied amino acids in food

systems. In previous studies, Vasconcelos et al. (2010) have obtained different form of

proteins in Brazilian cowpea, such as globulins, albumins, prolamins, glutelins and residuals.

These protein fractions were found to be different in terms of molecular weights and

susceptibility to proteolysis. In the study, results showed that the building blocks of protein,

amino acids were easier release from protein units, than the release of glucose from starch

molecules, thereby lowering the pH of the digesta (Waramboi et al., 2012). This can be

explained by the nature of the protein. Proteins, comprising of polymer chain of amino acids

bonded together by peptide bonds between carboxyl (COOH) and amino groups (NH2) will

start to break down in the stomach under acidic conditions. Eventually, more amino acids will

be produced leading to a pH reduction in the digestion samples (Dangin et al., 2001).

Figures 3.11 A and 3.11 B show the rate of protein digestion against the rate of starch

digestion in the milled samples. The rate of protein digestion was found to be proceeded at a

much faster (33 X) rate than starch digestion in the milled samples with implications for

nutrient asynchrony.


56

Figure 3.11. Rate of protein digestion against rate of starch digestion

0

50

100

150

200

250

300

350

400

450

0 2 4 6 8 10 12

Field peas 1Field peas 12Field peas 17Field peas 2Field peas 4Field peas 7

C

0

100

200

300

400

500

600

0 5 10 15

Field peas 1Field peas 12Field peas 17Field peas 2Field peas 4Chickpeas 7

D

Rate of starch digestion, KST x 10-3 (min-1)

Ra

te o

f p

ro

tein

dig

est

ion

, KP

R x

10

-3 (

min

-1)

KPR = 17.378 KST + 128 R² = 0.3059***

0

100

200

300

400

500

600

0 2 4 6 8 10 12 14 16

Hammer-milling(all samples)

A KPR = 13.485 KST + 142.31

R² = 0.278***

0

50

100

150

200

250

300

350

400

450

0 2 4 6 8 10 12 14

Cryo-milling(all samples)

B


57

CHAPTER 4 Conclusions

Particle size and type of mill affected starch and protein digestion in all milled grains.

Irrespective of the mill and for most or all the grain samples, starch and protein digestion can

be said to proceed by diffusion based on the inverse relationship between the rate of starch

and protein digestion (rate coefficient) and the square of the average particle size. However,

the hammer-milled grains were more digested with a higher diffusion coefficient, presumably

due to greater frictional heat and mechanical effects generated than during cryo-milling.

Frictional heat, consequently molecular transformations, would have been minimal or non-

existent during cryo-milling because this occurs in a glassy state.

The present study is the first on starch and protein digestion kinetics of a wide array

of cryo- and hammer-milled cereals and pulses. With differences in starch and protein

digestion, the presence of both nutrients will affect, as expected, overall energy availability

from food and feed. It is unknown if one is preferentially digested or the digestion of one is

rate-limited by the digestion of other. Hence, the significance of this study is having the

knowledge (a) relative digestibility of energy-yielding macromolecules in food and feed, (b)

nutrient asynchrony with the rate of protein digestion being much faster than that of starch

digestion in milled grains, (c) knowledge of how to maximise the end use of food and feed by

possibly targeting processing conditions that maximise the digestibility of the lower

digestible component for animal feeds, or conditions that minimize the digestibility of the

higher digestible component for human foods if that helps growth and gut health.


58

CHAPTER 5 Further research needs and recommendations

The following have been identified for future research needs:

a) Animal trials (feeding metabolism) to examine the concept of particle size

distributions.

b) Grain functional properties, such as pasting and gelatinisation combined with

digestion kinetics to select an appropriate particle size for desired application uses

of grains.

c) Identification of mill settings required to vary particle size distributions at fixed

average particle sizes.

d) Evaluation of particle size distribution at different settings in all commercial mills,

such as disc- and roller-mills.


59

References

AKESON, W. R. & STAHMANN, M. A. 1964. Pepsin pancreatin digest index of protein

quality evaluation. Journal of Nutrition, 83, 257-261.

AMERAH, A., RAVINDRAN, V., LENTLE, R. & THOMAS, D. 2007. Feed particle size:

Implications on the digestion and performance of poultry. World's Poultry Science

Journal, 63, 439-455.

