Estimatingdough 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
High throughput, reliable and relatively cheap methodscharacterising 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)
Objective, computer-driven small-
and micro-scale functional tests
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
Predictive methods based on chemical/genetic data
Predictive methods based on chemical/genetic data
Contributors: Morell, M.Howitt, C.Newberry, M.CSIRO Plant industry, Canberra, Australia
Appels, R.Ma, W.Murdoch Uni, Perth, Australia
Tömösközi, S.Kemény, S.Balázs, G.BUTE, Budapest, Hungary
Bedő, Z., Láng, L.Juhász, A., Rakszegi, M.,Baracskai
I., Kovács
A.
H.A.S. A.R.I. Martonvásár, HungaryTamás, L.Oszvald, M.ELTE, Budapest, Hungary
Morgounov, A.CIMMYT, Ankara , Turkey
Suter, D.A.I.GWF, Enfield, Australia
Two possible approaches
Industry/marketing application (Protein
Quality
Index
)Integrating protein content with dough parameters to predictend-product quality.Developing a single parameter describing the end-product-specific ‘quality’
of samples
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)
Q = Σαi*(qH)i
i=1
13Q = Σαi*(qH)i
i=1
13
Payne score Payne, P. I., Nightingale, M. A., Krattiger, A. F & Holt, L. M. (1987) The relationships betweenHMW glutenin
subunit composition and t he bread-making quality of british-grown wheat-varieties. J. Sci. Food Agric. 40 51–65.
qH,i
= 0 or 1, indicating thepresence or absence of HMW GS allele i
αi
= factor indicating the contribution ofallele i to quality attribute (Rmax)
Protein
Scoring
System
Q = Σαi*(qH)i
i=1
13Q = Σαi*(qH)i
i=1
13
Q = Σαi*(qH)i +i=1
17Σαi*(qL)j
j=1
16+ Σ Σβi,j*(qH)j *(qL)j
i=1 j=1
17 16Q = Σαi*(qH)i +
i=1
17Q = Σαi*(qH)i +
i=1
17Σαi*(qL)j
j=1
16+ Σ Σβi,j*(qH)j *(qL)j
i=1 j=1
17 16
Payne score
Protein Scoring System
Payne, P. I., Nightingale, M. A., Krattiger, A. F & Holt, L. M. (1987) The relationships betweenHMW glutenin
subunit composition and t he bread-making quality of british-grown wheat-varieties. J. Sci. Food Agric. 40 51–65.
Békés, F., Kemény, S. & Morell, M. K. (2006) An integrated approach to predicting end-
product quality of wheat. Eur. J. Agron. 25, 155–162
qH,i
= 0 or 1, indicating thepresence or absence of HMW GS allele i
αi
= factor indicating the contribution ofallele i to quality attribute (Rmax)
qL,j
= 0 or 1, indicating thepresence or absence of LMW GS allele i
αj
= factor indicating the contribution ofallele j to quality attribute (Rmax)
βi,j
= factor indicating the contribution ofinteraction between alleles i and j
Protein
Scoring
System
Q = Σαi*(qH)i
i=1
13Q = Σαi*(qH)i
i=1
13
Q = Σαi*(qH)i +i=1
17Σαi*(qL)j
j=1
16+ Σ Σβi,j*(qH)j *(qL)j
i=1 j=1
17 16Q = Σαi*(qH)i +
i=1
17Q = Σαi*(qH)i +
i=1
17Σαi*(qL)j
j=1
16+ Σ Σβi,j*(qH)j *(qL)j
i=1 j=1
17 16
Payne score
Protein Scoring System
More alleles are involved, including for example OE7+8*
Payne score versus PSS
Q = Σαi*(qH)i
i=1
13Q = Σαi*(qH)i
i=1
13
Q = Σαi*(qH)i +i=1
17Σαi*(qL)j
j=1
16+ Σ Σβi,j*(qH)j *(qL)j
i=1 j=1
17 16Q = Σαi*(qH)i +
i=1
17Q = Σαi*(qH)i +
i=1
17Σαi*(qL)j
j=1
16+ Σ Σβi,j*(qH)j *(qL)j
i=1 j=1
17 16
Payne score
Protein Scoring System
Both HMW and LMW GS alleles are considered
