EFFECTS OF WHEAT ENDOSPERM PROTEIN CONTENT AND coMposITIoN oN WHITE NOODLE QUALITY
BY
Chun Wang
A Thesis
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EFFECTS OF WHEAT ENDOSPERM PROTEIN CONTENT AND COMPOSTTTON ON WHITE NOODLE QUALITY
BY
CHUN WANG
A Thesis/Practicum submitted to the Faculty of Graduate Studies of The University
of Manitoba in partial fulfillment of the requirements of the degree
of
Doctor of Philosophy
CHUN WANG @ 2OO2
Permission has been granted to the Library of The University of Manitoba to lend or sell copies of this thesis/practicum, to the National Library of Canada to microfilm this thesis and to lend or sell copies of the film, and to University Microfilm Inc. to publish an abstract of this thesis/practicum.
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I authorize the University of Manitoba to lend this thesis to other institutions or individuals for the purpose of scholarly research.
Chun Wang
I further authorize the University of Manitoba to reproduce this thesis by photocopying or by other means, in total or in part, at the request of other institutions or individuals for the purpose of scholarly research.
Chun Wang IV
ACKNOWLEDGEMENTS
i would like to express my gratitude to my advisors Drs. Miklos I. P. Kovacs and Richard Holley, who so selflessly offered their time and encouragement to me. I would also like to thank Drs. Walter Bushuk, Dave Hatcher, and Linda Malcolmson for serving on my
committee, to thank Dr. Craig F. Morris, Washington State University, for accepting to serve as the external examiner on my examining committee. I would also like to thank
Dr. H. Sapirstein for his work as my committee member from beginning of this study.
I would like to thank Dr. B. Fowler, Crop Development Centre, University of Saskatchewan, for growing the wheat samples with different nitrogen ferttlizations and providing their protein content used in Chapters 5-7, Drs D. Brown and R. DePal-rw for providing 20 Canadian grown samples, Mr. Hideke Okusu, Nippon Flour Mills, Japan, for providing the commercial Udon flour, Mr. Hu Xingzhong, Northwest Sci-Tech University of Agriculture and Forestry, China, for providing commercial Lamian flour and for help to determine noodle optimum cooking time and cooked noodle texture, Dr. B. Marchylo for permitting use of the quality parameters of 95, 96, and 98 DURC samples in the Appendix II, and Dr. J. M. Clarke for providing breeding lines and SDSV and GI data in Appendix II. Thank you Dr. M. L P. Kovacs for permitting use of the quality parameters (PSI, SDSV, mixograph and farinograph) for nitrogen fertilization samples, and the quality parameters (mixograph, farinograph, extensograph, alveograph, RVA, gluten index, cooked gluten viscoelasticity, and loaf volume) for Canadian wheat samples. Thank you Mr. G. Dahlke for so graciously lending your hand, time and expertise for technical assistance. Thanks also to Dr. Sheila Woods for assistance in statistical analysis.
My appreciation is extended to the Cereal Research Centre, Agriculture and Agri-Food
Canada, for providing the facilities and fund which allowed me to pursue my studies. ABSTRACT
'Wang, Chun, Ph.D., The University of Manitoba, January 2003
Effects of Wheat Endosperm Protein Content and Composition on V/hite Noodle Qualiq¿ Advisor: Drs. Miklos I. P. Kovacs and Richard Holley
Noodle making quality may be predicted through an understanding of fundamental
properties of flourprotein and starch. A series of studies were conducted: (1) to develop a
small-scale method which could discriminate among samples varying in dough strength;
(2) to assess the effects of nitrogen fertllization on protein content and composition, and
to investigate the effect of protein content and composition on white noodle making
quality; and (3) to compare noodle making quality of flours from Canada and China.
The swelling index of glutenin (SIG) test was developed using only 40 mg flour or
wholemeal. It was found that the swelling capacity of glutenin depended on swelling time
and mixing intensity in non-reducing solvents. The SIG values obtained from long
swelling times were positively related to gel protein and insoluble glutenin content, whereas the SIG values with shot swelling times showed strong relationships with SDS
sedimentation volumes and Zeleny sedimentation volumes. In addition, SIG values were also able to satisfactorily account for variations in gluten strength for durum wheat.
Furthermore, swelling curves, which were obtained from SIG values versus different swelling times, were divided into three stages: swelling, swollen, and breakdown. As with dough mixing graphs obtained from the mixograph or farinograph, the swelling VI curves can be used to differentiate between some strong or weak varieties that cannot be identified with sedimentation, gel protein, and insoluble glutenin values.
The effects of nitrogen fertllization on protein composition were investigated using five spring and five winter wheat cultivars grown at six levels of nitrogen fertllization for each of two years. The sequential extraction and SE-HPLC procedures for protein fractionation resulted in similar trends of protein fractions with increased protein content, but different trends of protein fraction proportions were observed between winter and spring wheat cultivars as nitrogen ferlilization increased. The proportion of monomeric protein content increased with a decrease of insoluble glutenin in spring wheat cultivars, whereas soluble glutenin content increased as the insoluble glutenin decreased in winter wheat cultivars.
In addition to the study on the effects of nitrogen fertilization on protein cornposition, the samples were also used to evaluate the effects of protein content and composition on flour and noodle colour. As protein content increased, flour brightness (Z *) decreased with increases of red-green chromaticity (a*) and yellowness (ó*). In noodle colour, decreased L+ and increased a* and åx were observed at both increased rest time and increased protein content. The absolute amount of monomeric protein was equal to or better than protein content in relation to L*, a*, and ó*. On the other hand, as rest time increased, r-values of protein content with Z* decreased, due to the stronger effect of enzymes such as polyphenol oxidase in longer rested (24h) samples. vii
The flour samples were also used to investigate effects of protein content and composition on noodle making quality including noodle processing quality, cooking quality, and cooked noodle texture. In terms of noodle processing quality, an increase in protein content produced noodles with short dough sheet length, high thickness and noodle strength as determined by the extension test. Noodles from higher protein content flour had longer optimum cooking time, lower cooking loss and water uptake, and higher cooked noodle thickness. On the other hand, high protein flour produced noodles with firm texture determined by two instrumental measurements and in both optimum and constant cooking time tests. The statistical data showed that the absolute amounts of protein fractions were commonly better than their relative amounts in predicting noodle making quality. Furthermore, the absolute amount of insoluble glutenin was equal to or better than protein content in predicting most parameters of noodle making quality.
In addition to samples varying in nitrogen fertilization, a series of samples containing
Canadian and Chinese varieties both with a wide range of protein content and dough strength were analysed. Although there were no signif,rcant differences of protein content and insoluble glutenin content between Canada Prairie Spring (CPS) and Chinese wheat flours, Chinese flours had higher soluble glutenin content, and CPS flours had higher monomeric protein content. Chinese flours were characterized by weaker gluten and higher RVA peak viscosity in comparison with CPS flours. Noodles from CPS flours had stronger dough sheets and firmer cooked texture. viii
In the final part of this thesis, stepwise multiple regression analysis was conducted to examine the effects of the independent variables (including protein parameters and flour swelling power) on cooked noodle texture parameters. The selected models explained about 650/o of cutting stress variation in both constant and optimum cooking time tests, but only 4Io/o and 8o/o of hardness variation in constant and optimum cooking time tests, respectively. Around 50o/o of chewiness variation was explained in the constant cooking time test, but a much lower percentage was obtained from the optirnum cooking time test.
Almost 80% of SFM variation in the constant cooking time test was explained, but only
33o/o was explained in the optimum cooking time tests. The results strongly suggested that both protein content and quality should be considered in evaluation of the suitability of flours for making white noodles. ix
TABLE OF CONTENTS
PAGE
ACHNOWLEDGEMENTS iv
ABSTRACT V
TABLE OF CONTENTS. ix
LIST OF TABLES. xiv
LIST OF FIGIIRES. xxii
ABBREViATIONS xxiv
Chapter I INTRODUCTION. 1
Chapter 2 LITERATLIRE REVIEW. 6
Wheat Protein Fractionation and Functionality in Breadmaking Quality. 7 Classification of Gluten Proteins. .Wheat 7 Protein Fractionation Procedures ... 8 Gluten Proteins and Their Functionality in Breadmaking...... i0 Small Scale Tests Used to Predict Glutenin Content and Quality. t2 Effects of Nitrogen Fertilization on Protein Content and Protein Composition...... t4 White Noodle Quality 16 Noodle Colour and Discolouration. 18 Noodle Texture. 21 Effect of Starch on White Noodle Texture. 22 Starch Pasting Properties. 22 Starch Composition and Its Effect on Starch Pasting and Noodle Texture. 23 Fractionation and Reconstitution Study. 25 Effect of Protein Content and Quality on Noodle Texture. 26 Protein Quantity. 27 Protein Quality. 28
Chapter 3 SWELLING INDEX OF GLUTENIN TEST. I. METHOD AND COMPARISON WITH SEDMENTATION, GEL-PROTEIN, AND INSOLUBLE GLUTENIN TESTS ..... 31 Abstract. 31 aa lntroduction...... JJ Materials and Methods. . .. 35 Wheat Flours and Small Scale Tests 35 Determination of Soluble and Insoluble Glutenin in Flour . . ... 36 Swelling Index of Glutenin Test in SDS-Lactic Acid (SIG). . .. 37 Swelling Index of Glutenin Test in Dilute Lactic Acid (SIG- LA) .. 38 Swelling Index of Glutenin in SDS (SiG-SDS) 38 Statistical Analysis 39 Results and Discussion. . 39 lnfluence of Various Factors on SIG Value 39 Reproducibility of the SIG Test 45 Correlations between SIG, SIG-LA, SiG-SDS, Gel Protein, SDS-sedimentation, Zeleny Sedimentation and Insoluble Glutenin Content 45 Effects of Swelling Property of Glutenin on Sedimentation, Gel Protein, and SIG Tests . 47
Comparison of SIG and Sedirnentation Test Methodology. . . .. 53
Chapter 4 SWELLING INDEX OF GLUTENIN TEST. II. APPLICATION IN PREDICTION OF DOUGH PROPERTIES AND END-USE QUALiTY
Abstract 54 lntroduction ..... 56 Materials and Methods s8 'Wheat Samples 58 Small-scale Tests . 58 Dough Quality Tests . 60 Gluten Quality Tests . 60 Breadmaking Quality Test . 6r Statistical Analysis 6t Results and Discussion . 61 Associations between Small-scale Tests and Dough Strength ... 6I Associations between Small-scale Tests and Dough Extensibility .... Relationships between Swelling Cun¿es and Dough Mixing Graphs 67 Correlation of Small-scale Tests with Gluten Properlies 68 The Correlation of Small-scale Tests with Breadmaking Quality 70 The Classification of Quality Parameters 72 Conclusion 73 XI
Chapter 5 EFFECTS OF NITROGEN FERTILZATION ON QUANTITIES AND PROPORTIONS OF DIFFERENT PROTEIN T\?ES IN WHEAT FLOUR. 75
Abstract 75 lntroduction ..... 76 Materials and Methods .... 78 Samples 78 Quality Measurements . . 79 Analysis of Protein Composition ..... 79 Glutenin Subunit Analysis by RP-HPLC 80 Statistical Analysis 80 Results and Discussion . 81 Variation of Protein Composition with Nitrogen Fertilization.. 81 Relationships Among Protein Fractions Determined by Two Procedures 83 Statistical Analysis Between Protein Composition and Flour Protein Content 83 Effects of Genotype and Environment on Protein Fractions 8l Effect of Protein Increase on Glutenin Subunits 88 Variation of Quality Parameters with Nitrogen Fertilization .. 90 Relationships between Protein Fractions and Dough Mixing Characteristics .. 9t Conclusion 95
Chapter 6 EFFECTS OF PROTEIN CONTENT AND QUALITY ON WHITE NOODLE MAKING QUALITY: COLOIIR 97
Abstract. 97 lntroduction ..... 98 Materials and Methods .... 100 Samples. 100 Flour Colour Assessment ...... 101 Noodle Sheet Preparation i01 Colour Measurement ... r02 Statistical Analysis 102 Results and Discussion .. 103 Effects of Flour Protein Content and Characteristics on Flour Colour. 103 Effects of Flour Colour and Protein Content on Noodle Colour. 105 Relationships between Protein Fractions and Noodle Colour... 111 Effects of Environmental and Genetic Factors on Flour and Noodie Colours IT2 Conclusion 1r4 x11
Chapter 7 EFFECTS OF PROTEIN CONTENT AND QUALITY ON WHITE NOODLE MAKING QUALITY: PROCESSING, cooKrNG QUALiTY AND COOKED NOODLE TEXTURE... 116
Abstract 116 lntroduction ..... rt7 Materials and Methods .... . t20 Wheat Samples and Quality Analysis 120 Noodle Preparation t20 Noodle Processing Quality 121 Cooking Quality Assessments .... 122 Noodle Texture Analysis 122 Data Analysis ... 123 Results and Discussion . r23 Effects of Protein Content and Composition on Noodle- making Quality t23 Effects of Genotype and Environment on Noodle-making Quality Parameters 129 Correlations between Protein Fractions and No odle-making Quality. 131 Relationships between Dough Mixing Characteristics and Noodle-making Quality r42 Conclusions 145
Chapter 8 A COMPARATTVE STUDY ON THE NOODLE MAKING QUALITY OF V/HEAT FLOIIRS FROM CANADA AND CHINA 146
Abstract r46 Introduction 147 Materials and Methods t48 Samples r48 Protein Parameter and Analyses .... 149 Functional Measurements 149 Flour Swelling Power and Pasting Properties. t49 Noodle Making Procedure and Quality Measurement ...... 1s0 Statistical Analyses 150 Results and Discussion 1s0 Protein Parameters 1s0 Dough Strength Parameters r54 Flour Pasting Properties. i55 Noodle Making Quality 156 Relationships between Protein Parameters and Noodle Making Quality t62 Relationships between Functional Measurements and Noodle r69 x111
Making Quality Conclusion t75
Chapter 9 GENERAL DISCUSSION 175
SIG Test as a Fundamental Measurement of Glutenin Content and Quality. 175 Effect of Nitrogen Ferlilization on Protein Composition .. 177 Relationships between Protein and Noodle Colour 179 Relationships of Protein Parameters with Noodle Making Quality..... 182 Proposed Balance between Starch and Protein in Noodle Flour t87 Future Studies 188
Chapter 10 LITERATURE CITED r92
Appendix I Tables 1-36 207
Appendix II Swelling lndex of Glutenin Test for Prediction of Durum Wheat Quality 243 xlv
LIST OF TABLES
TABLE PAGE
Table 3-1 Test cultivars and their flour protein content, SDS sedimentation volume, zeleny sedimentation volume, gel-protein, and insoluble glutenin content 36
Table 3-2 Effects of mixing intensity on the SIG and SIG-SDS tests.. 40
Table 3-3 Reproducibility of SIG tests on three diverse wheat varieties. 45
Table 3-4 Correlation coefficients between SIG test, sedimentation tests, gel protein and percent of insoluble glutenin in flour 47
Table 3-5 Comparison of SDSV, SIG, insoluble glutenin content and soluble glutenin content for three groups of samples 49
Table 3-6 correlation coefficients of SIG (with different swelling time) with gel protein, sedimentation tests, and insoluble glutenin content. 52
Table 4-1 Means, standard deviations (SD), coefficients of variation (CV), and ranges for flour quality of 20 wheat samples s9
Table 4-2 Correlation coefficients between small-scale tests and dough quality parameters for 20 wheat flour samples . . . 64
Table 4-3 Correlation coefficients of dough quality parameters with SIG from different swelling time for 20 wheat flour samples ...