BECKER, A., HILL, S. & MITCHELL, J. 2001. Milling-A further parameter affecting the

rapid visco analyser (RVA) profile. Cereal chemistry, 78, 166-172.

BENMOUSSA, M., SUHENDRA, B., ABOUBACAR, A. & HAMAKER, B. R. 2006.

Distinctive sorghum starch granule morphologies appear to improve raw starch

digestibility. Starch‐Stärke, 58, 92-99.

BJÖRCK, I. M. & NYMAN, M. E. 1987. In vitro effects of phytic acid and polyphenols on

starch digestion and fiber degradation. Journal of Food Science, 52, 1588-1594.

BLACK, J. L., HUGHES, R. J., NIELSEN, S. G., TREDREA, A. M., MACALPINE, R. &

BARNEVELD, R. J. 2005. The energy value of cereal grains, particularly wheat and

sorghum, for poultry. Proceedings of the Australian Poultry Science Symposium, 17,

21-29.

BOND, F. C. 1963. Some recent advances in grinding theory and practice. Brisitish

Chemistry Engineering 8, 631-634.

BYRD-BREDBENNER, C., MOE, G., BESHGETOOR, D. & BERNING, J. 2009.

Perspectives in nutrition London Mc Graw Hill Higher.

CHEN, G., KRAVCHUK, O. & SOPADE, P. Kinetics of starch digestion in Australian

sweetpotato as affected by particle size. 15th triennial symposium of the international

society for tropical root crops. International Potato Centre, Lima, Peru, 2009.

DANGIN, M., BOIRIE, Y., GARCIA-RODENAS, C., GACHON, P., FAUQUANT, J.,

CALLIER, P., BALLÈVRE, O. & BEAUFRÈRE, B. 2001. The digestion rate of

protein is an independent regulating factor of postprandial protein retention. American

Journal of Physiology-Endocrinology And Metabolism, 280, E340-E348.

DHITAL, S., SHRESTHA, A. K. & GIDLEY, M. J. 2010. Relationship between granule size

and in-vitro digestibility of maize and potato starches. Carbohydrate Polymers, 82,

480-488.

GIDLEY, M. J., SOPADE, P. A., RABADI, G. A., WILLIAMS, B. & BLACK, J. 2010.

Processing methods for improving the utilization of cereals grains by pigs. Australian

Government's Cooperative Research Centres 102, 1-34.

GOÑI, I., GARCIA-ALONSO, A. & SAURA-CALIXTO, F. 1997. A starch hydrolysis

procedure to estimate glycemic index. Nutrition Research, 17, 427-437.

HAMAKER, B., KIRLEIS, A., BUTLER, L., AXTELL, J. & MERTZ, E. 1987. Improving

the in vitro protein digestibility of sorghum with reducing agents. Proceedings of the

National Academy of Sciences, 84, 626.

HERRERA, S. R., GOMEZ, A. R., TORABI, M. & HUBER, J. T. 1990. Influence of

synchronizing proteina and starch degradation in the rumen on nutrient utilization and

microbial protein synthesis. Jounrnal of Dairy Science, 73, 142-149.

HSU., H. W., VAVAK., D. L., SATTERLEE., L. D. & MILLER, G. A. 1977. A

multienzyme technique for estimating protein digestibility. Journal of Food Science,

42, 1269-1273.

HUANG, Z. Q., XIE, X., CHEN, Y., LU, J. & TONG, Z. F. 2008. Ball-milling treatment

effect on physicochemical properties and features for cassava and maize starches.

Comptes Rendus Chimie, 11, 73-79.


60

KERR, W., WARD, C., MCWATTERS, K. & RESURRECCION, A. 2000. Effect of milling

and particle size on functionality and physicochemical properties of cowpea flour.

Cereal chemistry, 77, 213-219.

KIM, C. H. & MAGA, J. 1987. Properties of extruded whey protein concentrate and cereal

flour blends. Lebensmittel Wissenschaft Technologie, 20, 311-318.

LAURINEN, P., SILJANDER-RASI, H., KARHUNEN, J., ALAVIUHKOLA, T., NASI, M.

& TUPPI, K. 2000. Effects of different grinding methods and particle size of barley

and wheat on pig performance and digestibility. Animal Feed Science and Technology,

83, 1-16.

