International Gluten Workshop, 11th; Beijing (China); 12-15 Aug 2012

1. Estimating
dough properties and end-product quality
from flour composition
F. BÉKÉS1, W. MA2 and S. TÖMÖSKÖZI3
1FBFD PTY LTD, Beecroft, NSW, Australia,
2State Agricultural Biotechnology Centre, Murdoch University WA, Australia
3Budapest University of Technology and Economics,
Department of Applied Biotechnology and Food Science, Budapest, Hungary

5. High throughput, reliable and relatively cheap methods
characterising functional properties and end-products quality
• Objective, computer-driven small- and micro-scale functional tests
• Predictive methods based on chemical/genetic data
• Spectroscopy based predictive methods (NIR)

6. Objective, computer-driven small- and
micro-scale functional tests

7. Objective, computer-driven small- and
micro-scale functional tests
S. TÖMÖSKÖZI1, SZ. SZENDI1, A. BAGDI, A. HARASZTOS1, B. BALÁZS1, R. APPELS2
and F. BÉKÉS3
New possibilities in micro-scale wheat quality
characterisation: micro-gluten determination and starch
isolation

8. Predictive methods based on chemical/genetic data

9. Predictive methods based on chemical/genetic data

10. Contributors: Morell, M. Tömösközi, S.
Howitt, C. Kemény, S.
Newberry, M. Balázs, G.
CSIRO Plant industry, Canberra, Australia BUTE, Budapest, Hungary
Appels, R. Bedő, Z., Láng, L.
Ma, W. Juhász, A., Rakszegi, M.,
Murdoch Uni, Perth, Australia Baracskai I., Kovács A.
Tamás, L. H.A.S. A.R.I. Martonvásár, Hungary
Oszvald, M. Morgounov, A.
ELTE, Budapest, Hungary
CIMMYT, Ankara , Turkey
Suter, D.A.I.
GWF, Enfield, Australia

14. Two possible approaches
Research /breeding application (Protein Scoring System)
Developing the mathematical models describing dough properties,
based on the contribution of the storage protein genes and their expression levels
Quality attributes* = f (Overall protein content,
Contribution of different individual alleles,
Interactions between alleles,
Relative expression levels)
Industry/marketing application (Protein Quality Index)
Integrating protein content with dough parameters to predict
end-product quality.
Developing a single parameter describing the end-product-specific ‘quality’ of samples

15. Protein Scoring System
Payne score Payne, P. I., Nightingale, M. A., Krattiger, A. F & Holt, L. M. (1987) The relationships between
HMW glutenin subunit composition and t he bread-making quality of british-grown wheat-varieties.
J. Sci. Food Agric. 40 51–65.
i=1
qH,i = 0 or 1, indicating the
Q = Σαi*(qH)i presence or absence of HMW GS allele i
13 αi = factor indicating the contribution of
allele i to quality attribute (Rmax)

16. Protein Scoring System
Payne score Payne, P. I., Nightingale, M. A., Krattiger, A. F & Holt, L. M. (1987) The relationships between
HMW glutenin subunit composition and t he bread-making quality of british-grown wheat-varieties.
J. Sci. Food Agric. 40 51–65.
i=1
qH,i = 0 or 1, indicating the
Q = Σαi*(qH)i presence or absence of HMW GS allele i
13 αi = factor indicating the contribution of
allele i to quality attribute (Rmax)
Protein Scoring System Békés, F., Kemény, S. & Morell, M. K. (2006) An integrated approach to predicting end-
product quality of wheat. Eur. J. Agron. 25, 155–162
i=1 j=1 i=1 j=1
Q = Σαi*(qH)i + αi*(qL)+ Σ
Σ j Σβi,j*(qH)j *(qL)j
17 16 17 16
qL,j = 0 or 1, indicating the
presence or absence of LMW GS allele i
αj = factor indicating the contribution of
allele j to quality attribute (Rmax)
βi,j = factor indicating the contribution of
interaction between alleles i and j

17. Payne score versus PSS
Payne score
i=1
Q = Σαi*(qH)i
13
More alleles are involved,
including for example OE7+8*
Protein Scoring System
i=1 j=1 i=1 j=1
Q = Σαi*(qH)i + αi*(qL)+ Σ
Σ j Σβi,j*(qH)j *(qL)j
17 16 17 16

18. Payne score versus PSS
Payne score
i=1
Q = Σαi*(qH)i
13
Both HMW and LMW GS alleles
are considered
Protein Scoring System
i=1 j=1 i=1 j=1
Q = Σαi*(qH)i + αi*(qL)+ Σ
Σ j Σβi,j*(qH)j *(qL)j
17 16 17 16

19. Payne score versus PSS
Payne score
i=1
Q = Σαi*(qH)i
13
Beyond the individual effects of alleles,
The effects of their interaction is also taken account
Protein Scoring System
i=1 j=1 i=1 j=1
Q = Σαi*(qH)i + αi*(qL)+ Σ
Σ j Σβi,j*(qH)j *(qL)j
17 16 17 16