Table 4-4 Correlation coefficients of small-scale tests with gluten quality parameters for 20 wheat flour samples....
Table 4-5 Correlation coefficients of gluten quality parameters with SIG from different swelling time for 20 wheat flour samples.... 70
Table 4-6 Correlation coefficients of small-scale tests with baking and noodle making quality for 20 wheat flour samples.... 71
Table 4-7 correlation coefficients of baking and noodle making quality with sIG from different swelling time for 20 wheat flour samples.... 72 TABLE PAGE
Tabie 5-1 Influence of nitrogen fertllization level and variety determined by analysis of variance (F-test) on protein content, protein fractions (sequential extraction), and SIG values for nine wheat cultivars grown at six nitrogen levels in each of two years. g7
Table 5-2 Influence of nitrogen fertilization level and variety determined by analysis of variance (F-test) on protein content and protein fractions (SE-HPLC) for nine wheat cultivars grown at six nitrogen levels in each of two years.. 88
Table 5-3 Multiple comparisons of HMW/LMW glutenin subunit ratio determined by RP-HPLC among cultivars 89
Table 5-4 Influence of nitrogen fertilization level and variety determined by analysis of variance (F-test) on dough mixing characteristics for nine wheat cultivars grown at six nitrogen levels in two years. 90
Table 5-5 Simple correlation coefficients between protein fractions and dough properlies for two years spring and winter wheat 92
Table 5-6 Simple correlation coefficients between protein fractions and dough properties for spring and winter wheat.... 93
Table 5-l Simple correlation coefnicients between SIG values and dough properties for two years spring and winter wheats 95
Table 6-1 Simple cotrelation coefficients of flour colour with flour protein content, moisture, and particle size index 105 Table 6-2 Simple correlation coefficients among flour and noodle colour parameters for 2 years of three spring and three winter varieties 110
Table 6-3 Simple correlation coefficients between noodle colour and protein content and fractions for 1995 spring and winter wheats. 113
Table 6-4 Influence of nitrogen fertilization level and variety determined by analysis of variance (F{est) on flour colour and noodle colour from three spring and three winter wheats for each of two years. lI4
Table 7-7 Optimum water absorption (%) for flours with different nitrogen levels.... lzl XVI
TABLE PAGE
Table 7-2 Analysis of variance F-values and level of signifìcance for effects of variety, nitrogen fertilization, and their interactions on noodle processing properties. 129
Table 7 -3 Analysis of variance F-values and level of significance for effects of variety, nitrogen fertilization, and their interactions on noodle cooking properties. 130
Table 7-4 Analysis of variance F-values and level of significance for effects of variety, nitrogen fertilization, and their interactions on cooked noodle texture in the constant cooking time test. 130
T able I -5 Analysis of variance F-values and level of significance for effects of variety, nitrogen fertilization, and their interactions on cooked noodle texture in the optimum cooking time test. 131
Table 7-6 Correlation coefficients between protein parameters and noodle processing quality. 132
Table 7-7 Correlation coefficients between protein parameters and noodle cooking properties. I34
Table 7-8 Correlation coefficients between protein parameters and noodle cooking properties .. I34
Table 7-9 Correlation coeff,rcients between protein parameters and cooked noodle texture in constant cooking time tests. 140
Table 7-10 Correlation coeff,rcients between protein parameters and cooked noodle texture in the optimum cooking time test. 141
Table 7-I1 Correlation coefficients between dough mixing characteristics and noodle processing quality I4Z
TableT-12 Correlations of Dough Mixing Characteristics with OCT and CNT 143
Tablel-I3 Correlations of Dough Mixing Characteristics with CL and WT 143 XVlI
TABLE PAGE
Table 7-74 Correlation coefficients between dough mixing characteristics and cooked noodle texture 144
Table 8-1 Variations of protein parameters of Chinese and Canadian wheat classes. . .. 153
Table 8-2 Variations of flour quality parameters of Chinese and Canadian wheat classes. 156
Table 8-3 Variations of noodle processing quality parameters for Chinese and Canadian wheat classes. 158
Table 8-4 Variation of noodle cooking quality parameters of Chinese and Canadian wheat classes. 159
Table 8-5 Variations of cooked noodle texture parameters in constant cooking time for Chinese and Canadian wheat classes. r61
Table 8-6 Va¡iations of cooked noodle texture parameters in optimum cooking time for Chinese and Canadian wheat classes. 162
Table 8-7 Correlation coefficients between protein parameters and noodle making quality. 164
Table 8-8 Correlation coefficients between protein parameters and noodle cooking quality t64
Table 8-9 Correlation coefficients between protein parameters and cooked noodle quality in constant and optimum cooking time tests. r66
Table 8-10 Correlation coefficients between functional measurements and noodle processing quality 170
Table 8-11 Correlation coefficients between functional measurements and noodle cooking quality 170
Table 8-12 Correlation coefficients between functional measurements and cooked noodie quality 172 XVllI
TABLE PAGE
Table 9-1 Predicting SIG values using stepwise multiple linear regressions of protein content and composition. t77
Table 9-2 Predicting flour colour using stepwise multiple linear regression of protein content, PSI, and moisture content 180
Table 9-3 Predicting noodle colour using stepwise multiple linear regressions of protein content and composition. 181
Table 9-4 Predicting cooked noodle texture using stepwise multiple linear regressions of protein parameters and FSP. 186
Appendix I Table 1 Multiple comparisons of protein fractions determined by sequential extraction and SIG values for 1994 spring wheat cultivars. 207
Table 2 Multiple comparisons of protein fractions determined by sequential extraction and SIG values for 1995 spring wheat cultivars. 208
Table 3 Multiple comparisons of protein fractions determined by sequential extraction and SIG values for 1994 winter wheat cultivars.
Table 4 Multiple comparisons of protein fractions determined by sequential extraction and SIG values for 1995 winter wheat cultivars. 210
Table 5 Multiple comparisons of protein fractions determined by SE-HPLC for 1994 spring wheat cultivars. 211
Table 6 Multiple comparisons of protein fractions determined by SE-HPLC for 1995 spring wheat cultivars. 212
Table 7 Multiple comparisons of protein fractions determined by SE-HPLC for 1994 winter wheat cultivars. 2t3
Table 8 Multiple comparisons of protein fractions determined by SE-HPLC for 1995 winter wheat cultivars. 214
Table 9 Multiple comparisons of HMWLMW glutenin subunit ratio determined by RP-HPLC for 1995 spring and winter wheat cultivars 2I5 X1X
TABLE PAGE
Table 10 data for 1994 spring wheat cultivars. Quality 216
Table 11 data for 1995 spring wheat cultivars. Quality 217
Table 12 data for lgg4winter wheat cultivars. Quality 218
Table 13 data for 1995 winter wheat cultivars. Quality 219 Table 14 Multiple comparison of flour color for three 1995 spring, three winter wheat, tluee 1994 spring and three winter wheat cultivars. 220
Table 15 Multiple comparisons of noodle color for 95 spring wheat cultivars. 221
Table 16 Multiple comparisons of noodle color for 95 winter wheat cultivars. 222
Table 17 Multiple comparisons of noodle color for tlvee 1994 spring andthree 1994 winter wheat cultivars. 223
Table 18 Multiple comparisons of noodle making processing quality and cooking quality for 1994 spring wheat cultivars. 224
Table 19 Multiple comparisons for noodle making process quality and cooking quality for 1995 spring wheat cultivars. 225
Table 20 Multiple comparisons of noodle making processing quality and cooking quality for 7994 winter wheat cultivars. 226
TabIe2l Multiple comparisons of noodle processing quality and cooking quality for i995 winter wheat cultivars. 2Zj
Table22 Multiple comparisons of noodle cooking properties. 2Zg
Table23 Multiple comparisons of cooked noodle texture in constant cooking time test for 1994 spring wheat cultivars. 22g
Table24 Multiple comparisons of cooked noodle texture in constant cooking time test for 1995 spring wheat cultivars. 230 XX
TABLE PAGE
Table 25 Multiple comparisons of cooked noodle texture in constant cooking time test for 1994 winter wheat cultivars. 231
Table26 Multiple comparisons of cooked noodle texture in constant cooking time test for 1995 winter wheat cultivars. 232
Table 27 Multiple comparisons of cooked noodle texture in optimurn cooking time test for i994 spring wheat cultivars. 233
Table 28 Multipie comparisons of cooked noodle texture in optimum cooking time test for 1995 spring wheat cultivars. 234 Table 29 Multiple comparisons of cooked noodle texture in optimum cooking time test for 1994 winter wheat cultivars. 235
Table 30 Multiple comparisons of cooked noodle texture in optimum cooking time test for 1995 winter wheat cultivars. 236
Table 31 Protein parameters for Chinese and Canadian wheat varieties. 237
Table 32 Quality parameters for Chinese and Canadian wheat varieties. ng
Table 33 Variations of noodle making quality parameters ng
Table 34 Variations of noodle cooking quality parameters 240
Table 35 Variations of cooked noodle texture parameters in constant cooking time.. 241
Table 36 Variations of cooked noodle texture parameters in optimum cooking time 242
Appendix Swelling index of glutenin test for prediction of durum wheat II quality. 243
Table I Reproducibility of SIG tests on three diverse durum wheat varieties from method evaiuation samples. 250 XXI
TABLE PAGE
Table II Correlation coefficients between SIG, sedimentation tests and gluten strength parameters. 253
Table III Correlation coefficients of SiG with different swelling time with SDS sedimentation, the percentage of insoluble glutenin content, the percentage of soluble glutenin content and gluten index for the 20 selected samples from breeding lines 255
Table IV Relationships among SDS sedimentation, SIG and gluten index for the common and unusual samples from breeding lines. 257
Table V Correlation coefficients among protein fractions, small-scale tests and dough quality parameters for the 12 method evaluation samples. 259 XXII
LIST OF FIGURES
FIGTIRE PAGE
Fig. 3-l Effect of temperature on SIG-SDSLA. Suneca (r); Banks (v); Alpha 16 (o) 40
Fig.3-2 Effect of hydration time on sIG-SDSLA (A) and SIG-SDS (B) tests. Suneca (r); Banks (v);Alpha16(e).. 4l
Fig. 3-3 Effect of swelling time on SiG-SDSLA (A) and SIG-SDS (B) tesrs. Suneca (r); Banks (v);Alpha 16 (o). 43
Fig. 3-a Effect of solvent:flour ratio on SIG-SDSLA (A) and SIG-SDS (B) tests. Suneca (r); Banks (v); Alpha 16(o). 44
Fig. 3-5 Swelling curves for three groups of samples. A: strong varieties. Glenlea (r); Suneca(v); Sunco (o). B: medium varieties. Banks (r); Timgalen (v); Tomes (o). c: weak varieties. Fielder (r); Alpha 16 (v); 90w950 (o)...... 50
Fig. 4-l Representative swelling curve showing measured indices of swelling time to peak (STP), swollen time (ST) and area under the curve... 6g
Fig. 5-1 Percent monomeric protein (A), percent soluble glutenin (B), and percent insoluble glutenin in flour (C) determined by SE-HPLC as a function of percent corresponding protein fractions determined by sequential extraction 84
Fig. 5-2 Percent monomeric protein in flour (A), percent monomeric protein in protein (B), percent soluble glutenin in flour (C), percent soluble gl¡tenin in protein (D), percent insoluble glutenin in flour (E), and percent insoluble glutenin in protein (F) (obtained by sequential extraction) as a function of percent protein in flour. g6
Fig. 6-1 changes in brightness of Katepwa (A) and Norstar (D), in redness of Katepwa (B) and Norstar (E), and in yellowness of Katepwa (C) and Norstar (F) of raw white noodle sheets over time for different nitrogen fertilizations. 108
Fig. 7-1 Representative fresh noodle extension curve showing measured indices of maximum resistance, area, and distance. t2I Fig.7-2 Effect of nitrogen fertilization on cooked noodle-cutting stress (CS) for three 1994 spring wheat cultivars. r2s
Fig.7-3 Effect of nitrogen fertilization on cooked noodle chewiness for three 1994 spring wheat cultivars... t27 XXIlI
FIGURE PAGE
Fig.7-4 Cooked noodle cutting stress (CS) in constant cooking time as a function of percent of protein (A), insoluble glutenin in flour (B) and in protein (C); and in optimum cooking time as a function of percent of protein (D), insoluble glutenin in flour (E) and in protein (F) for total samples. .... 136
Fig. 7-5 Cooked noodle chewiness (CHEW) in constant cooking time as a function of percent of protein (A), insoluble glutenin in flour (B) and in protein (C); and in optimum cooking time as a function of percent of protein (D), insoluble glutenin in flour (E) and in protein (F) .. . 137
Appendix II
Fig. I Effect of swelling time on swelling index of glutenin test for three cultivars. 248
Fig. 2 Effect of solvent: flour ratio on swelling index of glutenin test for three cultivars. 249
Fig. 3 Diagram of comparing SIG value to 1/10 SDS sedimentation volume for20 samples. SIG and 1/i0 SDSV 254
Fig. 4 Three kinds of representative swelling curves. 254 XXlV
ABBREVIATIONS
ADHE: TPA adhesiveness.
AGF: percentage of albumin-globulin in flour.
AGP: the percentage of albumin-globulin in protein.
AREA: the area under extension curve.
ART: farinograph arrival time.
BKD: RVA breakdown.
CGVS: cooked gluten viscoelasticity.
CHEW: TPA chewiness.
CL: cooking loss
CNT: cooked noodle thickness.
COHE: TPA cohesiveness.
CPS: Canada Prairie Spring wheat.
CS: cutting stress.
CWES: Canada Westem Extra Strong Red Spring wheat.
CWRS: Canada Western Red Spring wheat.
CV/SV/S: Canada'Westem Soft White Spring wheat.
DDT: farinograph dough development time.
DIST: extension distance.
DSL: dough sheet length.
DTT: dithiothreitol.
ETP: mixograph energy to peak.
EXT: extensibility. XXV
FAB : farinograph absorption.
FSP: flour swelling power.
G: alveograph G index.
GI: gluten index.
GLIF: the percentage of gliadin in flour.
GLIP: the percentage of gliadin in protein.
GUM: TPA gumminess.
HARD: TPA hardness.
IGF: percentage of insoluble glutenin in flour.
IGP: percentage of insoluble glutenin in protein.
LV: loafvolume.
MDT: mixograph mixing development time.
MPF: percentage of monomeric protein in flour.
MPP: percentage of monomeric protein in protein.
MTI: farinograph mixing tolerance index.
OCT: optimum cooking time.
P: alveograph P index.
Pro: flour protein content.
PSI: particle size index.
PV: RVA peak viscosity.
RECOV : compression recovery.
RESI: TPA resilience.
Rmax: the maximum extension resistance for dough in extensograph or fresh noodle. XXVi
SDS: sodium doc
SDSV: SDS sedimentation volume.
SFM: surface firmness.
SGF: percentage of soluble glutenin in flour.
SGP: percentage of soluble glutenin in protein.
SIG: swelling index of glutenin determined with SDS containing lactic acid.
sIGO: swelling index of glutenin determined with 0 min swelling time.
sIG20: swelling index of glutenin determined with 20 min swelling time.
SIG5: swelling index of glutenin determined with 5 min swelling time.
sIG-LA: swelling index of glutenin determined with lactic acid solution.
SiG-SDS: swelling index of glutenin determined with SDS solution.