LAWRANCE, L. 2007. Lupins Australia's role in world markets. Australian Commodities,

14, 353-356.

MAGA, J. A., LORENZ, K. & ONAYEMI, O. 1973. Digestive acceptability of proteins as

measured by the initial rate of in vitro proteolysis. Journal of Food Science, 38, 173-

174.

MAHASUKHONTHACHAT, K., SOPADE, P. A. & GIDLEY, M. J. 2010. Kinetics of

starch digestion in sorghum as affected by particle size. Journal of Food Engineering,

96, 18-28.

MERTZ, E. T., HASSEN, M. M., CAIRNSWHITTERN, C., KIRLEIS, A. W., TU, L. &

AXTELL, J. D. 1984. Pepsin digestibility of proteins in sorghum and other major

cereals. Proceedings of the National Academy of Sciences of the United States of

America-Biological Sciences, 81, 1-2.

NALLE, C., RAVINDRAN, V. & RAVINDRAN, G. 2011. Nutritional value of white lupins

(Lupinus albus) for broilers: apparent metabolisable energy, apparent ileal amino acid

digestibility and production performance. Animal, 1, 1-7.

RICHARDSON, J. M., WILKINSON, R. G. & SINCLAIR, L. A. 2003. Synchrony of

nutrient supply to the ruman and dietary energy source and their effects on the growth

and metabolism of lambs. Journal of Animal Science, 81, 1332-1339.

SARIKAYA, E., HIGASA, T. & ADACHI, M. e Mikami, B.(2000). Comparison of

degradation abilities of a-and b-amylases on raw starch granules. Process

Biochemistry, 35, 711.

SIDDIQUE, K. H. M. & SYKES, J. 1997. Pulse production in Australia past, present and

future. Australian Journal of Experimental Agriculture, 37, 103-111.

SINGH, J., DARTOIS, A. & KAUR, L. 2010. Starch digestibility in food matrix: A review.

Trends in Food Science & Technology, 21, 168-180.

SOPADE, P. & OKONMAH, G. 1993. The influence of physicochemical properties of foods

on their water absorption behaviour: A quantitative approach. ASEAN Food Journal,

8, 32.

SOPADE, P. A. & GIDLEY, M. J. 2009. A Rapid In‐vitro Digestibility Assay Based on

Glucometry for Investigating Kinetics of Starch Digestion. Starch‐Stärke, 61, 245-255.

TANG, C. H., CHEN, L. & MA, C. Y. 2009. Thermal aggregation, amino acid composition

and in vitro digestibility of vicilin-rich protein isolates from three Phaseolus legumes:

A comparative study. Food chemistry, 113, 957-963.

TESTER, R. & MORRISON, W. 1994. Properties of damaged starch granules. V.

Composition and swelling of fractions of wheat starch in water at various

temperatures. Journal of Cereal Science, 20, 175-181.

TINUS, T., DAMOUR, M., VAN RIEL, V. & SOPADE, P. 2012. Particle size-starch-protein

digestibility relationships in cowpea (Vigna unguiculata). Journal of Food

Engineering, 113, 254-264.

VASCONCELOS, I. M., MAIA, F. M. M., FARIAS, D. F., CAMPELLO, C. C.,

CARVALHO, A. F. U., DE AZEVEDO MOREIRA, R. & DE OLIVEIRA, J. T. A.


61

2010. Protein fractions, amino acid composition and antinutritional constituents of

high-yielding cowpea cultivars. Journal of food composition and analysis, 23, 54-60.

VIEIRA, F. C. & SARMENTO, S. 2008. Heat‐Moisture Treatment and Enzymatic

Digestibility of Peruvian Carrot, Sweet Potato and Ginger Starches. Starch‐Stärke, 60,

223-232.

WARAMBOI, J. G., GIDLEY, M. J. & SOPADE, P. A. 2012. Kinetics of starch digestion in

sweetpotato flours from Papua New Guinean and Australian cultivars. Carbohydrate

Polymers, 87, 461-470.

WEURDING, R. E., VELDMAN, A., VEEN, W. A. G., VAN DER AAR, P. J. &

VERSTEGEN, M. W. A. 2001. In vitro starch digestion correlates well with rate and

extent of starch digestion in broiler chickens. The Journal of nutrition, 131, 2336-

2342.