Payne score versus PSS
Q = Σαi*(qH)i
i=1
13Q = Σαi*(qH)i
i=1
13
Q = Σαi*(qH)i +i=1
17Σαi*(qL)j
j=1
16+ Σ Σβi,j*(qH)j *(qL)j
i=1 j=1
17 16Q = Σαi*(qH)i +
i=1
17Q = Σαi*(qH)i +
i=1
17Σαi*(qL)j
j=1
16+ Σ Σβi,j*(qH)j *(qL)j
i=1 j=1
17 16
Payne score
Protein Scoring System
Beyond the individual effects of alleles, The effects of their interaction is also taken account
Payne score versus PSS
Q = Σαi*(qH)i
i=1
13Q = Σαi*(qH)i
i=1
13
Q = Σαi*(qH)i +i=1
17Σαi*(qL)j
j=1
16+ Σ Σβi,j*(qH)j *(qL)j
i=1 j=1
17 16Q = Σαi*(qH)i +
i=1
17Q = Σαi*(qH)i +
i=1
17Σαi*(qL)j
j=1
16+ Σ Σβi,j*(qH)j *(qL)j
i=1 j=1
17 16
Payne score
Protein Scoring System
Instead of subjective estimation, factors of relative contributions aredetermined by statistical methods
Payne score versus PSS
α
and β
factors can be determinedexperimentally by
in vitro incorporation
method, using wheat and/or rice flours
as ‘base-flour
Payne score versus PSS
Q = Σαi*(qH)i
i=1
13Q = Σαi*(qH)i
i=1
13
Q = Σαi*(qH)i +i=1
17Σαi*(qL)j
j=1
16+ Σ Σβi,j*(qH)j *(qL)j
i=1 j=1
17 16Q = Σαi*(qH)i +
i=1
17Q = Σαi*(qH)i +
i=1
17Σαi*(qL)j
j=1
16+ Σ Σβi,j*(qH)j *(qL)j
i=1 j=1
17 16
Payne score
Protein Scoring System
Predictive equations for bothdough strength (Rmax) and extensibiity (Ext)
Payne score versus PSS
For EXT scores: -
Glu3 >Glu1-
Variation among alleles at any loci is much less than thosefor Rmax score
The contribution of glutenin alleleson dough strength and extensibility
RMAX EXT
0 20 40 60 0 20 40 60
individual HMWLMW
interactive
HMW-HMWLMW-LMWHMW-LMW
Rmax Ext
The contribution of glutenin alleleson dough strength and extensibility
Relative contribution [%] Relative contribution [%]
Application of PSS
qi
= 0 or 1, indicating the presence or absence of HMW GS allele i
Q = Σαi*(qH)i +i=1
17Σαi*(qL)j
j=1
16+ Σ Σβi,j*(qH)j *(qL)j
i=1 j=1
17 16Q = Σαi*(qH)i +
i=1
17Q = Σαi*(qH)i +
i=1
17Σαi*(qL)j
j=1
16+ Σ Σβi,j*(qH)j *(qL)j
i=1 j=1
17 16
Q = the predicted genetic potential of Rmax or Ext
0 200 400 600 800 1000RMAX
0
10
20
30
40
Ext
The ‘biodiversity’ of Rmax and Ext
Glu-1A 3Glu-1B 10Glu-1D 4
Glu-3A 6Glu-3B 5Glu-3D 5
3 x 10 x 4 x 6 x 5 x 5 = 18000
Application of PSS
The ‘biodiversity’
of Rmax and Ext
qi
= 0 or 1, indicating the presence or absence of HMW GS allele i
Q = Σαi*(qH)i +i=1
17Σαi*(qL)j
j=1
16+ Σ Σβi,j*(qH)j *(qL)j
i=1 j=1
17 16Q = Σαi*(qH)i +
i=1
17Q = Σαi*(qH)i +
i=1
17Σαi*(qL)j
j=1
16+ Σ Σβi,j*(qH)j *(qL)j
i=1 j=1
17 16
Q = the predicted genetic potential of Rmax or Ext
Complex quality characterisation of Hungarian wheat cultivars
G. BALÁZS1, A. HARASZTOS1, SZ. SZENDI1, A. BAGDI1, M RAKSZEGI2, L. LÁNG2, Z. BEDŐ2, F. BÉKÉS3 and S. TÖMÖSKÖZI1
1 Budapest University of Technology and Economics (BUTE), Department of Applied Biotechnology and Food Science, Budapest, Hungary; 2 Agricultural Institute, Centre for Agricultural Research, Hungarian Academy of Sciences, Martonvásár, Hungary;
3 FBFD PTY LTD, Beecroft, NSW, Australia
The research work was supported by the HungarianNational Research Fund (OTKA 80292 and OTKA80334) and the Development of breeding,agricultural production and food industrialprocessing system of Pannon wheat varietiesHungarian National Project (TECH-09-A3-2009-0221).