SPRI: TPA springiness.
STA: farinograph stability.
TEG: mixograph total energy.
THICK: thickness of fresh noodle.
W: alveograph W index.
WG: wet gluten.
WT: water uptake. CHAPTER 1
Introduction
Wheat is the primary cereal grain consumed by humans around the world, with nearly
600 million tons annual consumption in recent years globally. Amongst cereal crops,
wheat has a unique characteristic, its gluten, the functional protein that confers upon
dough its viscoelastic properties. When dough is formed by mixing flour with water,
gluten protein becomes hydrated forming a continuous matrix surrounding embedded
starch granules. Hydrated gluten is highly cohesive, exhibiting both elasticity and
extensibility, and these properties allow wheat flours to be used to produce a wide variety
of different products. Varietal differences in both protein quantity and quality are
important attributes which affect the breadmaking quality of a flour variety (Finney and
Barmore 1948, Bushuk et al. 1969).
Wheat protein consists of two main components, gliadin - a monomer (single chain
polypeptide) with intra-chain disulfide bonds either present, or absent as in co-gliadin, and
glutenin - a pollnner of varying size with inter-chain disulfide bonds linking high
molecular weight (HMV/) and low molecular weight (LMV/) subunits. The two fractions
differ in their functional properties. Glutenin is considered responsible for the elastic properties of gluten and dough, while gliadin is believed to contribute to the viscous flow or extensibility characteri stics.
The contribution of protein composition to dough functional properties and end-use 2
quality is complex. Osborne (1907) was the pioneer of wheat protein fractionation, which
was based on solubility of protein classes. Clean separation of the protein classes has been very difficult to achieve because of overlapping solubility of proteins that are present. Wheat protein is essentially a highly heterogeneous mixture of polypeptides
(MacRitchie 1992). Recently, several methods have been published that provide clear separation of wheat protein fractions (Fu and Sapirstein 1996; Fu and Kovacs 1999). The relationships between protein composition and bread-making quality have been extensively researched. The insoluble glutenin present, which is considered an expression of protein quality, is directly related to breadmaking quality (Orth and Bushuk 1972;
Gupta et al.7992,I993;Bean et al. 1998; Sapirstein and Fu 1998).
Wheats of different varieties vary in protein content and protein quality. As the demand for wheat of different quality for a range of different food products is becoming more sophisticated and specific, the development of end-use targeting for wheat varieties challenges many wheat breeders. One of the more important needs arising from this challenge is the reliance upon rapid methods for evaluation of the end-use quality of a wheat variety which are based on a very small sample size, especially in early generation testing where only 60 - 1009 seed is available. Therefore, cereal laboratories involved in wheat breeding programs are always looking for new procedures to assess quality that meet the criteria of rapidness, simplicity, and small sample size. Based on determinations of glutenin, several small-scale methods have been developed and are widely used in the evaiuation of protein quality. These tests include the sodium dodecyl sulfate (SDS) sedimentatron, Zeleny sedimentation, and gel protein and insoiuble glutenin content tests. ô J
However, they do not always differentiate effectively among wheats of different quality,
especially between some strong or some weak cultivars (Pritchard 1993: f{halkar et al.
1996; Wang and Kovacs2002a,b).
Among various wheat-based food products, the white noodle is a relatively simple
product, generally comprised of a flour-water dough with or without of salt that is
sheeted and cut into various shapes, followed by drying. White noodles are broadly
classified into white Japanese Udon and Chinese styles. Both noodle types are preferably
made from white wheats that contain low levels of the browning enzyme, polyphenol
oxidase (PPO) (Hou 2001). For Japanese white noodles, characteristics that contribute to
the production of improved noodle quality include high starch pasting peak viscosity, low
amylose content, low protein content and high protein quality as measured by SDS
sedimentation volume (Nagao 1996; Black et al 2000). On the other hand, Chinese white
noodles require flours with higher protein content, and high pasting properties are not
necessary because the Chinese prefer cooked noodles with firm texture. In contrast with
the extensive literature on relationships between protein composition and breadmaking
quality, little has been reported on noodle making quality in relation to protein content
and composition.
China is the leading producer in the world of white noodles from common wheat flours.
Other countries are also increasing production of white noodles, so the consumption of this classic Chinese food is increasing worldwide. However, in China the lack of sufficient quantity and quality of its domestic wheat affect the availabiiity of noodle 4
flours, which obliges the major millers to import wheat from abroad. Canada is one of the
major wheat exporters to the Chinese market, and a considerable proportion of Canadian
wheat exported to China is used for white noodle production. Unfortunately, little
research has been done on the Chinese white noodle making quality of Canadian wheat.
As a result several questions arise. For example, how do factors such as genetic and
environmental factors affect the noodle making quality of Canadian wheat? What are the
differences between Canadian and Chinese wheats in terms of their noodle making
qualities? These types of information would be useful for wheat breeding progïams
targeted to improve the end-use quality of Canadian wheat, and ultimately to improve the
competitiveness of Canadian wheat in world markets.
In the present work, and before investigations of the effects of protein content and quality
on white noodle making quality were begun, a method based on glutenin swelling
properties was developed, and it differentiated among wheat flour samples of varying protein quality- Results by this method were used to predict differences in wheat cultivar performance in dough mixing properties and noodle-making quality. I¡r addition, the developed method was evaluated against cur¡ent widely used methods, such as SDS sedimentation, Zeleny sedimentation, gel protein, and insoluble glutenin content tests. ln addition to the success in common wheat evaluation, the new method was extended to evaluate its suitability for assessing strength parameters in durum wheat. Next, protein content and composition were assessed in samples with different protein content obtained following growth of wheat with different nitrogen fertjlization levels. White noodle making quality from this set of samples was also evaluated to identify the contributions 5 of protein content and protein fractions to noodle making quality parameters. Finally, two sets of samples, one from Canada and another from China, were used to compare protein content, composition, flour pasting properties, dough mixing properties, and noodle making quality. The roles of protein content, quality, and starch pasting in noodle making quality were evaluated using simple correlation and stepwise multiple regression. While the work in this thesis concentrated on protein functionality, an understanding of contributions of both protein and starch will provide the capability to objectively predict noodle-making quality and provide information in breeding programs necessary to obtain desired cultivars for Chinese white noodle products. CHAPTER 2
Literature Review
'Wheat is unique from other cereals since wheat flour, when rnixed with water can form dougli that consists mainly of starch granules surrounded by a hydrated film of gluten proteins and which possesses properties suitable for a large variety of wheat products, such as breads, steamed bread, and noodles. Proteins, in both content and quality, are recognized as the most important components governing bread-making quality (Finney and Barmore 1948). Gluten proteins, of which gliadins and glutenins are two major fractions, are complex protein mixtures, and glutenins are the most important in bread- making quality and dough rheological properties (Orth and Bushuk 1972; Hueber and
W all 197 6; MacRitchie 1 999).
Common wheat is a polyploid species containing three (AABBDD) related genomes. The genetic constitution of wheat is important because all quality traits result from the expression of genes and their interaction with the environment (including the level of plant-available nitrogen, which is an important factor influencing protein quantity and quality). Therefore, cereal chemists focus mainly on evaluations of the effects of genetic and environmental factors on glutenin composition and end-use quality of wheat, and try to explain why flours have good or poor quality. Intensive research has been undertaken to elucidate the components of gluten proteins that are responsible for the quality of wheat after the study of Finney and Barmore (1948). 7
This review will cover related literature on gluten protein classification, extraction and isolation procedures used for wheat proteins, physicochemical properties of the protein fractions, the effect of nitrogen fertilization on protein content and quality, and the relationship of the protein fractions to technological parameters related to wheat quality.
The literature on the effects of protein content, quality and starch pasting properties on white noodle making quality will also be reviewed.
WHEAT PROTEIN FRACTIONATION AND FUNCTIONALITY IN BREAD-
MAKING QUALITY
Cløssification of Gluten Proteins
There are several classifications of wheat protein based on protein solubility, molecular size, composition of amino acids, or structure. Based on sequential extraction and differential solubility, wheat proteins were divided into five different groups, albumins
(soluble in water and dilute buffers), globulins (not soluble in water but soluble in saline solutions), gliadins (which are soluble tn 70o/o ethanol), soluble glutenins (which are soluble in dilute acid), and insoluble glutenins (Osborne 1907; Chen and Bushuk Igi}).
Many albumins and globulins are enzymes or enzyme inhibitors. Their apparent molecular weights from SDS-PAGE are lower than the gliadins (<30,000 Da). Gliadins and glutenins are storage proteins.
An alternative classification of storage proteins is based on their biological and functional 8
properties rather than on their solubility characteristics (Shewry et al. 1986). Gluten
proteins are classified into high molecular weight glutenins (HMW glutenin subunits),
sulphur-poor prolamins (ro-gliadins), and sulphur-rich prolamins (LMW glutenin subunits
and o-, B-, and y-gliadins).
According to molecular composition, wheat proteins can be divided into monomeric and
polymeric proteins (MacRitchie 1992), which correspond to gliadin and glutenin,
respectively. While gliadins are single polypeptide chains, glutenins are multi-chained
structures of polypeptides that are held together by disulfide bonds. The major difference
between these two groups of storage proteins is found when anaTyzing their functionality.
Gliadins are contributors to the viscosity of dough, and glutenins play the elastic role in
dough.
PI4teat Protein Fractionation Procedures
Extraction is commonly used in most wheat protein isolation procedures. There are two
kinds of extraction procedures, direct extraction and sequential extraction. Several
solvents were used in direct extraction procedures: aqueous alcohol solution such as 50o% propanol (Bean et al. 1998), acid solution such as acetic acid, mixed solution containing detergents such as cetyltrimethyl ammonium bromide (Meredith and Wren 1966), and sodium dodecyl sulfate (SDS) (Danno et al. I974). Using any of these solvents, however, some 10-30o/o of the protein remains undissolved, depending on the particular gluten sample. The very high molecular weight of the polyrneric proteins is responsible for their 9
partial insolubility. To solve this problem, several extraction procedures were developed.
He et al. (1991) showed that the extraction rate was increased to more fhan 95o/o by
prolonged extraction time (up to 10 h in SDS solvent). Complete solubilization of total
wheat proteins was attained with the extractions of SDS solvent under sonication (Singh
et al. 1990a) and with high concentrations of SDS-urea mixture at higher temperature
(Gao and Bushuk 1992).
The modified Osborne fractionation procedure (Chen and Bushuk I97O) is a well known
sequential procedure used to obtain five protein fractions, but each ofthese fractions is a
complex mixture of different polypeptides and these polypeptides overlap in their
solubilities (MacRitchie 1992; Wang et al. 1998). Recently, procedures were introduced
to solve this problem (Fu and Sapirsteinlgg6; Sapirstein and Fu 1998; Fu and Kovacs
1999). One-step extraction with a mixed solution (7 .5% propanol + 0.3M NaI) to separate
monomeric protein from polymeric protein was also published (Fu et at. I998a). It is
believed that all monomeric proteins can be selectively extracted and then soluble
glutenin and insoluble glutenin could be separated with 50o/o propanol and 50% propanol
containing a reducing agent, respectively.
Reliable measurements of molecular weight distribution of wheat proteins are not easy to obtain, due to the difficulty in complete solubilization and the lack of a separation method for very high molecular weight polymers. The molecular weight distribution of
'Wall wheat protein can be determined by gel filtration using Sepharose 4B (Huebner and
1976) or by a controlled pore glass column (Field et al. 1983), but they require a long 10
time for analysis. With the development of instrumentation, size-exclusion high-
perfotmance liquid chromatography (SE-HPLC) was introduced to determine the
molecular weight distribution of wheat proteins. A procedure introduced by Dachkevitch
and Autran (1989) was used to separate SDS-soluble protein into four fractions beyond
insoluble glutenin. In order to characterize total protein, extraction with sonication was
described as an efltcient method to extract total protein without reducing agents (Singh er
al. 1990a), but it was thought that sonication might cleave disulf,rde bonds of large
glutenin aggregates to smaller aggregates (Khan et al. 1994). SE-HPLC is used for this
purpose by the extraction of protein with sonication in SDS solution. Three peaks were
designated as glutenin, gliadin, and albumin/globulin according to their molecular size.
Another valuable approach, known as multi-stacking SDS gel electrophoresis, initiated
by Khan and Huckle (1992), uses a series of gel layers with pore sizes decreasing in steps
with increasing mobility. More recently, other new promising techniques have appeared.
Flow Field-flow fractionation (FFF) provides analysis of the distribution of molecular
sizes far beyond the size limits of SE-HPLC (Stevenson and Preston 1996; Wahlund ¿¡
aL.7996).
Gluten Proteins ønd Their Functionality irt Bread-making
Because gliadins are heterogeneous mixtures of single-chained polypeptides, there are no free cysteine residues, and all S-S linkages are intramolecular. In accordance with their mobility in acid-PAGE, gliadins are divided into four groups: cr- (fastest mobility) , ß-, T-, and ro-gliadins (slowest mobility) with the molecular weight range from 30,000 to 75,000 11
Da (MacRitchie 1992). Gliadins of one variety can be separated tnTo 20-2.5 components
using one-dimensional electrophoresis, and into < 50 components by using two-
dimensional electrophoresis (Bushuk andZillman 1978; Payne et at. 1982;Pogna et ctl.
1eeo).
Gliadins are geÍrcrally considered to contribute to the viscosity and extensibility of
gluten. Although some authors have associated specific gliadin alleles with bread-making
quality, it is now widely accepted that these proteins may not have a direct effect on
wheat quality in terms of dough srrength (MacRitchie 1992).
With molecular weights over twenty million Da (Huebner and Wall1976; Stevenson and
Preston 1996) glutenins consist of a heterogeneous mixture of polymers formed by
disulfide-bond linkage of polypeptides or subunits. According to their solubility,
glutenins are divided into two parts: soluble glutenin and insoluble glutenin. It is believed
that insolubie glutenin contains distinctly larger size polymers than soluble glutenin
fractions (Gupta et al.1993; Bean and Lookhart 2001).
In pollmer science, two quantitative factors are found governing strength: the fraction of polymer above a critical molecular weight and the molecular weight distribution of this fraction (MacRitchie 1999). The functionality of glutenin depends therefore on its molecular size; the small glutenin fractions showed viscous-like behaviour, and gel-like behaviour for large glutenin fractions (Tsiami et al. 1997a,b). 12
A number of studies carried out after the discovery of the relationship between insoluble
glutenin and bread-making quality (Orth and Bushuk 1972) have indicated that the bread-
making quality of wheat is correlated with the proportion of high molecular weight
glutenin polymers. A direct role of high molecular weight glutenin in determining the
bread-making quality has been shown by the correlations of dough strength and bread-
making quality with the amount of swollen insoluble glutenin (Zeleny 1947; Axford, et al.
1979; Graveland et al. 1979), with the amount of insoluble glutenin (Orth and Bushuk
1972; Bean et al. 1998; Sapirstein and Fu 1998), and with the molecular weight
distribution of glutenin (Huebner and Wall 1976; Dachkevitch and Autran 1989; Singh er
al.I990b; Gupta et al. 1992). The results showed that the amount of large glutenin rather
than total glutenin was responsible for dough strength. Good quality flour has a higher
total glutenin protein content and a higher proportion of glutenins with the largest
molecular sizes at the 4o/o origin of multi-stacking SDS-PAGE gels than do poor quality
flour samples (Huang and Khan 1997).