WHITNEY, E. & ROLFES, S. R. 2011. Understanding Nutrition, United States, Wadsworth

Cengage Learning America.

ZHANG, Y., LI, Y., QIU, F. & QIU, Z. 2010. Comparative analysis of the human urinary

proteome by 1D SDS-PAGE and chip-HPLC-MS/MS identification of the AACT

putative urinary biomarker. Journal of Chromatography B, 878, 3395-3401.


62

Appendices

Appendix A: Cryo-milling condition and particle size distribution of the samples.

Sample Sample Cryo-milling a


code Cycle [min.] d(v, 0.1)b

d(v, 0.5)b

d(v, 0.9)b

d (4, 3)b

Barley 3763 B37CG3 1 19 340 1193 482

Barley 3763 B37CG2 2 17 264 1192 455

Barley 3763 B37CG4 3 15 229 1096 403

Barley 3763 B37CG5 4 15 194 1012 392

Barley 3763 B37CG1 6 7 40 319 108

Barley 3873 B38CG3 1 20 231 985 381

Barley 3873 B38CG2 2 10 54 374 133

Barley 3873 B38CG4 3 11 52 322 118

Barley 3873 B38CG5 4 8 38 227 83

Barley 3873 B38CG1 6 8 37 186 71

Barley 3933 B39CG3 1 10 69 471 167

Barley 3933 B39CG2 2 6 31 267 94

Barley 3933 B39CG4 3 5 27 218 76

Barley 3933 B39CG5 4 4 24 173 60

Barley 3933 B39CG1 6 3 19 128 44

Hard Wheat HWCG3 1 24 181 823 321

Hard Wheat HWCG2 2 13 68 289 119




Soft Wheat SWCG3 1 18 140 814 295

Soft Wheat SWCG2 2 13 76 401 149




Sprouted Wheat SPWCG3 1 14 115 622 234





Mandelup Lupins MLCG3 1 12 275 1092 422







63

Coromup Lupins CLCG3 1 12 223 924 357





Gunyidi Lupins GLCG3 1 19 254 1112 431





Field peas 1 FP1CG3 1 8 63 785 263

Field peas 1 FP1CG2 2 7 31 309 101

Field peas 1 FP1CG4 3 5 25 227 73

Field peas 1 FP1CG5 4 6 25 200 65

Field peas 1 FP1CG1 6 5 23 146 51

Field peas 12 FP12CG3 1 10 60 589 204

Field peas 12 FP12CG2 2 6 33 314 104

Field peas 12 FP12CG4 3 5 28 258 85

Field peas 12 FP12CG5 4 6 28 201 70

Field peas 12 FP12CG1 6 4 24 183 61

Field peas 17 FP17CG3 1 12 146 796 293

Field peas 17 FP17CG2 2 8 43 415 144

Field peas 17 FP17CG4 3 5 26 230 79

Field peas 17 FP17CG5 4 6 28 236 76

Field peas 17 FP17CG1 6 5 26 201 68

Field peas 2 FP2CG3 1 12 102 895 279

Field peas 2 FP2CG2 2 11 45 797 246

Field peas 2 FP2CG4 3 7 32 301 100

Field peas 2 FP2CG5 4 6 26 196 65

Field peas 2 FP2CG1 6 5 25 161 55

Field peas 4 FP4CG3 1 11 96 772 271

Field peas 4 FP4CG2 2 8 38 380 128

Field peas 4 FP4CG4 3 6 32 263 86

Field peas 4 FP4CG5 4 6 29 200 69

Field peas 4 FP4CG1 6 5 25 164 59

Field peas 7 FP7CG3 1 7 39 488 162

Field peas 7 FP7CG2 2 7 33 293 96

Field peas 7 FP7CG4 3 5 26 229 75

Field peas 7 FP7CG5 4 6 28 218 72

Field peas 7 FP7CG1 6 7 29 178 63

Standard error mean, SEM 0.1 2.6 10.1 7.7 a The number of cycles with 5 min. pre-cooling, 2 min. intermediate (in-between cycles) cooling and 10 s-1 impactor speed



64

Appendix B: Hammer-milling condition and particle size distribution of the samples.