Study outlineOld and new Hungarian wheat cultivars originated from Agricultural Institute of Hungarian Academy of Sciences (Martonvásár, Hungary) have been characterised covering thequalitative 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 understandingabout 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 bycolorimetric method, starch by SDmatic (Chopin Technologies). Water extractable (WE-), and total arabynoxylan (TOT-AX) content by GC-FID, with the hydrolysis and derivatisation ofsugars; 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.
Variety N ame Glu1-A Glu1-B Glu1-D Glu3-A Glu3-BGlu3-
DBANKUTI-1201 2* OE7+8 2+12 f i cBEZOSTAJA-1 2* 7+9 5+10 c c bBANKUTI-1205-RCAT000030 2* 7+9 2+12 a i cDIOSZEGI-N12 1 7+8/7+9 2+12/5+10 a f m FERTODI-293-24-5 1 7+9 2+12 c c dFLEISCHMANN-481GLENLEA 2* OE7+8 5+10 g g cLOVASZPATONAI-407 2* 7+8 5+10 b b bMV CSARDAS b c a c j b
Results
Mixolab is a relatively new complex rheolgicalinstrument from Chopin Technologies. During asingle measurement it is possible to analyze theconventional, mainly protein related doughproperties like dough strength and stability, andwith a temperature program, it is possible tocharacterize the mainly carbohydrate relatedviscous parameters.
Examples: novel methods in the quality measueremtsAllelic composition of glutenin proteins Mixolab
F FB D
Application of PSS
qi
= 0 or 1, indicating the presence or absence of HMW GS allele i
Q = Σαi*(qH)i +i=1
17Σαi*(qL)j
j=1
16+ Σ Σβi,j*(qH)j *(qL)j
i=1 j=1
17 16Q = Σαi*(qH)i +
i=1
17Q = Σαi*(qH)i +
i=1
17Σαi*(qL)j
j=1
16+ Σ Σβi,j*(qH)j *(qL)j
i=1 j=1
17 16
Q = the predicted genetic potential of Rmax or Ext
Tool for breeders to select parent lines
qi = 0 or 1, indicating the concentration of proteins in allele i
Q = the actual dough strength or extensibility of the sample
Q = Σαi*(qH)i +i=1
17Σαi*(qL)j
j=1
16+ Σ Σβi,j*(qH)j *(qL)j
i=1 j=1
17 16Q = Σαi*(qH)i +
i=1
17Q = Σαi*(qH)i +
i=1
17Σαi*(qL)j
j=1
16+ Σ Σβi,j*(qH)j *(qL)j
i=1 j=1
17 16
Application of PSS
Comparison of measured and estimated Rmax and Ext
RM
AX
esrim
ated
0 200 400 600 800Measured RMAX
0
200
400
600
800
R2 = 0.8736 R2 = 0.5589
qi = 0 or 1, indicating the concentration of proteins in allele i
Q = the actual dough strength or extensibility of the sample
Q = Σαi*(qH)i +i=1
17Σαi*(qL)j
j=1
16+ Σ Σβi,j*(qH)j *(qL)j
i=1 j=1
17 16Q = Σαi*(qH)i +
i=1
17Q = Σαi*(qH)i +
i=1
17Σαi*(qL)j
j=1
16+ Σ Σβi,j*(qH)j *(qL)j
i=1 j=1
17 16
Application of PSS
Application of PSSAnomalies in quality parameters of grists
In 10-15% of the cases of commercial gristings
Q - quality parameter of the gristcomponent Q - quality parameter of the i-thi