Smøll-Scale Tests Used to Predict Glutenin Content and euølity
Wheat quality assessment includes many types of tests that measure the properties of wheat flour and dough which are important to the quality of baked products. Except for the direct methods of dough strength evaluation, such as dough rheology measurements and baking test, a number of small-scale tests can also be used based on the measurement of glutenin content (such as Zeleny), SDS sedimentation tests, gel protein test, insoluble glutenin content, and size-exclusion HPLC. Glutenin has an important swelling property 13
in various non-reducing solvents, such as dilute acetic acid, lactic acid, or SDS solutions.
Based on the swelling capacity of insoluble glutenin, three tlpes of small-scale tests have
been established to predict quality in the early stages of wheat breeding. The Zeleny
sedimentation test was introduced in 1947 (Zeleny 7947), and was modified in 1957
(Pinckey et al. 1957), and accepted as an Approved Method of AACC (AACC 2000).
The SDS sedimentation test was developed by McDermott and Redman (1977). Both
tests measure the volume of the flocculation by dispersing glutenin in dilute lactic acid or
SDS/lactic acid solutions, respectively. The higher the sedimentation volume, the better
the bread-making or pasta-making quality of the tested wheat varieties. The SDS
sedimentation test has a better correlation with loaf volume than the Zeleny
sedimentation test (Axford et al. 7979). Therefore, some modified methods have been
developed (Kovacs 1985; Penaet al. 1990) and widelyused in the prediction of bread-
making and pasta-making qualities for common and durum wheat in early stages of
breeding programs. In sedimentation tests, the solutions (lactic acid or acidified SDS)
dissolve most of the proteins (monomeric proteins and soluble glutenins), but not the high
molecular weight glutenins which remain in the swollen layer of the sediment (Fullington
et al. 1987; Echert et al. 1993). The SDS and Zeleny sedimentation tests are, therefore,
similar in principle.
The third method, based on the swelling properties of glutenin, involves the determination of gel protein, also termed macropolymer ('Weegels, et al. 1994), since the swollen insoluble glutenin can form a gel-like layer on the top of the starch pellet after a high speed centrifugation of SDS extracts (Moonen et al. 1982). With the centrifugation 14
separating starch from swollen glutenin, the weight or volume of gel-protein depends
mainly on the content of glutenins (Graveland et al. 1979; Sapirstein and Suchy lggg).
The direct measurement of insoluble glutenin content is done by the determination of the
amount of residue protein after removing soluble protein (Orth and O'Brien 7976;Bean
et al. 1998), or determination of the amount of insoluble glutenin by extracting them with
reducing agents (Fu and Sapirstein 1996; Sapirstein and Fu 1998). The glutenin contents
obtained from all of the above methods were claimed to have better correlations with
parameters of wheat quality than protein content (weegels et al. 1996).
EFFECTS OF NITROGEN FERTTLIZATTON ON PROTEIN CONTENT AND
PROTEIN COMPOSITION
Wheat quality depends on the quality and quantity of its endosperm proteins, which are
influenced by genotype (cultivar grown) and environmental factors. While the quality of
proteins is genetically determined, the quantity is highly influenced by the environment,
especially by nitrogen fertilization (Croy and Hageman I970; McNeal et al. 1972). It has
long been known that an increase of nitrogen fertilization can improve wheat bread-
making quality by increasing its protein content. However, a very high rate of nitrogen
fertilization was associated with a marked weakening of dough properties and a deterioration of bread-making quality (Tipples et al. T977; Bushuk et al. I97B). Fowler
(1998) showed that increments of nitrogen fertilizer caused a linear increase in protein in both a high protein and a normal protein line of wheat, with the high protein line 15
maintaining a higher grain protein content at all levels of applied nitrogen. Results also
showed that low or moderate rates of applied nitrogen reduced protein levels and it was
reasoned that the protein was being diluted by the large increases in yield due to fertilizer
application.
ln terms of the effect of nitrogen fertlhzation on protein fractions determined by
sequential extractions, Tanaka and Bushuk (1972) found that all protein fractions varied
in proportion to the total protein content of the flour, with the composition of the protein
showing no net change. However, Doekes and Wennekes (1982) reported that the
increase in protein content increased only the gliadin content, and the glutenin, albumin,
and globulin contents did not change. Bushuk et al. (1978) found that the proportion of
acetic acid-soluble glutenin increased markedly with increased nitrogen application, and
the increase was offset by a corresponding decrease in the proportions of albumin and
residue protein.
Extracting with SDS buffer under sonication, it was found that in the molecular size
distribution of total flour protein, determined with SE-HPLC, the proportion of glutenin
remained constant, the proportion of gliadin increased, and the proportion of albumin-
globulin decreased as the flour protein content increased with nitrogen fertilization
(Gnpta et al. 1992). Peltonen and Virtanen (1994), using densitometric measurements of proteins separated by SDS-PAGE, observed that the increase of gliadins was due mainly to an increase of low molecular weight gliadins. The effects on gliadins were larger than on glutenin, and major protein types (a-gliadins, y-gliadins, and LMV/-subunits of glutenin) were more affected than minor types (co-gliadins and HMW subunits) (Wieser T6
and Seilmeier 1998). Recently Pechanek et al. (1997) found that the effect of increased
protein content on gliadin-to-glutenin ratio was inconsistent for two French varieties. An
increase in protein content increased the ratio in one variety, Capo, but the opposite was
true for another vanety, Renan. They also found that the ratio of LMW-Io-HMW glutenin
subunits decreased as total protein content increased by nitrogen fertilization.
WHITE NOODLE QUALITY
Wheat flour noodles are one of the most important staple foods in many Asian countries.
It is believed that noodles originated in China as early as 5000 BC, then spread to other
Asian countries (Corke and Bhattacharya 1999). Today, the amount of flour used for
noodle making in Asia accounts for about 40'/o of the total flour consumed (Hou ZOOI).
Almost 30-40% of the wheat flour produced in China is consumed as noodles (Ding and
Zheng 199i). Noodles are now also becoming popular in Western countries. Because
many Asian countries are rapidly increasing the amount of wheat imported, exporting
nations are increasingly interested in the quality requirements for wheat destined for use
in noodles.
Many different types of noodles are found in Asian countries. There is no systematic classification or nomenclature for Asian noodles (Hou 2001). The terminology of classifying noodles can be confusing since noodles of almost identical composition have different names in various countries. Asian noodles can be classified according to: 1) the raw material used in their manufacture;2) salt used; 3) processing method, and 4) size of 17
noodles (Hou 2001). Although a wide diversity exists in noodle types, for simplicity they
can be classified into three main groups: white-salted noodles (Udon in Japan and Gua
Mian in China) which are popular in China, Japan, and Korea; yellow-alkaline noodles
(also called Cantonese or Chinese noodles) which are popular in Malaysia, Singapore,
Indonesia, Thailand, and Southern China; and instant noodles which are popular in East
and Southeast Asia (Corke and Bhattacharya 1999). There are regional preferences for
noodle colour, texture, flavour, size and shape, shelf life, and ease of cooking- These
quality characteristics are determined by flour characteristics, method of preparation, and
the inclusion of other raw materials or chemical additives. At this point, the review will
focus on providing information on white noodle quality requirements of flour and other
related aspects of Asian noodles.
Noodle appearance, cooked noodle texture, and process properties are three key quality
attributes in the evaluation of wheat flour for noodle making of any type. In terms of
appearance, white noodles should have a creamy white colour free from any discoloration
or darkening. Cooked noodle texture, a key criterion for the white noodle, is a specific
requirement according to regional preferences. ln modern industrial production, noodle
dough process behaviour is of particular importance, but this property is often ignored in
published research (Hou 2001).
White noodles have the most simplified formulas, containing only flour, water, and salt.
According to consuming regions, there are two kinds of white noodles. The Japanese type
Udon noodles is made from low protein soft wheat flour with high swelling starch 18
capability, while the Chinese type (Gua Mian), is made from hard wheat with medium to
high protein flour and starch which swells to a lesser extent (Hou 2001). More research
has been don on Udon noodles than other noodle types.
Noodle Colour and Discolouration
Noodle appearance is the first critical assessment of noodle quality made by a consumer.
All types of noodles require brightness, and the best white noodle colour is bright creamy
white. Absence of undesirable discolouration (spots) is essential to consumer acceptance
of both alkaline and white noodles. The majority of discerning Asians prefer noodle
products made from high quality patent wheat flours, which can produce bright noodles
with minimal discolouration. Based on research, noodle colour and discolouration are
determined by several factors, e.g., flour colour (Miskelly Ig84), ash content (Crosbie er
al' 1990), flour extraction rate (Yasunaga and Uemura 1962; Hatcher and Syrnons 2000),
flour particle size (Hatcher et al. 2001), sprout damage (Kruger et al. 1995), protein
content (Miskelly 1984), and enzymes (Hatcher and Kruger 1996).
Flour colour is regarded as one of the most important factors in assessing the value of
flour for Japanese Udon noodles (Miskelly 1984; Hou 2001). Wheat flour colour has several components. Flour tends to be slightly yellow due to carotenoids, principally xanthophylls, together with flavone compounds (Gooding and Davies I9g7). Moss
(1967) found significant varietal differences in the yellow pigment content of flours milled from Australian wheat varieties. Bleaching with benzoyl peroxide has been found 19
to destroy the yellow pigments, and this practice is often used commercially for flour
(Miskelly 1984). Flour colour is also dramatically influenced by bran content. Bran
contamination is dependent on the texture of the endosperm and the flour extraction rate
(Kim and Flores 1999; Hatcher and Symons 2000). Flour colour is influenced by the
inherent greyness of the endosperm as well (Gooding and Davies 1997). At present,
noodle manufacturers prefer white seed coat wheat with low inherent gre)mess, as the
resulting flour and corresponding noodle does not show the bran specks as obviously as
red seed coat wheat varieties. Flour colour with Z * > 90 measured with a Minolta
Chroma Meter is often required to produce bright white noodles (Hou 2001). Previous
reports showed that flours from Australian soft wheat had higher brightness than those
from US and Canadian soft wheat (Nemeth et al. 1994; Jun et al. 1998), while the
Australian flours, showed similar brightness to the commercial Japanese white noodle
flours (Jun et a|.1998).
Because flour ash content is highly correlated with flour colour (Shuey and Skarsaune
1973), ash content is a critical parameter in evaluating white noodle flour quality for
noodle use. The requirements of ash content vary with different types of white noodles.
Udon noodle flours require 0.33-0.45% ash content, but premium quality noodles are often made from flours of 0.4o/o or less ash content (Nagao 1996). For Chinese white noodles, first- and second-grade flour ash content should not exceed 0.55% or Q.jOo/o, respectively (Huang 1996).
Yasunaga and Uemura (1962) found that an increased extraction rate was the main cause 20
of colour deterioration in Udon noodles. High extraction milling would consequently
increase the amount of noticeable bran flecks in the end products. Generally, the lower
the extraction the brighter the colour and the lower the yellowness in the noodles. Such
was the case in a study by Miskelly and Moss (1985), in which straight-grade flours gave
raw noodles that \¡/ere more yellow but duller, than those prepared from patent flours. In
several other studies it was shown that a decline in white noodle brightness occurred with
increasing flour extraction rate (Oh et al. 1985b;Lee et at. 1987; Hatcher and Symons
2000). According to Lee et al. (1987), the appearance of the cooked Korean white
noodles made from Australian wheat declined noticeably when the extraction rate
increased from 45 to 75%o. More recently, using computer image analysis, Hatcher and
Symons (2000) found that straight-grade flours (about 75Yo fTour yield) produced more
spots than the matching patent flours (60% flour yield).
It is well known that flour protein content is related inversely to white noodle brightness
(Moss l97l; Miskelly and Moss 1985; Lee et al. 1987) because starch granules, which
are highly reflective, are diluted by the protein (Gooding and Davies IggT). Flour colour
grade is strongly associated with protein content (Bushuk et at. 1969), and high protein
content is often associated with increased gïeyness (Barnes 1989). However, no
significant relationship was found between yellow pigment and protein content (Moss
1967).It is well recognized that noodle colour appeared to be influenced more by protein than by ash content, because colour showed a better correlation with protein content
(Miskelly 1984; Jun et al. 1998). 2T
Fresh noodles may be stored up to several days before use. This presents potential
problems because time dependent colour changes can occur before cooking.
Discolouration is thought to involve the enzymes polyphenol oxidase (PPO) and
peroxidase (POD) (Hatcher and Kruger 1993, 1996; Gooding and Davies 1997).
Enzymes, present largely in wheat bran, increase exponentially with increasing mill
extraction rates (Hatcher and Kruger 1993). Those enzymes caTalyze the oxidation of
free, reduced phenolic compounds to quinones, which react to form brown pigments. On
the other hand, based on a significant correlation (r : 0.83, P < 0.001) between flour
protein content and water activities of noodle doughs, Balk et al. (1995) indicated that
flour protein content affects the water activity of dough, which in turn influences the
discolouration of noodle dough as well. ln a cultivar, therefore, discolouration is more
affected by protein than by enzyrnes such as polyphenol oxidase (Baik et al. 1995).
Noodle Texture
Cooked noodle texture (the eating quality) is the most important assessment of noodle
quality. In contrast with colour, noodle texture characteristics are more complicated and
less well understood, because cooked noodle texture is a specific requirement according
to regional preferences. A bright, smooth white noodle with medium fîrmness and strong
chewiness is usually preferred in China, but soft texture is preferred in Japan (Huang and
Morrison 1988). Starch composition and pasting properties are well correlated with
Japanese white noodle texture, while protein content and quality influence Chinese white noodle texture (Oda et al. 1980; Huang and Morrison 1988; Toyokawa et al. I989a,b;Li 22
1996). Understanding how to measure and assess starch and protein quality is essential for any applied research in noodles (Huang and Morrison 1988; Crosbie 199I;Balk et al.
1994a; Bhattacharya and Corke 1996).
Effect of Starch on White Noodle Texture
Størclt Ptrstíng Propertíes
Australian Standard White (ASW) wheat was found to be superior to wheat from other sources in Udon processing characteristics (Nagao 1996). This was related mainly to its starch pasting characteristics (as measured on the amylograph or Rapid Visco Analyzer).
Comparing the starch pasting properties of ASW wheat, US Western White, and Japanese domestic wheat variety Norin 61, Oda et al. (1980) found that the total starch of ASW wheat and Japanese wheat had higher peak viscosity and lower gelatinisation temperature. These relatively low gelatinisation temperatures and high pasting viscosity were presumed to be factors contributing to soft and pliable noodles (Nagao et al. 1977,
Crosbie l99I; Konik et al. 1992; Jun et al. 1998). Starch swelling properties were also found to be measurable in a starch or flour swelling volume test, and a high volume was correlated with good Japanese noodle quality Qrlagao et al. 1977; Toyokawa et al.
1989a,b; Crosbie 1991; Konik et al 1993;Ytn et al. 1996; Wang and Seib 1996; Jw et al.1998). 23
Starch Composition øttd lts Effect on Starch Pastirtg and Nooclle Texture
Starch is a carbohydrate composed of two types of glucose polymers, amylose and
amylopectin. Amylose is made of straight chains and behaves as a linear polymer.
Amylopectin contains the same straight chains but also branches, producing a larger
molecule. Low-amylose wheat is also referred to as partial waxy wheat. The term "waxy"
was f,trst applied to amylose-free mutants of maize, referring to the waxy appearance of
the endosperm of dried kernels, compared with the flinty or translucent appearance of
wild-type kernels.