20. Payne score versus PSS
Payne score
i=1 Instead of subjective estimation,
Q = Σαi*(qH)i factors of relative contributions are
determined by statistical methods
13
Protein Scoring System
i=1 j=1 i=1 j=1
Q = Σαi*(qH)i + αi*(qL)+ Σ
Σ j Σβi,j*(qH)j *(qL)j
17 16 17 16

21. Payne score versus PSS
α and β factors can be determined
experimentally by
in vitro incorporation method,
using wheat and/or rice flours
as ‘base-flour

22. Payne score versus PSS
Payne score
i=1
Q = Σαi*(qH)i
13
Predictive equations for both
dough strength (Rmax) and extensibiity (Ext)
Protein Scoring System
i=1 j=1 i=1 j=1
Q = Σαi*(qH)i + αi*(qL)+ Σ
Σ j Σβi,j*(qH)j *(qL)j
17 16 17 16

23. The contribution of glutenin alleles
on dough strength and extensibility
RMAX EXT
For EXT scores: - Glu3 >Glu1
- Variation among alleles at any loci is much less than those
for Rmax score

24. The contribution of glutenin alleles
on dough strength and extensibility
Rmax Ext
Relative contribution [%] Relative contribution [%]
0 20 40 60 0 20 40 60
individual
HMW
LMW
interactive
HMW-HMW
LMW-LMW
HMW-LMW

25. Application of PSS
i=1 j=1 i=1 j=1
Q = Σαi*(qH)i + αi*(qL)+ Σ Σβi,j*(qH)j *(qL)j
Σ j
17 16 17 16
qi = 0 or 1, indicating the presence or absence of HMW GS allele i
Q = the predicted genetic potential of Rmax or Ext

26. Application of PSS
i=1 j=1 i=1 j=1
Q = Σαi*(qH)i + αi*(qL)+ Σ Σβi,j*(qH)j *(qL)j
Σ j
17 16 17 16
qi = 0 or 1, indicating the presence or absence of HMW GS allele i
Q = the predicted genetic potential of Rmax or Ext
The ‘biodiversity’ of Rmax and Ext
The ‘biodiversity’ of Rmax and Ext
40
Glu-1A 3
Glu-1B 10
30 Glu-1D 4
Glu-3A 6
Ext
20
Glu-3B 5
Glu-3D 5
10
3 x 10 x 4 x 6 x 5 x 5 = 18000
0
0 200 400 600 800 1000
RMAX

27. Application of PSS
i=1 j=1 i=1 j=1
Q = Σαi*(qH)i + αi*(qL)+ Σ Σβi,j*(qH)j *(qL)j
Σ j
17 16 17 16
qi = 0 or 1, indicating the presence or absence of HMW GS allele i
Q = the predicted genetic potential of Rmax or Ext
Tool for breeders to select parent lines
Complex quality characterisation of Hungarian
wheat cultivars
FF
G. BALÁZS1, A. HARASZTOS1, SZ. SZENDI1, A. BAGDI1, M RAKSZEGI2, L. BD
The research work was supported by the Hungarian
LÁNG2, Z. BEDŐ2, F. BÉKÉS3 and S. TÖMÖSKÖZI1 National Research Fund (OTKA 80292 and OTKA
80334) and the Development of breeding,
1 Budapest University of Technology and Economics (BUTE), Department of Applied Biotechnology and Food Science, Budapest, agricultural production and food industrial
Hungary; 2 Agricultural Institute, Centre for Agricultural Research, Hungarian Academy of Sciences, Martonvásár, Hungary; processing system of Pannon wheat varieties
3 FBFD PTY LTD, Beecroft, NSW, Australia Hungarian National Project (TECH-09-A3-2009-
0221).
Study outline
Old and new Hungarian wheat cultivars originated from Agricultural Institute of Hungarian Academy of Sciences (Martonvásár, Hungary) have been characterised covering the
qualitative and quantitative analysis of gluten and non-gluten proteins as well as the starchy and non-starchy carbohydrates:
→ to typify the genetic potential of these lines
→ looking for correlations between the results of different conventional, and novel analytical methods
→ and get an improved understanding about rheological parameters and biochemical background.
The following measurements were applied: lab-on-a-chip instrument (LOC), Bioanalyzer 2100 from Agilent, SE- and RP- HPLC for protein profiling; Amylase/amylopectin ratio by
colorimetric method, starch by SDmatic (Chopin Technologies). Water extractable (WE-), and total arabynoxylan (TOT-AX) content by GC-FID, with the hydrolysis and derivatisation of
sugars; and rheological tests, such as MixoLab (Chopin Technologies), RVA (Rapid Visco Analyser, Perten Instruments.), and micro sized version of Zeleny sedimentation test (Sedicom,
BME-Labintern Ltd, Hungary).
Some of the results presented on this poster below.
Results Examples: novel methods in the quality measueremts
Allelic composition of glutenin proteins Mixolab
Glu3-
Variety N ame
Glu1-A Glu1-B Glu1-D Glu3-A Glu3-B D Mixolab is a relatively new complex rheolgical
BANKUTI-1201 2* OE7+8 2+12 f i c instrument from Chopin Technologies. During a
BEZOS A-1
TAJ 2* 7+9 5+10 c c b single measurement it is possible to analyze the
BANKUTI-1205-
RCAT000030 2* 7+9 2+12 a i c conventional, mainly protein related dough
DIOS ZEGI-N12 1 7+8/7+9 2+12/5+10 a f m properties like dough strength and stability, and
FERTODI-293-24-5 1 7+9 2+12 c c d with a temperature program, it is possible to
FLEISCHMANN-481 characterize the mainly carbohydrate related
GLENLEA 2* OE7+8 5+10 g g c viscous parameters.
LOVAS ATONAI-407
ZP 2* 7+8 5+10 b b b
MV CS ARDAS b c a c j b