Sample Sample Hammer-

milling a


code Sieve-Pass d(v, 0.1)b

d(v, 0.5)b

d(v, 0.9)b

d (4, 3)b

Barley 3763 B37HM 5 0.5 -1 7 40 319 108

Barley 3763 B37HM 4 1.0 -1 17 229 1012 392

Barley 3763 B37HM 1 2.0 -1 19 340 1193 482

Barley 3763 B37HM 2 2.0 -3 15 264 1192 455

Barley 3763 B37HM 3 2.0 -6 15 194 1096 403

Barley 3873 B38HM 5 0.5 -1 10 48 323 113

Barley 3873 B38HM 4 1.0 -1 13 111 804 286

Barley 3873 B38HM 1 2.0 -1 22 575 1396 627

Barley 3873 B38HM 2 2.0 -3 22 385 1214 508

Barley 3873 B38HM 3 2.0 -6 15 184 1147 416

Barley 3933 B39HM 5 0.5 -1 10 52 409 144

Barley 3933 B39HM 4 1.0 -1 14 196 930 352

Barley 3933 B39HM 1 2.0 -1 18 405 1268 526

Barley 3933 B39HM 2 2.0 -3 14 268 1109 426

Barley 3933 B39HM 3 2.0 -6 13 189 995 366

Hard Wheat HWHM 5 0.5 -1 17 92 334 139

Hard Wheat HWHM 4 1.0 -1 22 143 800 289

Hard Wheat HWHM 1 2.0 -1 47 329 1190 495

Hard Wheat HWHM 2 2.0 -3 33 252 1095 435

Hard Wheat HWHM 3 2.0 -6 20 117 818 301

Soft Wheat SWHM 5 0.5 -1 11 63 407 143

Soft Wheat SWHM 4 1.0 -1 13 72 779 255

Soft Wheat SWHM 1 2.0 -1 22 335 1225 491

Soft Wheat SWHM 2 2.0 -3 20 311 1173 465

Soft Wheat SWHM 3 2.0 -6 17 131 1072 379

Sprouted Wheat SPWHM 5 0.5 -1 13 87 462 176





Mandelup Lupins MLHM 5 0.5 -1 10 63 401 141 Mandelup Lupins MLHM 4 1.0 -1 35 319 1081 450 Mandelup Lupins MLHM 1 2.0 -1 47 548 1376 626

Mandelup Lupins MLHM 2 2.0 -3 33 574 1398 606

Mandelup Lupins MLHM 3 2.0 -6 13 191 1223 446


65

Coromup Lupins CLHM 5 0.5 -1 8 54 321 115





Gunyidi Lupins GLHM 5 0.5 -1 15 85 421 158





Field peas 1 FP1HM 5 0.5 -1 9 30 160 71

Field peas 1 FP1HM 4 1.0 -1 12 51 804 220

Field peas 1 FP1HM 1 2.0 -1 12 57 1136 344

Field peas 1 FP1HM 2 2.0 -3 10 37 1036 283

Field peas 1 FP1HM 3 2.0 -6 10 36 811 249

Field peas 12 FP12HM 5 0.5 -1 11 35 229 82

Field peas 12 FP12HM 4 1.0 -1 13 53 568 181

Field peas 12 FP12HM 1 2.0 -1 16 161 1230 434

Field peas 12 FP12HM 2 2.0 -3 12 41 898 235

Field peas 12 FP12HM 3 2.0 -6 9 35 326 126

Field peas 17 FP17HM 5 0.5 -1 9 30 199 72

Field peas 17 FP17HM 4 1.0 -1 13 67 910 286 Field peas 17 FP17HM 1 2.0 -1 13 97 1197 399

Field peas 17 FP17HM 2 2.0 -3 12 65 1207 386

Field peas 17 FP17HM 3 2.0 -6 10 37 1051 297 Field peas 2 FP2HM 5 0.5 -1 9 34 194 76