The ratio of amylose to amylopectin content determines the physicochemical properties
of starch and, therefore, the end-use of the wheat. Oda et at. (1980) found that starches
with lower amylose content tended to reach peak viscosity at a lower temperature and had
greater breakdown (amylograph paste stability). Moss and Miskelly (198a) also fo¡nd
that two cultivars with lower starch amylose, Gamenya and Halberd, had greater breakdown. ln a survey comparison of different sources of wheat flours, peak paste viscosity was significantly negatively correlated with apparent amylose content among starches obtained from several individual Australian cultivars (Moss i980; Moss and
Miskelly 1984).
Starch is synthesized in specialized organelles known as amyloplasts, where amylose production is regulated by a granule-bound starch synthase (GBSS) (Nakamura et al.
1993). Three genes encode GBSS in hexaploid wheat. Fuily waxy wheat (< I% apparent 24
amylose) has no (null) or non-functioning alleles at each of these loci. Wheat that is null
at one or two of these alleles is termed partial waxy wheat, and have 26-21% and 20-24o/o
apparent amylose content, respectively. Common wheat is called non-waxy wheat (a11
three GBSS genes present), and has 28o/o apparent amylose content (Seib 2000). Breeders
have been able to develop waxy or partially waxy wheat by hybridizing wheat carryring
various null alleles.
Waxy and partially waxy wheat starches demonstrate higher onset and peak gelatinisation
temperatures, enthalpy of gelatinisation, and degree of crystallization than non-waxy
wheat starch (Yasui et al. 1996; Demeke et al. 1999; Abdel-Aal et a\.2002). Waxy flours
had lower pasting temperature, and higher peak viscosity than their non-waxy parent
(Yasui et al. 1999). Using flours with a wide range of apparent amylose content (17.5-
23.5o/o), Zeng et al. (1997) found that higher peak viscosity, greater breakdown, lower
f,rnal viscosity, negative setback, and less total setback determined by a Rapid Visco-
Analyser (RVA) were associated with lower apparent and total amylose contents.
A negative correlation has been found between amylose content and Udon noodle quality
(Miura and Tanii 1994; Zhao et al. 1998). The decrease of amylose content allows
development of the smooth surface and soft texture which is important in Japanese noodle quality. Flour amylose content between 22-24% is often required for Japanese type noodle making (Hou 2001). There is approximately 40-50o/o of Australian wheat cultivars that is null at one allele (Seib 2000). The amylose content of prime starch in commercial Western Australia ASW was x3%o lower than in commercial US Western 25
White wheat, and three Japanese domestic cultivars (Endo et al. 7989). Therefore, the
lower level of amylose in Australian wheat explains why ASW is suitable for Japanese
noodle processing.
Recently, recombinant inbred lines of wheat with all combinations of the three waxy
genes were used to study the relationship between GBSS gene dosage and allelic
variation among the loci for white noodle texture as determined by texture profile
analysis (TPA) (Epstein et a\.2002). Noodles from normal starch lines had the hardest,
most adhesive and chewy, but least cohesive, springy and resilient texture, while noodles
from waxy lines had the softest, thickest, least adhesive and chewy, and most cohesive
and springy texture. Noodles from partially waxy lines generally were intermediate in
texture between normal and waxy lines.
Fractionation and Reconstitution Study
Fractionation and reconstitution methods are well known and often applied in evaluating
the role of flour components in bread-making quality (Weegels 1996). There are only a
few publications that investigated the role of flour components in white noodle quality
using fractionation and reconstitution methods (Oh et al. 1985c; Toyokawa et al. 1989a).
Toyokawa et al. (1989a) carried out an interchange of fractionated gluten, primary starch, tailing starch, and water solubles from wheat flours of ASW, US white soft and Japanese domestic to investigate the role of each in Japanese noodle quality. They found that 26
primary and tailing starch fractions were responsible for noodle texture, with the primary
starch fraction consistently having a greater effect on noodle texture than the tailing
starch fraction. The gluten fraction of the flour affected the noodle colour but not texture,
while the water-soluble fraction did not affect the cooked noodle texture or other
measured properties. Toyokawa et al. (I989b) observed in a later study that an optim¡m
ratio of amylose-to-amylopectin is necessary for good noodle quality, and increased
levels of amylose reduced the water binding of cooked noodles, resulting in firmer
noodles with loss of elasticity.
Using hard red winter wheat straight flours, Oh et at. (1985c) concluded that the gluten
fraction influenced cooked noodle firmness and surface firmness most. The primary
starch and water-soluble fractions did not affect any noodle quality factors, and the tailing
starch fraction was responsible for the colour difference and dry noodle strength.
The reason for the different results in the above two studies was presumed due to
different sets of samples. In the study by Oh et al. (1985c), two hard red winter wheat
flours with the same protein content and different protein quality were used, but the
differences of flour starch pasting properties were ignored. ln the study by Toyokawa et
al. (I989a), three soft wheat flours with different starch pasting properties and similar
protein content were used, but the differences of flour protein quality were ignored.
Effect of Protein Content and Quality on Noodle Texture 27
Although most studies on white salted noodles concluded that starch components
contribute to flour pasting properties and determine white noodle texture, Baik et al.,
(99aa) found that cooked white noodle texture, determined with TPA, was correlated
with protein content instead of parameters determined from swelling volume or the
amylograph. This result agreed with Oh et al. (1985b) who concluded that protein was
the main factor contributing to white noodle texture. Both protein content and quality are
important in noodle processing, cooking, and eating properties.
Protein Quøntity
It has long been known that flour protein content is associated with the bite of cooked
noodles (Nagao et al. 1977; Oh et al. I985b). Generally, flour protein content has a
positive correlation with noodle hardness and a negative correlation with noodle
brightness. High-protein noodles required lower optimum absorption, longer cooking
time, and gave a higher cutting stress than low-protein noodles (Oh et a/. 1985b). The
smoothness (surface firmness) of white noodles decreased as protein increased (Oh et al.
i985b). Low protein content causes soft texture and breakage during the drying process
(Oh et al' I985b; Huang and Lin 1990; Li 1996). Extremely low protein content and abnormally weak gluten reduce the quality and acceptability of the final product (Nagao
1996). However, flours containing high protein levels, especially too strong gluten, produce noodles with texture which is too firm and have problems with appearance and final product size (Li 1996). Thus, there is an optimum flour protein content required for 28
each noodle type (Hou 2001). Chinese white noodles are somewhat similar to Udon in
that both require flours that yreld brightness, but the flour protein content of Chinese
white is i0.5-i2.5o/o, and thus is higher than that of Udon noodle flour (g.0-9.5%) (Hou
2001).
Recently in China, new specifications were established for the quality of flour used in
noodle making' First and second grade noodle flour are required to have at least 28 and
260/o wel gluten content (reflecting protein content), and with at least 4.0 and 3.0 min
farinograph stability time, respectively (Huang 1996).
Protein Qualiþ
Besides protein quantity, protein quality is another important factor, especially when
requirements of processing quality, and cooked noodle texture are considered. Compared
to bread-making, however, there has been very little research undertaken to examine the relationship between specific proteins and noodle quality (Kruger 1996). In noodle studies, protein quality is usually assessed with SDS sedimentation volume (SDSV)
(Huang and Morrison 1988), SDS-FY test (Baik et al. lggilb), dough rheological properties including the farinograph (Moss et at. 1986), mixograph (Baik et al. I994a), alveograph (wang et al. 1995), and extensograph (Moss et al. 19g6). A stronger relationship was observed between SDSV and noodle texture (cutting force and texture profile analysis) than between protein content and noodle texture (Huang and Morrison
1988; Baik et al. 1994a). This supports the hlpothesis that both protein quantity and 29
quality should be considered in tests used to predict the textural properties of noodles.
Wang et al. (1995) reported that Chinese white noodle quality (sensory evaluation) is
signif,rcantly related to mixograph dough development time, SDS sedimentation volume,
and alveograph W index. Recently, in a survey on main wheat varieties from China,
similar protein content was observed between Chinese wheat and No. 1 CWRS wheat
(13.5), but the gluten strength, measured with sedimentation volume, was found to be
lower in Chinese wheat (30.3 mL) compared to CWRS wheat (50-70 mL) (Zhang and eu
1998). Therefore, blending Canadian wheat with Chinese wheat is a practical approach in
milling industry to improve end-use quality of chinese flours in china.
ln the study by Oh et al. (1985c), isolated gluten was further fractionated into gliadin and
both low and high molecular weight glutenin, using a lactic acid solution and
ultracentrifugation. The results of interchange and reconstitution between two varieties
showed that the high molecular weight gluten fraction contributed to the cooked noodle
f,trmness. lnterchanging other protein fractions did not influence the strength of cooked
noodles.
Similar to Chinese white noodles, protein quality affects the cooked texture of Japanese white noodles as well. To compare the physical properties of flours from different sources, Jun et al. (1998) found that the Japanese commercial white noodle flours and
Australian flours had a higher SDSV and a higher gluten index than soft white wheat flours from the US. This agreed with the result of Baik et al. (I994a) who indicated that 30 the low protein Japanese noodle flour obtained from Japanese millers showed relatively strong gluten (long dough mixing time). 31
CHAPTER 3
Swelling Index of Glutenin Test. I. Method and Comparison with Sedimentation, Gel-Protein, and Insoluble Glutenin Testsr
ABSTRACT
Molecular weight distribution of wheat proteins is primarily responsible for the viscoelastic properties of flour dough. Furthermore, the amount of sodium dodecyl sulphate (SDS) insoluble proteins (mainly high molecular weight glutenin) plays the major role. We have developed a simple test to determine the swelling power of glutenin (swelling index of glutenin (SIG)) for predicting dough properties and bread-making quality. Flour samples (a0 mg) were hydrated in distilled water and then allowed to swell in non-reducing solvents (SDS, lactic acid, or mixtures of the two) followed by low speed centrifugation. The SIG was calculated as the weight of the residue divided by the original sample weight. The SIG test was compared to the results from other small-scale tests for 20 flour samples. SIG tests showed highly significant correlations with the gel protein and insoluble glutenin test (r > 0.85, r > 0.93, P < 0.001, respectively) and significant corelations with the SDS and Zeleny sedimentation tests (r > 0.74, r > 0.72, p <
0.001, respectively). It was found that the swelling capacity of glutenin depended on swelling time and mixing intensity in non-reducing solvents. Swelling cllrves obtained from SIG values vs. different swelling time can be divided into three distinct stages: the swelling, swollen and breakdown stages, which may reflect soluble and insoluble glutenin contents and quality among different cultivars. SIG test values for short swelling time and low mixing intensity were significantly correlated to gel protein content and SDS-sedimentation values (r:0.96, r: 0.90,
I Pnblished in cereal chemistry Qo}z) 79(2):r83-rl9 by c. wang, and M. I. p. Kovacs 32
P < 0.001, respectively). SiG test values for long swelling time and high mixing intensity were significantly correlated to insoluble glutenin content (r:0.96, P < 0.001). The difference of swelling condition (time and mixing intensity) among these small-scale methods is the reason for their different correlations with insoluble glutenin content. Because large numbers of samples can be analyzed in a short time with excellent reproducibility, the SIG test may be a useful screening test in a breeding program, predicting the quantity and quality of insoluble glutenin. JJ
INTRODUCTION
Protein quantity and quality are important attributes in the varietal variation of the breadmaking
quality of wheat flour (Finney and Barmore I94S). The insoluble glutenin content, which is
considered to be a key expression of protein quality, is directly related to breadmaking quality
(Orth and Bushuk 7972, Gupta et al. 1992, Bean et c¿\. 1998, Sapirstein and Fu 1998). Glutenin
has the important property of swelling in various non-reducing solvents (dilute acetic acid, lactic
acid, and sodium dodecyl sulfate (SDS)) and the swelling volume appears to be directly related to quantity and quality of glutenin present (Weegels et ctl. 1996). Therefore, small-scale tests used to predict breadmaking quality are based on glutenin swelling capacity or directly on insoluble glutenin content.
The Zeleny (Zeleny 1947) and SDS-sedimentation tests (McDermott and Redman 1977) measure the sedimentation volume of a suspension of flour in dilute lactic acid and in SDS-lactic acid solvents, respectively. The sedimentation volume depends mainly on the amount, and the swelling characteristics of the glutenin, since other proteins such as gliadin are soluble in the
Zeleny and SDS test solutions (Echert et al. 1993). The SDS-sedimentation test correlates better to loaf volume than the Zeleny sedimentation test (Axford et al. 1979) and therefore a number of modified SDS-sedimentation tests have been developed (Kovacs 1985, Pena et al. 1990) and are widely used in predicting dough properties and breadmaking qualities in the early stages of wheat breeding programs (Weegels et al. 1996).
The determination of gel protein, also called glutenin macropolyner (Weegels et al. 1994) is a 34
more direct test in which the swollen insoluble glutenin forms a gel-like layer on top of the
starch pellet when SDS extracts are centrifuged (Graveland et al. 1979). The high centrifugation
speed separates the starch from the swollen glutenin and the weight of the remaining gel protein
is an index of the quantity of insoluble glutenin present (Graveland et at. I98Z).
The most direct means of determining the insoluble glutenin content is by a sequential extraction procedure (Orth and Bushuk 7912, Orth and O'Brien 1976, Fu and Sapirstein 1996) but the protein fractionation and Çeldahl determinations make it a complex and time-consuming process. ln a recent report, monomeric protein and soluble glutenin were extracted with 50% | propanol and the remaining protein pellet analyzed via nitrogen combustion (Bean et al. 7998), a method which is quicker than Kjeldahl.
Although the glutenin swelling characteristics is the basis of sedimentation and gel protein tests, little is known of its role in these tests. In this report, the effects of various swelling conditions and various swelling solvents on glutenin swelling property were investigated, and a test to determine the swelling power of glutenin (swelling index of glutenin or SIG) was developed.
The test used only a small sample size- which is important in program breeding - and had the added advantages of being simple and quick. The implication of differences among Zeleny, SDS sedimentation, gel protein and SIG tests is discussed on the basis of glutenin swelling properties and glutenin fractions (soluble and insoluble glutenin). 35
MATERIALS AND METHODS
Wheat Flours and Small-scale Tests
A set of 20 cultivars and breeding lines including six Australian cultivars was chosen to represent a wide range of dough rheological properties and breadmaking quality (Table 3-l).
They were field grown in four replicates at two locations in 1996 in Saskatchewan (Swift
Current and lndian Head), Canada. The replicates were bulked and a composite of each sample was made from each location, using equal weights. Composite grain samples were tempered to
15 and 16% moisture content according to grain hardness, and milled into straight grade flour on a Buhler laboratory mill.
