Estimating dough properties and end-product quality from flour composition

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

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Estimating dough properties and end-product quality from flour composition

  1. 1. 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
  2. 2. 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)
  3. 3. Objective, computer-driven small- and micro-scale functional tests
  4. 4. Objective, computer-driven small- and micro-scale functional testsS. 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
  5. 5. Predictive methods based on chemical/genetic data
  6. 6. Predictive methods based on chemical/genetic data
  7. 7. 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
  8. 8. Two possible approachesResearch /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
  9. 9. Protein Scoring SystemPayne 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)
  10. 10. Protein Scoring SystemPayne 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
  11. 11. Payne score versus PSSPayne 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
  12. 12. Payne score versus PSSPayne score i=1 Q = Σαi*(qH)i 13 Both HMW and LMW GS alleles are consideredProtein 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
  13. 13. Payne score versus PSSPayne score i=1 Q = Σαi*(qH)i 13 Beyond the individual effects of alleles, The effects of their interaction is also taken accountProtein 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
  14. 14. Payne score versus PSSPayne score i=1 Instead of subjective estimation, Q = Σαi*(qH)i factors of relative contributions are determined by statistical methods 13Protein 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
  15. 15. 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
  16. 16. Payne score versus PSSPayne 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
  17. 17. The contribution of glutenin alleles on dough strength and extensibility RMAX EXTFor EXT scores: - Glu3 >Glu1 - Variation among alleles at any loci is much less than those for Rmax score
  18. 18. The contribution of glutenin alleles on dough strength and extensibility Rmax Ext Relative contribution [%] Relative contribution [%] 0 20 40 60 0 20 40 60individual HMW LMWinteractive HMW-HMW LMW-LMW HMW-LMW
  19. 19. 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 16qi = 0 or 1, indicating the presence or absence of HMW GS allele iQ = the predicted genetic potential of Rmax or Ext
  20. 20. 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 16qi = 0 or 1, indicating the presence or absence of HMW GS allele iQ = 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
  21. 21. 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 ExtTool 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
  22. 22. 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 16qi = 0 or 1, indicating the concentration of proteins in allele iQ = the actual dough strength or extensibility of the sample
  23. 23. 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 16qi = 0 or 1, indicating the concentration of proteins in allele iQ = the actual dough strength or extensibility of the sample Comparison of measured and estimated Rmax and Ext 800 600RMAX esrimated 400 200 R2 = 0.8736 R2 = 0.5589 0 0 200 400 600 800 Measured RMAX
  24. 24. Application of PSS Anomalies in quality parameters of gristsIn 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
  25. 25. 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 3Sample U 0 20 40 60 80 100Sample 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
  26. 26. Two possible approachesResearch /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
  27. 27. Prediction of water absorption 400 64Dough 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)
  28. 28. 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 modelssoluble 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
  29. 29. 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
  30. 30. Thank you very much

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