Field peas 2 FP2HM 4 1.0 -1 13 47 604 185

Field peas 2 FP2HM 1 2.0 -1 12 59 1052 323

Field peas 2 FP2HM 2 2.0 -3 11 45 895 246

Field peas 2 FP2HM 3 2.0 -6 8 34 752 193

Field peas 4 FP4HM 5 0.5 -1 11 34 163 69

Field peas 4 FP4HM 4 1.0 -1 11 40 861 228

Field peas 4 FP4HM 1 2.0 -1 14 99 1204 403

Field peas 4 FP4HM 2 2.0 -3 12 50 1103 334

Field peas 4 FP4HM 3 2.0 -6 14 74 920 304

Field peas 7 FP7HM 5 0.5 -1 10 33 120 65

Field peas 7 FP7HM 4 1.0 -1 13 47 598 181

Field peas 7 FP7HM 1 2.0 -1 16 73 959 294

Field peas 7 FP7HM 2 2.0 -3 14 58 913 250

Field peas 7 FP7HM 3 2.0 -6 13 45 835 238

Standard error mean, SEM 0.4 8.4 21.2 7.9 a The retention sieve and number of passes.



66

Appendix C: Parameter of the first order kinetic model for starch digestion



)

Barley 3764 CG 1 4.091 100.00 0.01166

Barley 3764 CG 2 2.658 100.00 0.00770

Barley 3764 CG 3 3.459 100.00 0.00521

Barley 3764 CG 4 3.219 88.66 0.00933

Barley 3764 CG 5 4.096 94.94 0.01069

Barley 3873 CG 1 4.281 98.49 0.00934

Barley 3873 CG 2 4.271 100.00 0.00748

Barley 3873 CG 3 4.287 79.30 0.00542

Barley 3873 CG 4 4.171 99.85 0.00765

Barley 3873 CG 5 10.453 100.00 0.00786

Barley 3933 CG 1 7.356 100.00 0.01197

Barley 3933 CG 2 1.865 100.00 0.01099

Barley 3933 CG 3 2.668 100.00 0.01071

Barley 3933 CG 4 3.278 100.00 0.01167

Barley 3933 CG 5 4.546 100.00 0.01193

Hard Wheat CG 1 4.570 58.59 0.01219

Hard Wheat CG 2 4.945 45.31 0.01071

Hard Wheat CG 3 1.692 14.06 0.00957

Hard Wheat CG 4 5.596 49.22 0.01114

Hard Wheat CG 5 6.442 53.19 0.01121

Soft Wheat CG 1 3.904 61.33 0.01258

Soft Wheat CG 2 4.190 53.99 0.00919

Soft Wheat CG 3 3.480 45.87 0.00729

Soft Wheat CG 4 4.763 55.15 0.01061

Soft Wheat CG 5 4.589 55.64 0.01093

Sprouted Wheat CG 1 4.693 78.59 0.01105






67

Appendix D: Parameter of the first order kinetic model for starch digestion


Hammer-Milling (g/100 g dry starch) (g/100 g dry starch) (min-1

)

Barley 3764 HM 1 5.094 76.49 0.00484

Barley 3764 HM 2 3.871 73.65 0.00866

Barley 3764 HM 3 5.297 94.94 0.00932

Barley 3764 HM 4 3.999 92.87 0.01100

Barley 3764 HM 5 4.377 96.60 0.01328

Barley 3873 HM 1 3.821 87.88 0.00335

Barley 3873 HM 2 5.923 91.45 0.00478

Barley 3873 HM 3 3.297 82.68 0.00735

Barley 3873 HM 4 6.081 96.77 0.00838

Barley 3873 HM 5 3.694 100.00 0.01118

Barley 3933 HM 1 2.813 100.00 0.00802

Barley 3933 HM 2 2.178 100.00 0.00886

Barley 3933 HM 3 1.917 100.00 0.01015

Barley 3933 HM 4 3.398 100.00 0.01075

Barley 3933 HM 5 3.337 100.00 0.01363

Hard Wheat HM 1 5.154 54.53 0.00969

Hard Wheat HM 2 5.237 39.77 0.01018

Hard Wheat HM 3 6.159 44.49 0.01113

Hard Wheat HM 4 5.984 44.92 0.01373

Hard Wheat HM 5 6.069 56.15 0.01445

Soft Wheat HM 1 3.165 40.09 0.00853

Soft Wheat HM 2 3.414 45.90 0.00977

Soft Wheat HM 3 4.080 48.18 0.01060

Soft Wheat HM 4 4.720 56.39 0.01225

Soft Wheat HM 5 4.236 66.70 0.01371

Sprouted Wheat HM 1 4.643 48.81 0.00717






68

Appendix E: The parameters of the modified first order kinetic model for

protein digestion in cryo-milled field peas

Sample Sample code

Average

particle size IVPD

(g/100 g dry protein)