The SDS-sedimentation test was carried out according to Kovacs (1985). The Zeleny sedimentation test was performed as specified by AACC Approved Method 56-60 (AACC
2000). The gel protein (based on Graveland et al. 1979) was determined by the following procedure. Flour (1.5 g) was suspended in 15 mL distilled water by intermittent vigorous hand shaking for 20 min and then 15 mL of 3% SDS was added. The mixture was swollen for another
20 min at 24 x 1'C with intermittent vigorous hand shaking. The suspension was centrifuged at
60,000 x g for 30 min at 15oC, and the clear supernatant was discarded. The gel layer was recovered using a spatula, and weighed. The gel protein was calculated as the weight of the gel divided by the original sample weight. 36
Table 3-1 Test Cultivars and Their Flour Protein Content, SDS Sedimentation Volume, Zeleny Sedimentation Volume, Gel-Protein, and Insoluble Glutenin Content
Wheat Class SDSV Gel Protein Neepawa CWRS 13.36 60 47 3.06 3.36 Biggar CPS 10.42 75 50 2.93 2.65 Genesis CPS 10.37 67 47 2.73 2.55 Fielder CWSWS 10.17 51 35 2.07 1.76 Hartog Australian 1 1.33 70 49 3.17 3.26 Suneca Australian 13.05 85 54 3.70 4.00 Sunco Australian 11.97 B6 52 s.30 3.37 Banks Australian 11.76 70 47 3.02 2.70 Timgalen Australian 13.44 71 46 3.18 3.27 Torres Australian '1 1.68 70 47 3.14 2.67 HY 367 CPS 11.03 78 50 2.91 2.83 AC Karma CPS 11 .11 64 47 2.88 2.78 Alphal6 CPS 1'0.22 43 34 1.93 2.15 HY616 CPS 1A.87 bb 49 2.59 2.87 R14555 Unknow 11.97 74 54 2.87 3.24 90w950 Unknow 1O.BB 49 36 2.16 2.63 Glenlea CWES 12.53 80 48 3.34 3.95 AC Vista CPS 1 1.39 71 44 3.01 3.12 HY 627 CPS 10.82 BO 51 2.93 2.77 AC Domain CWRS 14.27 67 46 3.37 3.7A Pro:flourproteincontent(%);SDSV:SDSsedimen sedimentation volume (mL); IGF : the percentage of insoluble glutenin in flour; CWRS : Canada Western Red Spring wheat; : : CV/ESRS Canada Western Extra Strong Red Spring wheat; CPS Canada Prairie Spnng wheat; CWSWS : Canada Wesrern Soft White Spring wheat.
Determination of Soluble and Insoluble Glutenin in Flour
Proteins were fractionated according to a modified procedure of Fu et at. (I998a), and the
protein fractions were detetmined by measuring the turbidity of the protei¡ precipitate with
trichloroacetic acid based on a sirnple assay for the determination of protei¡s in solution (Choi et al' 1993). This procedure classified the proteins into three fractions, monomeric protein, soluble glutenin, and insoluble glutenin. After removing all the monomeric proteins using the method of
Fu and Kovacs (1999) from 100 mg flour, the soluble glutenin was extracted with 3 x 1.0 mL of )ta-
40% (v/v) 1-propanol for 30 min each time. The suspensions were centrifuged at 15,000 x g for
5 min, and the three supernatants were pooled. Similarly, insoluble glutenin was extracted from
the residue with 3 x 1.0 mL of 40o/o (v/v) 1-propanol containing 0.2 %(w/v) dithiothreitol (DTT)
at 60'C for 30 min each time, and the three supernatants were pooled after 15,000 x g for 5 min.
A mixture of 40o/o l-propanol with or without reducing agent is more efficient than 50% l-
propanol for extracting soluble glutenin or insoluble glutenin (Wang et al. 7998). Subsamples
(0.4 mL) of supernatant for soluble or insoluble glutenin determinations were mixed with 4 mL of 40o/o (w/v) trichloroacetic acid and after standing for 35 min the turbidity was determined at
590 nm. Calibration curves for soluble and insoluble glutenin were prepared based on standard wheat flour (cultivar Genesis) extracts obtained in the same procedure as described above.
Protein content of soluble and insoluble glutenin fractions obtained from Genesis was determined by AACC Approved Method 46-13 (l/ x 5.7)(AACC 2000). The calibration curves were developed by diluting the known protein content fractions with 4Tyo l-propanol.
Absorption values at 590 nm for soluble and insoluble glutenin were converted to protein concentration, expressed in terms of the percentages of flour.
Swelling Index of Glutenin Test in SDS-Lactic Acid (SIG)
Flour (a0 mg) was weighed into a 1.5 mL pre-weighed plastic microcentrifuge tube, and 0.6 mL distilled water was added. The tubes were quickly capped and mixed thoroughly, using a single- tube vortex stirrer (Vortex Genie 2, Scientific Industries, Bohemia, NY) for five seconds. The contents of the tubes were then mixed in a thermomixer (Eppendorf model 5436, Brinkmann
Instruments, Inc.,'Westbury, NY) at 1400 rpm for 20 min with a 5 sec vortexing at 10 and 20 38
min. After the hydration, 0.6 mL SDS-lactic acid stock solution as described in AACC Approved
Method 56-70 (AACC 2000) was added and the tubes vortexed for 5 sec. The tubes were then
placed in the thermomixer at 1400 rpm for 20 min with a 5 sec vortexing at 10 min and at 20 min
followed by immediate centrifugation at 300 x g for 5 min (Micromax model, lnternational
Equipment Company, Needharn Height, MA). The bulk of the supernatant and foam on the
surface was removed quickly with a 1 mL syringe connected to a water aspirator filter pump, and
the residue was re-centrifuged at 300 x g for 2 min. The remaining supernatant was then drawn
off with the syringe equipped with a needle, taking care not to contact the resid¡e's surface. The tubes and their precipitates were then weighed and the SIG calculated as the weight of the
"swollen" precipitate divided by the original sample weight (14% moisture base).
Swelling Index of Glutenin Test in Dilute Lactic Acid (SIG-LA)
The procedure \ /as similar to the SIG procedure except that initially 0.8 mL distilled water was added to 40 mg flour, the thermomixer mixing time was 10 min followed by the addition of 0.4 mL isopropanoi-lactic acid stock solution prepared according to AACC Approved Method 56-60
(AACC 2000) with another 10 min thermomixer mixing period and a final centrifugation at 100
"Þ'VO
Swelling Index of Glutenin in SDS (SIG-SDS)
The procedure was similar to SiG except that the centrifugation speed was i000 x g and the SIG stock solution was replaced by 3% SDS. 39
Statistical Analysis
All measurements are averages of two determinations. Statistical analyses were performed using
the data analysis tools of Microsoft Excel 97.
RESULTS AND DISCUSSION
Influence of Various Factors on SIG Value
The influence of mixing intensity, the ratio of solvent-to-flour, hydration and swelling time, and centrifugation speed and temperature on the SIG tests were investigated using strong, medium and weak gluten type cultivars (farinograph dough development time 3.1,4.3 and 8.6 min for
Alpha 16, Banks, and Suneca, respectively). Table 3-2 shows the effects of mixing intensities
(mixing in the thermomixer combined with vortexing vs. hand-shaking) on the SIG. The strong variety Suneca required high mixing intensity (vorlexing) to obtain its higher SIG value while the medium and weak varieties Banks and Alpha 16 only needed low mixing intensity (hand- shaking). This indicates that the glutenin in a strong variety is tolerant to vigorous mixing condition. The decreased SIG values of Banks and Alpha 16 under high mixing intensity may be due to some of the insoluble glutenin (under low mixing intensity) becoming soluble, a phenomenon also seen during the fractionation of gel-protein (Graveland et al. IgTg).
Temperature effects on the SIG tests were also variety dependent: SIG values increased for strong varieties but decreased for weak ones at high temperature (Fig. 3-1). The SIG test should 40
therefore only be performed at a constant room temperature (24 t 1'C) in order to avoid the
different responses to temperature with different varieties. The use of a correction formula for
SIG values at different temperatures may not be valid because different varieties have different
temperature responses.
Table 3-2 Effects of Mixing Intensity on the SIG and sIG-sDS Testsr
SIG SIG-SDS Suneca Banks Alpha 16 Suneca Banks Alpha 16 Vortexing and nrixing 6.65 4.45 3.28 7.15 4.46 3.8 2 Handshaking 6.55 5.6 4.21 6.95 5.53 4.4 LSD', 0.1 0.1 0.1 0.15 0.15 0.15 'sI o¡ glutenin test in SDS solution. 2 Vortexing and mixing in thermomixer were replaced by handshaking and resting. 3 Least significant difference at 5o/o level, based on anaiysis of variance. Difference between two means exceeding this value are significant.
8.0
7.O
6.0 ^Þ I
õ s.o
4.0
30 10 Temperalure, 'C
Fig. 3-1. Effect of temperature on SIG. Error bars indicate standard deviations (n:2). Suneca (f); Banks (V); Alpha 16 (O).
All three varieties showed higher SIG values as hydration time increased (Fig. 3-2). On the other hand, the hydration in the procedure adds to the convenience and precision of the method 4I
because flour easily suspends in water, and sticky, inner dried particles, as occurs during direct
swelling by SDS, are thus avoided.
8.0
7,O
^o 6.0 o
õ u.o
4.0
3.0
Hydration ïme, mÌn
o o ôØ Ø
Ø
20 -f¡me, HVdralìon min
Fig. 3-2. Effect of hydration time on SIG (A) and SIG-SDS (B) tests. Error bars indicate standard deviations (n:2). Suneca (I); Banks (V);Alpha 16 (O).
The SIG values of the three varieties had different responses to increased swelling time (Fig. 3-
3). The SIG values for the strong variety (Suneca) increased during the first 15 min of swelling 42
and then stayed constant for any additional swelling time. The SIG values of the weak variety
(Alpha 16) dropped rapidly during the first 5 to 15 min and then more slowly with prolonged
swelling time. Interestingly, the SIG for Banks increased to a peak after 5 min and then dropped
dramatically. This phenomenon was seen during the FY sedimentation test (Kruger and Hatcher
1995) where values increased for strong varieties but decreased for weak ones over time. The
different changes among the three varieties of wheat may be due to different solubility rates of
their glutenin. The glutenin of a strong variety is solubilized at a slower rate than that of weak varieties during prolonged SDS extraction (He et al.I99I). To obtain maximum variation of SIG value among varieties, 20 min of swelling time was chosen for a routine SIG test as described in the method section.
The SIG test is also affected by the solvent-to-flour ratio (Fig. 3-a). The SIG values are higher with a lower ratio because the solvent is unable to effìciently extract all of the soluble glutenin and a portion of the soluble glutenin remains swollen in the residue. A sample size of 35-45 mg seems optimal when I.2mL solvent is used for swelling glutenin.
As expected, SIG values decreased as centrifugation force increased (results not shown). In order to maximize differentiation among varieties, a low centrifuge force is recommended in the SIG test (as described in method section), but it is difficult to separate supematant from residues if the centrifuge force is too low. Exact centrifuge force is critical in achieving reproducible SIG values between tests. 43
10 20 30 Swelling Time, min
10 20 30 Swelling Time, min
Fig. 3-3. Effect of swelling time on SIG (A) and SIG-SDS (B) tests. Vortexing for 5 sec at beginning and ending of swelling period, and vortexing for 5 sec at every interval of 10 min. Error bars indicate standard deviations (n: 2).Suneca (I); Banks (V); Alpha 16 (O).
Our results showed that glutenin swelling is influenced by the mixing intensity, the ratio of solvent-to-sample, and the swelling time and temperatures. The strong swelling treatment (high mixing intensity, high solvent-to-sample ratio, high temperature and long swelling time) allow efficient extraction of the soluble glutenin and results in complete swelling of the insoluble glutenin. Optimizing the test conditions enhances the differentiation of SIG values among 44
varieties and this differentiation reflects the insoluble glutenin content among varieties.
30 40
Solvenl: sample Ral¡o, mUg
8.0
7.O -at â Ëoo ôõ Ø 9 s.o Ø T.
4.0
3.0 20 30 40 50 Solvent: sample Ratio, mUg
Fig. 3-4. Effect of solvent:flour ratio on SIG (A) and SiG-SDS (B) tests. Error bars indicate
standard deviations (n:2). Suneca (l); Banks (V);Alpha 16 (O).
The three solvents that were used in the SIG tests gave swelling volumes of glutenin in the order of SIG-SDS > SIG > SIG-LA. A solvent containing SDS is recommended because the swelling volume of insoluble glutenin is greater in SDS than in lactic acid and the separation of the supernatant from the swollen glutenin by syringe is also easier. The highest SIG values were 45
obtained using 1.5% SDS as the solvent. However, the swollen glutenin of some samples was so
watery that a higher centrifugation speed was required to separate the supematant from the
residue and for this reason, we recommend a solvent composed of SDS and lactic acid.
Reproducibility of the SIG Test
The reproducibility of the SIG test with the three solvents was determined by using the three wheat varieties with high, medium, and low SiG value in 24 replicates under optimum conditions as in the method (Table 3-3). The coefficients of variation (CV (o/o)) ranged from 1.16 to 2.97 jn the SIG, from 2.28 to 7.57 in the SIG-SDS and from 0.97 to 2.04 in rhe SIG-LA. The stronger gluten type cultivar Suneca with its highest SIG values and firm residue gave the best reproducibility.
Table 3-3 Reproducibility of SIG Tests on Three Diverse Wheat Varieties (n :24)o
SIG SIG-SDS SIG-LA Suneca Banks Alpha 16 Suneca Banks Alpha 16 Suneca Banks Alpha 16 Range 6.63-6.95 4.48-4.86 3.46-3.91 6.61-7.40 4.24-5.11 3.48-4.01 4.72-4.9r 3.64-4.14 323-3A8 Mean 6.8 4.6 3.61 7.05 4.62 3.81 4.81 3.9 3.38 sDb o.o7g2 0.0839 o.lo9 0.16 0.35 0.134 0.0468 0.149 0.0691 1.82 2.69 2.28 7.s7 o SIG : swelling index of glutenin test in SD glutenin test in SDS soltltion; SIG-LA: swelling index of glutenin test in lactic acid solution. b Standardo,- r- i deviation.i ' Coefflcient of variation.
Correlations Between SIG, SIG-LA, SIG-SDS, Gel Protein, SDS-sedimentation, Zeleny
Sedimentation and Insoluble Glutenin Content 46
The relationships between SIG tests, sedimentation tests, and gel protein with percentage of
insoluble glutenin in flour (IGF) are shown in Table 3-4. All of the correlation coefficients were
positive and significant. The SIG, SIG-SDS, and SiG-LA had a strong linear correlation with
IGF. However, the sedimentation volumes (SDS and Zeleny) had lower correlation coefficients with IGF. The correlation of SDS-sedimentation with IGF in tliis study was higher than that obtained by Blackman and Gill (1980), but similar to Dachkevitch and Autran (1989). The order of conelation of these tests with IGF was: SIG > gel protein > sedimentation. Although sedimentation, gel protein, and SIG tests are based on the quantity and quality of insoluble protein, the results suggest that SIG tests are more closely related to insoluble glutenin content than gel protein or sedimentation tests. Sedimentation tests are the poorest predictors of insoluble glutenin content in these three tests. The correlation coefficients among small-scale tests are listed in Table 3-4. Strong linear relationships were found between different SIG tests, while two sedimentation tests displayed a good linear relationship as expected. The correlation coefficients also showed that gel protein fell between SIG and sedimentation tests. Mixing intensity and centrifugation force account for the differences obtained from sedimentation, gel protein and SIG tests. The low mixing intensity in the sedimentation test may not be adequate to extract all of the soluble glutenin, and the insoluble glutenin may not swell fully in the sedimentation tests. The suspending property of swollen insoluble protein probably affects the sedimentation volume. The centrifugation in SIG test forces the swollen insoluble glutenin and starch particles together, eliminating possible effects of different glutenin suspending properties among the varieties. The high mixing intensity and long swelling time in the SIG test provide similar conditions to that in the procedure of IGF test. Consequently, the SIG test mainly measures the swelling capacity of 47
insoluble glutenin, and reflects the content of insoluble glutenin.
