Estimating dough properties and end-product quality from flour composition

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-


Objective, computer-driven small-


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

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


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


17Σαi*(qL)j

j=1


i=1 j=1

17 16Q = Σαi*(qH)i +

i=1

17Q = Σαi*(qH)i +

i=1

17Σαi*(qL)j

j=1


i=1 j=1

17 16

Payne score


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


17Σαi*(qL)j

j=1


i=1 j=1

17 16Q = Σαi*(qH)i +

i=1

17Q = Σαi*(qH)i +

i=1

17Σαi*(qL)j

j=1


i=1 j=1

17 16

Payne score


Both HMW and LMW GS alleles are considered


Q = Σαi*(qH)i

i=1

13Q = Σαi*(qH)i

i=1

13


17Σαi*(qL)j

j=1


i=1 j=1

17 16Q = Σαi*(qH)i +

i=1

17Q = Σαi*(qH)i +

i=1

17Σαi*(qL)j

j=1


i=1 j=1

17 16

Payne score


Beyond the individual effects of alleles, The effects of their interaction is also taken account


Q = Σαi*(qH)i

i=1

13Q = Σαi*(qH)i

i=1

13


17Σαi*(qL)j

j=1


i=1 j=1

17 16Q = Σαi*(qH)i +

i=1

17Q = Σαi*(qH)i +

i=1

17Σαi*(qL)j

j=1


i=1 j=1

17 16

Payne score


Instead of subjective estimation, factors of relative contributions aredetermined by statistical methods


α

and β

factors can be determinedexperimentally by

in vitro incorporation

method, using wheat and/or rice flours

as ‘base-flour


Q = Σαi*(qH)i

i=1

13Q = Σαi*(qH)i

i=1

13


17Σαi*(qL)j

j=1


i=1 j=1

17 16Q = Σαi*(qH)i +

i=1

17Q = Σαi*(qH)i +

i=1

17Σαi*(qL)j

j=1


i=1 j=1

17 16

Payne score


Predictive equations for bothdough strength (Rmax) and extensibiity (Ext)


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


17Σαi*(qL)j

j=1


i=1 j=1

17 16Q = Σαi*(qH)i +

i=1

17Q = Σαi*(qH)i +

i=1

17Σαi*(qL)j

j=1


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



17Σαi*(qL)j

j=1


i=1 j=1

17 16Q = Σαi*(qH)i +

i=1

17Q = Σαi*(qH)i +

i=1

17Σαi*(qL)j

j=1


i=1 j=1

17 16


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



17Σαi*(qL)j

j=1


i=1 j=1

17 16Q = Σαi*(qH)i +

i=1

17Q = Σαi*(qH)i +

i=1

17Σαi*(qL)j

j=1


i=1 j=1

17 16


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


17Σαi*(qL)j

j=1


i=1 j=1

17 16Q = Σαi*(qH)i +

i=1

17Q = Σαi*(qH)i +

i=1

17Σαi*(qL)j

j=1


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


17Σαi*(qL)j

j=1


i=1 j=1

17 16Q = Σαi*(qH)i +

i=1

17Q = Σαi*(qH)i +

i=1

17Σαi*(qL)j

j=1


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

Estimating dough properties and end-product quality from flour composition

Technology

Transcript of Estimating dough properties and end-product quality from flour composition