x - mass fraction of the i-th component i
n - number of components in the grist
V
Q = x*Q + (1-x)* QV U
Sample U 0 20 40 60 80 100Sample V 100 80 60 40 20 0
Q
Q = Σ (xi Σxi = 1i=1 i=1
(x *Qi
)
QU
RmaxLIN
= xu
*Rmaxu
+ xv
*Rmaxv
+ 3 3
+ ΣΣßi,j*(HMW)u,i *(LMW)u,j)i=1 j=1
ßi,j*(HMW) *(LMW) )++( αi*(HMW)u,i(Σ i*(HMW)( i*(HMW)i=1
3Σαj*(LMW)u,j
j=1
j*(LMW)j=1
j*(LMW)j=
3+
3 3+ ΣΣßi,j*(HMW)u,i *(LMW)u,j)
i=1 j=1
ßi,j*(HMW) *(LMW) )ΣΣßi,j*(HMW)u,i *(LMW)u,j)i=1 j=1
ßi,j*(HMW) *(LMW) )++( αi*(HMW)u,i(Σ i*(HMW)( i*(HMW)i=1( αi*(HMW)u,i(Σ i*(HMW)( i*(HMW)i=1
3Σαj*(LMW)u,j
j=1
j*(LMW)j=1
j*(LMW)j=
Σαj*(LMW)u,j
j=1
j*(LMW)j=1
j*(LMW)j=
3Rmax = xu*
+ xv* + +3 3ΣΣßi,j*(HMW)v,i *(LMW)v,j)i=1 j=1
ßi,j*(HMW) *(LMW) )3 3ΣΣßi,j*(HMW)v,i *(LMW)v,j)i=1 j=1
ßi,j*(HMW) *(LMW) )++( αi*(HMW)v,i(Σ i*(HMW)( i*(HMW)i=1
3Σαj*(LMW)v,j
j=1
j*(LMW)j=1
j*(LMW)j=
3
+
+
Linear model
Non‐linear model
3 3ΣΣßi,j*(HMW) u,i *(LMW) v,j)i=1 j=1
ßi,j*(HMW) *(LMW) )xu
*3 3ΣΣßi,j*(HMW) v,i *(LMW) u,j)i=1 j=1
ßi,j*(HMW) *(LMW) )+ xv
*+RmaxLINRmax =
Only interactive components !!!
Inverse problem : optimalisation grist formulation –
looking for x with -
maximum dough strength
-
maximum extensibilityfor a set of components
V
Q = x*Q + ( 1-x)* QV U
Sample U 0 20 40 60 80 100Sample V 100 80 60 40 20 0
Q
QU
Application of PSS
Two possible approaches
Industry/marketing application (Protein
Quality
Index
)Integrating protein content with dough parameters to predictend-product quality.Developing a single parameter describing the end-product-specific ‘quality’
of samples
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)
Control +10% +20%0
100
200
300
400Control flour+ gliadin
+ glutenin
+ gluten
Control +10% +20%60
61
62
63
64
Dou
gh D
evel
opm
ent
Tim
e
Wat
er a
bsor
ptio
n
Prediction of water absorption
Haraszi, R., Gras, P.W., Tömösközi, S., Salgó, A., Békés, F.(2004) The application of a micro Z-arm mixer to characterize mixingproperties and water absorption of wheat flour. Cereal Chem. 81. 555-560.
W.A. = f(protein contentW.A. = f(Glu/Gli)
Multiple regression models
r2 = 0.110 r2
= 0.384
r2 = 0.235
r2 = 0.143
r2 = 0.173 r2
= 0.084 r2 = 0.035 r2
= 0.427
r2 = 0.505
Best individual model withsoluble proteins in the flour (AG*)
(soluble proteins*flour protein/100)
ProteinSoluble proteins
Total AX
Starch Damage
(AG)*
* * * 0.547* * * * 0.576
* * 0.558* * 0.611* * * 0.643
r2 = 0.643
r2
Prediction of water absorption
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 directand inhibitory/synergistic interactive effects
- Integrated approaches are needed to deal with these
complex relationships
Thank you very much