Table 3-4 Correlation Coefficients Between SIG Test, Sedimentation Tests, Gel Protein and Percent of lnsoluble Glutenin in Flouro,b
SIG SIG-SDS SIG-LA Gel Protein SDSV Zeleny IGF SIG 1.00 0.99+>k* 0.97*** 0.93*** 0.74>kx* O.J2*** 0.g6:*'¡i¡ SIG-SDS 1.00 0.96**+ 0.96+++ 0.J4.N+,k 0.72*++ 0.g5*** SIG-LA L00 0.95*;r,r, 0.74*** 0.75**;k 0.93>r.,¡* Gel Protein 1.00 0.93:*** 0.79ìk** 0-g5:3.*i¡ SDSV 1.00 0.90:È,ß* 0.64+* Zeleny 1.00 0.63** " src o¡ glutenin test in SDS solution; SIG-LA : swelling index of glutenin test in lactic acid solution; SDSV : SDS sedimentation volume; rGF : the percentage of insoluble glutenin in flour. b **, *** significant al lo/o and 0.I%olevel,respêctively.
Effects of Swelling Property of Glutenin on Sedimentation, Gel Protein, and SIG Tests
To investigate the performance of glutenin in the swelling processes, SIG values for 20 wheat varieties were determined with different swelling times from 0 to 7O min. According to SDS- sedimentation volume (SDSV), three strong varieties (SDSV over 80 mL), three weak varieties
(SDSV below 60 mL), and three medium varieties (SDSV around 70 mL) in the 20 test samples were chosen, and their SDSV, SIG values and glutenin composition are presented in Table 3-5.
Their swelling cufl/es are presented in Fig. 3-5. The process of glutenin swelling in the non- reducing solvent can be divided into three stages. ln the first stage (swelling stage), the glutenin began to swell to its peak volume. The glutenin from strong wheat needs a long time to reach its peak volume, while the weak wheat immediately reached its peak volume. In the second stage
(swollen stage), the swollen glutenin maintained its state and had the peak SIG value. The 48
glutenin from strong wheat kept its peak state much longer than that of weak wheat. ln the third
stage (breakdown stage), the glutenin loses its swelling ability, probably because parl of the
insoluble glutenin starts to dissolve.
The different varieties have totally different behaviors in the three stages. Generally, the swelling
process of a strong variety is longer than that of a weak vanety in both the swelling and swollen
stages, while in the breakdown stage, the value of SIG falls a little (eg., Glenlea and Suneca in
Fig. 3-5 A). Sunco had the highest SDS sedimentation value among the three strong varieties
(Table 3-5), but its insoluble glutenin content was low compared with that of Glenlea and
Suneca. On the other hand, the range of SIG values of the three varieties was similar to the range
of insoluble glutenin content. Furthermore, the insoluble glutenin contents of both Suneca and
Glenlea were almost the same, but their swelling curves were different. It took about 20 min for
Suneca to reach its peak value, while Glenlea required almost 50 min. This indicates that the
quality of glutenin (strength) also affects the swelling properties of glutenin. An extra lo¡g time
for swelling of Glenlea could be related to its extra strong gluten properties (farinograph dough
development time,l5.6 min). ln the medium group, the three varieties had almost the same value
of SDS-sedimentation, but their SIG values and insoluble glutenin contents were different (Table
3-5). Although the SIG values at 0 swelling time of the three varieties were almost the same,
Timgalen which had a higher insoluble glutenin content, had higher SIG values during the
swelling stage than the other two varieties which had lower insoluble glutenin content (Fig. 3-58
and Table 3-5). SIG values are, therefore, mainly dependent on insoluble glutenin content. In the weak variety group, the swelling and swollen stages were short, and for some varieties, the SIG 49
Table 3-5 Comparison of SDSV, SIG, Insoluble Glutenin Content and Soluble Glutenin Content ø. tt r.. Cr""pr St "f Strong Varieties Medium Varieties Weak Varietiesrr rçtreÐ LSD b Glenlea Suneca Sunco Banks Tim Torres Fielder 'r6 90w950 SDSV BO 86 70 71 70 51 43 49 2.88 stc 6.34 6.81 5.89 4.61 5.20 4.73 3.00 3.50 4.13 0.20 IGF 3.95 4.00 3.37 2]0 3.27 2.67 1.76 2.15 2.63 0.21 1.24 sG/F 1.67 1.39 1 .BB I .88 1.71 1.80 1 .19 0.72 0.29 SDSV:SDSsedimentationvolume,",.:swellingindexofgluteninM solution; IGF : the percentage of insoluble glutenin in flour; SG/F : the percentage of soluble glutenin in flour. b Least significant difference at 5Yo level, based on analysis of variance. Difference between two means exceeding this value are significant
method was not able to detect a swelling and swollen stage (eg., Fielder in Fig. 3-5C). The SIG value of Fielder which had the highest SDS-sedimentation value and lowest tcF among the three weak varieties (Table 3-5), dropped sharply as swelling time increased. The high soluble glutenin content in Fielder could account for its high SDS sedimentation volume and sharp decrease in its swelling curve (Table 3-5). We postulate that the soluble glutenin swelled quickly at short swelling time and low mixing intensity (as swelling condition in SDS sedimentation test and 0 swelling tirne) and then dissolved in SDS solution as swelling time increased. Since the insoluble glutenin content was low for Fielder, the increase resulting fi'om completely swollen insoluble glutenin could not compensate for tlie bigger decrease resulting from dissolving soluble glutenin, inducing the sharp decrease of SIG value. The ratio of insoluble-to-soluble glutenin in
90W950 was higher than that in Fielder (Table 3-5), explaining why 90W950 had a lower sedimentation value and SIG value at 0 min than those of Fielder. On the other hand, as swelling time increased, the decrease of SIG value of 90W950 was small (Fig. 3-5C), since its content of soluble glutenin was less than that of other weak varieties. Although mechanisms that determine the solubility and swelling ability of glutenins are not well known, we presume that content and 50
20 30 40 50 Swelling T¡me, min
20 30 40 Swellìng lime, min
20 30 40 50 Swelling Time. min
Fig. 3-5. Swelling curves for three groups of samples. A: strong varieties. Glenlea (l); Suneca(V); Sunco (o). B: medium varieties. Banks (r); Timgalen (V); Torres (o). i: leak varieties. Fielder (l); Alpha 16 (V); 90W950 (O). Vortexing for 5 sec ar beginning'and ending of swelling period, and vortexing for 5 sec at every interval of 10 min. Enor bars indicate standard deviations (n : 2). 51
quality of glutenin are the main determinants of the swelling property of glutenin. The content of
glutenin, especially insoluble glutenin, governs the SIG value determined under optimum
conditions, and glutenin quality may control the glutenin swelling curve. SIG can be used to
evaluate flour quality between varieties which have a wide range of quality. For the varieties
which have a naffower range of insoluble glutenin content, the difference between Suneca and
Glenlea, for example, swelling curves differentiate the quality of glutenin (Wang and Kovacs
2002b). Comparing the differences of glutenin content and SIG value between 90W950 and
Tottes, Torres had a higher SIG value and similar insoluble glutenin content compared with that of 90W950. In this case, the difference may be attributed to a different content of soluble glutenin and different swelling properties of insoluble glutenin. Overall, the sedimentation tests are determined by both soluble glutenin and insoluble glutenin content, since the mixing intensity is too low for dissolving the soluble glutenin and the soluble glutenin also contributes to the sedimentation volume.
Flour particle size influences the swelling capacity of glutenin. The large particle size of semolina had lower SIG value than that from ground semolina (Wang and Kovacs 2002c). It is well known that flour from hard wheats contains more damaged starch than flour from soft wheats. However, Schlesinger (1964) reported that the effect on Zeleny sedimentation values of increased starch damage by ball-milling of previously tested flours was negligible. Similarly, correlations of damaged starch and particle size index with SIG values were not significant.
When SIG values were determined with SDS solvent containing 2 mM DTT, the correlation coefficient of the SIG values with the IGF was significantly lower (r:0.27), as compared to values obtained without DTT ( r:0.96, P < 0.001). The range of SIG values from 20 samples 52
changed from 3.00-6.8i (without reducing agents) to 1.15-2.04 (with 2 n}y'-DTT). The results of
the subtraction of SIG values (with 2 mM DTT) from SIG values without reducing agents were
highly significantly correlated with IGF ( r:0.91, P < 0.001). Apparently, the possible effect of
damaged starch and flour particle size is negligible in the SIG test, and the results suggest that insoluble glutenin is the main factor responsible for SIG values.
Table 3-6 Correlation Coefficients of SiG (with different swelling time) with Gel protein, Sedimentation Tests, and insoluble Glutenin Contentl'2
Time (min Gel Protein 0 0.96""* 0.90*** 0.85"** 0.90*** *** 2 0.97*** 0.8 1 0.90*** 0.90*** 5 0.96*** 0.78*** 0.79*** 0.92*** 7 0.95*** o.77*** 0.75*** 0.94"** 10 0.94*"* 0.77*** 0.79*** 0.94*** '15 0.93**" 0.76*** 0.73**" 0.95*"* 20 0.93*"* o.74**" 0.72*** 0.96"** 30 0.90*** 0.76*** 0.72*"* 0.96*** 40 0.96*** 0.73*** 0.70"** 0.95*** 50 0.86*** 0.77*** 0.7 1*** 0.94*** 60 0.86*** 0.75*** 0.70*** 0.93*"* 70 0.84*** 0.74""* 0.69*** 0.93***
' SOSV: SDS sedimentation volume; IGF: the percentage of insoluble glutenin z in flour. xxx r¡*ificant at0.1o/olevel. 3 Vortexing for 5 sec at beginning and ending of swelling period, and vortexing for 5 sec at every interval of 10 min.
Correlation coefficients of SIG with sedimentation tests were higher at short swelling time than at long swelling time (Table 3-6). Gel protein was strongly related to SIG at a swelling time from
0 to 7 min. When swelling time increased, the corelation coefficients of SiG with insoluble glutenin content increased. Mixing intensity and swelling time were the main reasons for differences between the tests, and additional information about protein quality can be obtained 53
from SIG tests. The SIG value obtained at short swelling time and low mixing intensity is a good
predictor of glutenin quality which corresponds to sedimentation and gel protein tests, and the
SIG value obtained at long swelling time and high mixing intensity is a good predictor of
insoluble glutenin content.
Comparison of SIG and Sedimentation Test Methodology
SIG and sedimentation tests are simple and can quickly predict the quality of wheat, but the SIG test's advantage over the sedimentation test is the high correlation with insoluble glutenin content. The sedimentation boundary is difficult to identify in some varieties because the supernatant is too turbid. To get a clear supernatant in the SDS sedimentation test, an increase in concentration of lactic acid is needed. This problem is overcome in SIG tests by centrifugation.
The sedimentation tests require strict conditions, especially the sedimentation time in the Zeleny test. When the sedimentation volume is recorded at different times, the results vary. On the other hand, the conditions are easily controlled in SIG tests and the SIG procedure does not require a set of uniform and scaled leak-proof tubes which have to be carefully cleaned after each use.
Compared with determinations of gel protein and insoluble glutenin, the SIG test can be conducted quickly with simple equipment, and 24 samples can be tested at one time. Among the various small-scale quality test methods, SIG tests require the smallest amount of sample, an important consideration for wheat breeders. 54
CHAPTER 4
Swelling Index of Glutenin Test. II. Apptication in Prediction of Dough Properties and End-use Qualityr
ABSTRACT
Small-scale tests including SDS and Zeleny sedimentations, gel protein, insoluble
glutenin content, and a newly developed method, the swelling index of glutenin (SIG),
were compared with dough and gluten rheological parameters and end-use quality
parameters for 20 wheat cultivars and/or breeders lines. The SIG test is equal to or
slightly better than the other small-scale tests in prediction of dough strengfh. Quality
parameters were divided into two gror-rps according to their associations with insoluble
gh-rtenin content and glutenin quality. The glutenin quality is defined as the glutenin
swelling properties with short swelling time (up to 5 min) which are contributed by
soluble and insoluble glutenin content and their swelling properties. Parameters in the
first group were mainly dependent on insoluble glutenin content and appeared to reflect
gluten strength. Parameters in the second group were not only dependent on glutenin
content but also on glutenin quality. Small-scale tests are best to predict quality
parameters in the same group, but not those in the other group. The glutenin swelling
curve, obtained with different swelling times, was correlated with mixograph or farinograph. Dough development time in farinograph and mixing time in mixograph were strongly related to the swelling time of peak SIG value in the swelling curve (r:0.92, r : 0.86, respectively, P < 0.001). Farinograph stability was significantly related to the t Published in cereal chemisrry (2002) 79(2):190-196 by c. wang and M. I. p. Kovacs 55 time of swollen stage in swelling curves (r :0.62, P < 0.01). Similar to mixograph or farinograph, the glutenin swelling curves can be used to differentiate some strong varieties that cannot be differentiated by sedimentation, gel protein, and insoluble glutenin values. 56
INTRODUCTION
The impact of protein content and protein quality on breadmaking quality of wheat flours
was shown by Finney and Barmore (1948), and was well documented (MacRitchi e 1992).
Most evidence suggests physical dough properties, especially those associated with
dough strength, and baking properlies, are determined mainly by qualitative and
quantitative properties of the glutenins. The relationships between glutenin content and
dough properties and baking quality have been established by reconstitution studies,
solvent fractionation studies, and molecular weight distribution obtained by gel filtration
and size exclusion HPLC (Weegels et al. 1996). Protein quality, therefore, was defined
by insoluble glutenin content (Orth and Bushuk 1972). Glutenin quality is more complex
than protein quality, and it is a vague concept that is easier to realize than to define. The
definition of gh-rtenin quality in the 1980s was enhanced by obtaining the high molecular
weight glutenin subunit composition (Payne et at.1987), and cereal chemists now believe
that the amount and composition of high and low molecular weight glutenin subunits
determine glutenin quality. ln addition, the molecular weight distribution of glutenin, another important concept for glutenin quality, is certain to be a key factor in the variations of dough strength (Huebner and Wall 1976, Ewart 1987, Huang and Khan
1997, Southan and MacRitchie 1999). However, techniques for determining glutenin molecular weight distribution are not readily available, and the concept of glutenin quality is mainly based on the content of insoluble glutenin or the ratio of peak area for higher molecular weight glutenin to the peak area for lower molecular weight glutenin from chromatographic methods (Southan and MacRitchie Iggg). The glutenin swelling 57
curves, based on the swelling index of glutenin test (SIG) with different swelling times,
reflect the glutenin swelling properlies (Wang and Kovacs20O2a). At a short swelling
time, the soluble glutenin is in a state from swelling to dissolving, while insoluble
glutenin is in a state from semi-swelling to completely swelling. Therefore, similar to
SDS and Zeleny sedimentation volumes and gel protein content, the SIG value,
determined with short swelling time, is influenced by soluble and insoluble glutenin
content and their swelling properties, and is a parameter which reflects glutenin quality.
As the swelling time increased, soluble glutenin is dissolved in the solvent and the SIG
value is mainly based on the insoluble glutenin content (Wang and Kovac s 2002a).
The development of fast prediction methods for end-use quality continues to be a major
focus of wheat breeding programs. As discussed above, ideally screening test methods
should differentiate protein quality of wheat cultivars for end-use and should also lend
themselves to rapid routine use. Several small-scale tests for measuring glutenin content
quantitatively and semi-quantitatively have been developed as predictors of wheat quality
in breeding programs (Weegels et at. 1996). Zeleny and sodium dodecyl sulphate (SDS)
sedimentation tests are fast and simple and are still widely used to estimate glutenin to predict baking quality (Weegels et ctl. 1996). Based on a mechanism similar to
sedimentation tests, the gel protein content is strongly related to the SDS sedimentation volume (SDSV) and loaf volume (Moonen et al. I9B2). Usually, the small-scale tests correlate well with dough properties and breadmaking quality (Weegels et at. 1996), but they do not always differentiate effectively between wheats of different quality, especially between some extra strong or between some weak varieties (Pritchard 1993, 58
Khatkar et al. 7996, W angand Kovac s 2002a).