KPR x 10-3

(min-1

)

d [4,3], µm

Barley 3763 B37CG 3 481.8 77.9 156.4

Barley 3763 B37CG 2 454.7 79.9 181.2

Barley 3763 B37CG 4 403.0 80.7 187.6

Barley 3763 B37CG 5 392.4 80.7 224.1

Barley 3763 B37CG 1 107.8 81.3 307.5

Barley 3873 B38CG 3 380.7 76.9 182.3

Barley 3873 B38CG 2 133.0 77.3 190.0

Barley 3873 B38CG 4 117.7 78.2 260.2

Barley 3873 B38CG 5 82.9 79.3 264.2

Barley 3873 B38CG 1 70.9 79.9 371.8

Barley 3933 B39CG 3 166.7 79.4 184.2

Barley 3933 B39CG 2 93.7 80.2 271.5

Barley 3933 B39CG 4 76.1 80.6 300.5

Barley 3933 B39CG 5 60.3 81.6 303.4

Barley 3933 B39CG 1 43.7 83.2 314.6

Coromup Lupins CLCG 3 356.6 84.7 351.7





Gunyidi Lupins GLCG 3 431.5 75.5 143.7





Hard Wheat HWCG 3 321.1 78.2 194.0

Hard Wheat HWCG 2 119.0 79.3 218.8

Hard Wheat HWCG 4 99.6 79.4 256.6

Hard Wheat HWCG 5 93.7 83.0 300.5

Hard Wheat HWCG 1 75.5 85.2 308.3

Mandelup Lupins MLCG 3 421.7 80.8 173.1





Field peas 1 CP1CG 3 263.3 80.7 228.7

Field peas 1 CP1CG 2 101.0 82.2 290.3

Field peas 1 CP1CG 4 72.5 82.4 254.0

Field peas 1 CP1CG 5 65.3 84.3 295.0

Field peas 1 CP1CG 1 51.1 84.5 391.1

Field peas 12 CP12CG 3 204.4 78.6 176.5


69

Field peas 12 CP12CG 2 104.3 80.4 198.7

Field peas 12 CP12CG 4 84.7 80.7 199.8

Field peas 12 CP12CG 5 70.0 81.0 231.7

Field peas 12 CP12CG 1 61.5 83.5 243.6

Field peas 17 CP17CG 3 293.3 78.4 114.2

Field peas 17 CP17CG 2 143.8 79.0 172.2

Field peas 17 CP17CG 4 78.8 79.4 172.7

Field peas 17 CP17CG 5 75.9 79.7 253.9

Field peas 17 CP17CG 1 68.0 80.4 300.5

Field peas 2 CP2CG 3 279.1 80.2 186.1

Field peas 2 CP2CG 2 246.1 82.0 214.3

Field peas 2 CP2CG 4 99.6 82.3 232.4

Field peas 2 CP2CG 5 64.7 82.6 277.5

Field peas 2 CP2CG 1 55.4 82.8 307.6

Field peas 4 CP4CG 3 271.0 77.5 155.6

Field peas 4 CP4CG 2 128.2 77.9 179.1

Field peas 4 CP4CG 4 86.3 78.5 193.0

Field peas 4 CP4CG 5 69.1 78.9 196.4

Field peas 4 CP4CG 1 58.8 79.2 210.2

Field peas 7 CP7CG 3 161.9 76.2 273.4

Field peas 7 CP7CG 2 96.4 77.7 282.8

Field peas 7 CP7CG 4 74.7 79.9 302.1

Field peas 7 CP7CG 5 72.3 80.1 330.8

Field peas 7 CP7CG 1 63.5 80.3 348.1

Soft Wheat SWCG 3 294.6 78.1 264.9

Soft Wheat SWCG 2 149.0 85.5 304.9

Soft Wheat SWCG 4 107.3 86.7 320.7

Soft Wheat SWCG 5 73.2 87.8 335.6

Soft Wheat SWCG 1 70.1 87.9 378.5

Sprouted Wheat SPWCG 3 233.9 77.9 205.4






70

Appendix F: The parameters of the modified first order kinetic model for

protein digestion in hammer-milled field peas

Sample Sample code

Average

particle size IVPD KPR x 10-3

(min-1

)

d [4,3], µm (g/100 g dry protein)