28. Application of PSS
i=1 j=1 i=1 j=1
Q = Σαi*(qH)i + αi*(qL)+ Σ Σβi,j*(qH)j *(qL)j
Σ j
17 16 17 16
qi = 0 or 1, indicating the concentration of proteins in allele i
Q = the actual dough strength or extensibility of the sample

29. Application of PSS
i=1 j=1 i=1 j=1
Q = Σαi*(qH)i + αi*(qL)+ Σ Σβi,j*(qH)j *(qL)j
Σ j
17 16 17 16
qi = 0 or 1, indicating the concentration of proteins in allele i
Q = the actual dough strength or extensibility of the sample
Comparison of measured and estimated
Rmax and Ext
800
600
RMAX esrimated
400
200
R2 = 0.8736 R2 = 0.5589
0
0 200 400 600 800
Measured RMAX

30. Application of PSS
Anomalies in quality parameters of grists
In 10-15% of the cases of commercial gristings
Q = x*Q + (1-x)* Q
V U
Q= Σ (x *Q )
i=1
i i Σx = 1
i=1
i
QU
Q - quality parameter of the grist
Q - quality parameter of the i-th component
x
i
- mass fraction of the i-th component
QV
i
n - number of components in the grist
Sample U 0 20 40 60 80 100
Sample V 100 80 60 40 20 0

31. Application of PSS
Q = x*Q + ( 1-x)* Q
V U Linear model
RmaxLIN = xu*Rmaxu + xv*Rmaxv
QU i=1 j=
j=1 i=1 j=1
Rmax = xu* (Σα *(HMW) +Σα *(LMW) + ΣΣß *(HMW)
3
i u,i
3
j u,j
3 3
i,j u,i *(LMW) u,j )+
i=1 j=
j=1 i=1 j=1
+ x * (Σα *(HMW) +Σα *(LMW) + ΣΣß *(HMW) )+
QV *(LMW) v,j
v i v,i j v,j i,j v,i
3 3 3 3
Sample U 0 20 40 60 80 100
Sample V 100 80 60 40 20 0
Non‐linear model
i=1 j=1 i=1 j=1
Rmax = RmaxLIN
3 3
)
+ xu*ΣΣß i,j*(HMW) u,i *(LMW) v,j + xv* ΣΣß i,j*(HMW) v,i *(LMW) u,j
3 3
)
Only interactive components !!!
Inverse problem : optimalisation grist formulation – looking for x with
- maximum dough strength
- maximum extensibility
for a set of components

32. Two possible approaches
Research /breeding application (Protein Scoring System)
Developing the mathematical models describing dough properties,
based on the contribution of the storage protein genes and their expression levels
Quality attributes* = f (Overall protein content,
Contribution of different individual alleles,
Interactions between alleles,
Relative expression levels)
Industry/marketing application (Protein Quality Index)
Integrating protein content with dough parameters to predict
end-product quality.
Developing a single parameter describing the end-product-specific ‘quality’ of samples

33. Prediction of water absorption
400
64
Dough Development Time
Control flour
Water absorption
300 + gliadin
63
+ gluten
200 62 + glutenin
100 61
0 60
Control +10% +20% Control +10% +20%
Haraszi, R., Gras, P.W., Tömösközi, S., Salgó, A., Békés, F.(2004)
The application of a micro Z-arm mixer to characterize mixing
properties and water absorption of wheat flour. Cereal Chem. 81. 555-560.
W.A. = f(protein content
W.A. = f(Glu/Gli)

34. Prediction of water absorption
2 r = 0.235
r2 = 0.110 r2 = 0.384 r2 = 0.143
r2 = 0.173 r2 = 0.084 r2 = 0.035 r2 = 0.427
Best individual model with Multiple regression models
soluble proteins in the flour (AG*)
(soluble proteins*flour protein/100)
Soluble Total Starch
Protein
proteins AX Damage
(AG)*
r2
* * * 0.547
* * * * 0.576
* * 0.558
r2 = 0.505 * * 0.611
r2 = 0.643
* * * 0.643

35. Conclusion:
- Quality related molecular and traditional research is essential for
- satisfying customer’s need
- helping to solve the problems of the industry
- breeding to produce new, better cultivars
- All quality attributes are multigene traits with direct
and inhibitory/synergistic interactive effects
- Integrated approaches are needed to deal with these
complex relationships

36. Thank you very much