Barley 3763 B37HM 5 481.8 82.9 309.3

Barley 3763 B37HM 4 454.7 81.6 274.5

Barley 3763 B37HM 1 403.0 80.2 194.2

Barley 3763 B37HM 2 392.4 81.0 220.8

Barley 3763 B37HM 3 107.8 81.3 228.4

Barley 3873 B38HM 5 380.7 84.2 435.0

Barley 3873 B38HM 4 133.0 83.3 230.2

Barley 3873 B38HM 1 117.7 80.1 184.0

Barley 3873 B38HM 2 82.9 81.3 184.3

Barley 3873 B38HM 3 70.9 82.4 191.3

Barley 3933 B39HM 5 166.7 85.9 472.4

Barley 3933 B39HM 4 93.7 85.6 427.4

Barley 3933 B39HM 1 76.1 84.7 309.5

Barley 3933 B39HM 2 60.3 85.1 389.8

Barley 3933 B39HM 3 43.7 85.6 415.4

Coromup Lupins CLHM 5 356.6 87.7 523.5





Gunyidi Lupins GLHM 5 431.5 83.4 352.8





Hard Wheat HWHM 5 321.1 86.3 383.8

Hard Wheat HWHM 4 119.0 85.3 310.8

Hard Wheat HWHM 1 99.6 77.9 255.4

Hard Wheat HWHM 2 93.7 78.0 261.1

Hard Wheat HWHM 3 75.5 84.9 300.5

Mandelup Lupins MLHM 5 421.7 85.6 376.8





Field peas 1 CP1HM 5 263.3 85.1 333.9

Field peas 1 CP1HM 4 101.0 84.8 327.7

Field peas 1 CP1HM 1 72.5 81.0 275.4

Field peas 1 CP1HM 2 65.3 82.9 279.0

Field peas 1 CP1HM 3 51.1 84.1 285.0


71

Field peas 12 CP12HM 5 204.4 84.2 233.4

Field peas 12 CP12HM 4 104.3 81.2 234.3

Field peas 12 CP12HM 1 84.7 79.5 185.1

Field peas 12 CP12HM 2 70.0 80.3 236.9

Field peas 12 CP12HM 3 61.5 80.3 189.5

Field peas 17 CP17HM 5 293.3 83.7 184.1

Field peas 17 CP17HM 4 143.8 83.2 183.9

Field peas 17 CP17HM 1 78.8 79.8 210.1

Field peas 17 CP17HM 2 75.9 80.5 230.6

Field peas 17 CP17HM 3 68.0 81.1 174.6

Field peas 2 CP2HM 5 279.1 86.1 516.0

Field peas 2 CP2HM 4 246.1 84.2 362.2

Field peas 2 CP2HM 1 99.6 79.4 297.9

Field peas 2 CP2HM 2 64.7 81.8 300.4

Field peas 2 CP2HM 3 55.4 83.7 339.4

Field peas 4 CP4HM 5 271.0 80.8 217.9

Field peas 4 CP4HM 4 128.2 80.7 415.2

Field peas 4 CP4HM 1 86.3 79.2 268.4

Field peas 4 CP4HM 2 69.1 79.4 228.7

Field peas 4 CP4HM 3 58.8 80.6 299.3

Field peas 7 CP7HM 5 161.9 81.5 260.8

Field peas 7 CP7HM 4 96.4 81.0 256.6

Field peas 7 CP7HM 1 74.7 79.3 164.7

Field peas 7 CP7HM 2 72.3 80.4 185.5

Field peas 7 CP7HM 3 63.5 80.5 282.9

Soft Wheat SWHM 5 294.6 83.7 327.6

Soft Wheat SWHM 4 149.0 86.6 316.8

Soft Wheat SWHM 1 107.3 79.4 233.9

Soft Wheat SWHM 2 73.2 86.2 238.9

Soft Wheat SWHM 3 70.1 86.1 300.4

Sprouted Wheat SPWHM 5 233.9 85.4 300.0





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