Previously we reported that the SIG test, determined with different swelling time, had
strong correlations with sedimentation volumes, gel protein content, and insol¡ble
glutenin content (Wang and Kovacs 2002a). Since SIG test is developed based on
glutenin content and properties, it is possibly used to evaluate dough strength and
breadmaking quality in addition of noodle making quality. The present work f¡rther
compares the new test with other small-scale tests for prediction of dough properties and
bread making quality.
MATERIALS AND METHODS
Wheat Samples
A set of 20 cultivars and breeding lines with a wide range of quality was used in this
study as described in Chapter 3. Table 4-i gives means, standard deviations, coefficients
of variation, and ranges of quality tests for the samples.
Small-scale Tests
Flour protein (1/ x 5.7) was determined by the AACC Approved Method 46-13 (N x
5.7XAACC 2000). The SDS sedimentation test was carried out according to Kovacs
(1985). The Zeleny sedimentation test was done as specified by the AACC Approved 59
Table 4-1 Means, Standard Deviations (SD), Coefficients of Variation (CV), and Ranges for Flour Quality of 20 Wheat Samples
Attribute Mean SD CV Range Protein,% 1 i.63 1.14 9.81 10.17_14.27 Small-scale Tests Swelling lndex of Glutenin (SlG) 5 0.87 17.46 3.00-6.81 SDS Sedimentation (SDSV), mL 69 11.1 16.12 43-86 Zeleny Volume, mL 47 5.5'1 11.85 33.8-53.6 Gel Protein 2.91 0.44 14.91 '1.93-3.70 Percentage of lnsoluble Glutenin in Protein (lcp), % 25.5 3.1 12.16 17.31-31.52 Percentage of lnsoluble Glutenin in Flour (lGF), % 2.98 0.54 18.26 1.76-4.00 Farinograph ArrivalTime (ART), min 2.72 0.612 22.46 1.20-3.67 Dough Developing Time (DDT), min 5.94 2.63 44.28 2.07-15.60 Stability, min 12.75 4.36 34.17 3.20-17.70 Mixing Tolerance lndex (MTt), BU 44 28.14 64.02 0-142 Mixograph Mixing Time (MT), min 2.31 0.96 41.66 0.80-5.6 Total Energy (TËc) 45.71 7.7 16.85 31.30-68.90 Extensograph Maximum Resistance (Rmax), BU 327 122.73 37.53 100-610 Extensibility (Ext), cm 1 9.05 2j3 11 .19 14-22 Alveograph P lndex (P), mm 67.55 15.33 22.69 3.38-'103.40 G lndex (c), mL 26j9 2.55 9.73 21.6-30.2 W lndex (W), 10-4 J 254 77.51 30.51 83.56-428.6 Gluten Quality Cooked Gluten Viscoelasticity (CGVS), % 35.79 8.53 23.85 16-53.9 Gluten lndex (Gl), % 76.75 12.91 16.82 50-99 Baking Loaf Volume
Method 56-60 (AACC 2000). The insoluble glutenin content, the gel protein content and the swelling index of glutenin in SDS-lactic acid were determined according to Wang and
Kovacs (2002a). Flour (30-40 mg) samples were hydrated with 0.6 mL deionized water in a 1.5 mL centrifuge tube for 20 min, and then were swollen with different time after adding 0.6 mL 3.0 % SDS containing lactic acid (identical to the solvent used in SDS 60
sedimentation test in AACC Approved Method 56-70 (AACC 2000)). The tubes and the
residues were centrifuged at 300 xg for 5 min. The residue was weighed after removing
the supernatant. The SIG value is calculated as the weight of the residue divided by the
original sample weight. The standard SIG test has a 20 min swelling time and is
represented as SIG in the text. The SIG with short swelling time indicates the swelling
time less than or equal to 5 min, and the SIG with long swelling time represents a
swelling time greater than 20 min.
Dough Quality Tests
Dough rheological properties were measured using mixograph, farinograph, and
alveograph. Mixing time (MDT) and total energy (TEG) in mixogram were obtained
according to Pon et al. (7989), using a computerized. I0 g mixograph. Farinograph dough
arnval time (ART), dough development time (DDT), stability (STA), and mixing
tolerance index (MTI) were determined according to AACC Approved Method 54-21
(AACC 2000). Extensograph maximum resistance (R.o*) and extensibility (EXT) were
obtained by AACC Approved Method 54-10 (AACC 2000). Alveograph p, G, and w indices were obtained using AACC Approved Method 54-30A(AACC 2000).
Gluten Quality Test
Wet gluten content and gluten index (GI) were determined by the AACC Approved
Method 38-12 (AACC 2000). Cooked gluten viscoelasticity (CGVS) was obtained using 61
the procedure of Kovacs et al. (L994).
Breadmaking Quality Test
The breadmaking procedure was the optimized straight-dough with long fermentation
(180 min) baking rest of rhe AACC Approved Method 10-108 (AACC 2000).
Statistical Analysis
All small-scale test measurements are means of two repetitions. Correlations between
small-scale tests and quality parameters of dough and end-products were calculated by
using the data analysis tools of Microsoft Excel97.
RESULTS AND DISCUSSION
Associations Between Small-scale Tests and Dough Strength
The correlation coefficients of small-scale tests with dough strength parameters are given in Table 4-2. Dough strength parameters, such as MDT, DDT, srA, MTI, R.u*, and w, were significantly related to Zeleny and SDS sedimentation volume s (Ze\eny and SDSV, respectively) and weight of gel protein, were consistent with previous reports (Axford
1979,Preston et al. 7982, Campbell et al. 1987). As expected, SIG exhibited correlations to these dough strength parameters that were equal to or slightly stronger than for SDSV 62
and gel protein content because the SiG test with the standard procedure is strongly
related to insoluble glutenin content (Wang and Kovacs2002a).It is widely accepted that
dough strength is mainly determined by the amount of unextractable protein (mainly
insoluble glutenin) that is largely independent of different solvents (propanol, acetic acid
or SDS) and extraction procedures (Orth and Bushuk 1972, Dachkevitch and Autran
1989, Bean et al. 1998).
Generally, the insoluble glutenin content is expressed by two compositional variables,
namely the relative glutenin content (the percentage of insoluble glutenin in the total
protein (IGP)), and the absolute glutenin content (the percentage of insoluble glutenin in
the flour (iGF)). From the correlation coefficients between the dough strength parameters
and these two variables (Table 4-2), it is evident that the IGP correlated equal to or better
than other tests to all dough quality parameters except ART and TEG. This doesn't agree
with the results from Gupta et at. (1992), who reported that the relative polymeric protein
content correlated best with MDT and the absolute polyneric protein content best with
DDT. This different result may be due to the different techniques used in the determination of polymeric protein or different sets of samples.
Alveograph P value is also used as an index of dough elasticity. This study showed that alveograph P was significantly related (P < 0.05) to gel protein and SIG, but not (p >
0'05) to the sedimentation tests (Table 4-2). The dough sheet length (DSL) was inversely related to SiG and IGF (Table 4-2). Strong dough contracts significantly after sheeting.
Therefore, the DSL could be useful for the evaluation of dough elasticity. 63
Correlation coefficients of SIG values, determined with different swelling times, with 'w TEG, MDT, MTI, DDT, STA, Rro*, P, value, and DSL increased as swelling time
increased (Table 4-3). This suggests that dough strength is governed primarily by
insoluble glutenin content because, as swelling time increased, the soluble glutenin
dissolves and only insoluble glutenin contributes to the swelling capacity of glutenin
(Wang and Kovacs 2002a). This supports the assumption of Southan and MacRithie
(1999) who suggested that not all of the glutenin, only a fraction above a cetrain
molecular size, contributes to dough strength (the critical molecular size for effective
entanglements) (Southan and MacRitchie 1999). It is believed that insoluble glutenin
contains distinctly higher proportion of large-sized poll,rners than that in the soluble
glutenin fraction (Gupta et al. 1993, Bean and Lookhart 2001). The present results
showed that the amount of larger molecular weight glutenin (the remaining swelling state
aftet a long swelling time to remove low molecular weight glutenin) determines dough
strength and tolerance to dough mixing.
The farinograph arrival time (ART) is significantly correlated with the small-scale tests,
while gel protein content and IGP had the highest and lowest coefficients, respectively
(Table 4-2). Conelation coefficients between ART and SIG were smaller when SIG was determined at longer swelling times (Table 4-3). The highest coefficient occurred at 2 min swelling time, which was similar to the correlation coefficient of gel protein with
ART (Table 4-2). This indicated that ART is contributed by glutenin content and quality. o+
Table 4-2 Corcelation Coefficients 'Wheat Between Small-scale Tests and Dough Quality Parameters for ZO Flo'r Sarnplesl,2
Mixograph Farinograph Extensograph Alveograph MDT TEG ART DSL DDT STA MTI Ru,"* EXT G w Protein 0.23 0.56'er, i* 0.6f 0.38 0.23 -0.56x* 0.43 0.41 0.30 0.36 0.58,r.* _0.64** SIG 0.62** 0.75*:n,i _0.g3,F** 0,66** 0.73i:,F* 0.734
Mixograph ST Farinograph Extensograph llveograph (min)3 MDT TEG DSL ART DDT STA MTI RU'" EXT P G W rì 0.41* 0.66,r.'F 0.67*x 0.61'¡* 0.6g*:*,r _0.6g:k+r. 0.72.*** 0.54* 034 0.6g{:i.* 0.7g*;i<* _0.6g**x 2 0.50* 0.70'r¿*:i 0.73>k:F* _0.g0r.>¡,* 0.63+1, 0.70**r 0.73,kr:+ 0.42 0.47r, 0.5g,r,* O.gg,n:rc+: _0.g0,Ér:1, 5 0.50* 0.71;tt:* 0.69+;ft* 0.63r,* 0.J4-.t:** _0.g2>k>r,* 0.76*++ 0.34 0.50* 0.54* 0.gg:x>Fr: _0.g2f** 1 0.58'k'k 0.75)k*.,k 0.6g*:e{< 0.6g**:r. 0.74*>k,É _0.g3;i** 0.7g;tc** 0.34 0.53* 0.4gx 0.g2*** _0.g5*ì.* 10 0.59** 0.73:rr*r. 0.6g;r** 0.6g,f>¡:r. _0.g5+r:x 0.76:F** 0.7g{.** o.2g 0.50* 0.52,k 0.g0**+ _0.g6r::r.* 15 0.62*+ 0.75*,r* 0.65** 0.J2*.>r* _0.g3r:{r* 0.76*ì<* 0.g1*** 0.2g 0.5g** 0.43 0.g3+*,r _0.g6r<:r.:k 20 0.62** 0.75*>*:r. 0.66r.* _0.g3r:{:x 0.73**,F 0.73:r:ß>F 0.g7x*:ri 0.22 0.57** 0.43 0.g3:r*+ _0.g7r.:** 30 0.70*>t'F 0.75*:r.:t 0.60'r* o.JJ+'F* _0.g3;r.+rr 0.77'rt,k 0.g6+*>i 0.22 0.56** 0.4 0.g4++,k _0 g7,r.r.:ß 40 0.73":k'k 0.J4>t'** 0.58'¡'* 0.gO'r'i<:r' 0.77,k,F* -0.g2**{< 0.g7{:r<* 0.1g 0.5g** 0.33 0.g4*+r _0.g6,$** 50 0.76*** 0.77>k** 0.54* 0.g2*r<* _0.g2*** 0.7g**"* 0.g0>r.r.* 0. ig 0.57** 0.37 0.g4*;ri, _0.g6r,*.* 60 0.76*** 0.J4,k** 0.53* 0.g2*** _0.g0{<** 0.76:k:r.* 0.g0r.:}<* 0.1g 0.51** 0.35 0.g4r(*r: _0.g4,¡.,,+ 70 0'78)t:{
Alveograph G index and extensograph extensibility (EXT) are used to evaluate the
extensibility of dough. EXT and G had no significant correlation with SIG and insoluble
glutenin content (Table 4-2). Interestingly, G index was significantly related to
sedimentation and gel protein tests, wher eas Zeleny had the highest correlation coefñcient
with G index. In addition, EXT was significantly correlated to SDS sedimentation volume
and gel protein, but not to Zeleny.
As swelling time increased, the correlation coefficients of SIG with EXT and G decreased
(Table 4-3). The highest values of coefficients occurred at 0 min (0 min means a brief
vortex after addition of SDS-lactic acid solution). After 0 min swelling time, the
coefficients dropped dramatically and there was no significant correlation for EXT at 2
min and for G at i 5 min swelling time. Based on the effects of soluble glutenin on SIG
values (Wang and Kovacs2002a), it is evident that glutenin qualityplays arole in dough
extensibility. Other studies have shown that most of the variation in dough extensibility
could be explained by the differences in flour polymeric protein content (including soluble
and insoluble glutenin) assessed by SE-HPLC for SDS extracts with sonication (Gupta et al. 1992, Bangur et al. 1997). More recently, using two pairs of wheat lines grown in different locations, the differences of extensibility of dough were explained with total polymeric protein content and unextractable poll'rneric protein content (Larroque et al.
1999). They found that at a similar level of insoluble glutenin content, the higher extensibility of dough corresponded to higher polymeric protein content, and also to lower insoluble glutenin content at a similar level of polymeric protein content. It must be 67
remembered, however, that the high correlation of extensibility with SIG at short swelling
time is based on soluble and insoluble glutenin content and swelling properties (Wang and
Kovacs 2002a). At short swelling time, the quantitative contributions to SIG value by soluble glutenin, insoluble glutenin, and glutenin quality are unknown. Therefore, it is
difficult to correlate them with conclusions from other studies which were based on
polymeric protein content. SIG values with short swelling time presumably reflect a kind
balance of between soluble and insoluble glutenin content and quality which may be a
reason for its signif,rcant correlation with extensibility of dough.
Relationships Between swelling curves and Dough Mixing Graphs
A curve of SIG values vs. swelling time was divided into three distinct stages in which
swelling properties of glutenin depended on varieties (Wang and Kovacs 2002a).
Mixograms or farinogratns also have three stages when flour is mixed with water to form
(dough dough development, peak and breakdown). To explore the relationship between the two kinds of graphs (swelling curve and dough mixing graphs), several parameters were arbitrarily obtained from swelling curves such as swelling time to peak SIG value (STp), swollen time (ST) calculated by the range of STP x I0o/o SIG peak value around peak time, and area under the curve (Fig. a-1). STP is strongly correlated to DDT in farinograph and to MDT : : in mixograph (r 0.92, r 0.86, respectively, p < 0.001 ), while it has no signif,rcant correlation with farinograph ART. A significant correlation between ST and farinograph stability is also observed (r:0.62, P < 0.01). The area under the swelling curve is significantly related to mixograph TEG (r: 0.75,P < 0.00i). Therefore, glutenin 68
swelling curves, similar to mixographs and farinographs, could be used to differentiate
some strong varieties or weak varieties which could not be identified with sedimentation,
gel protein, insoluble glutenin and single SIG test (Pritchard 1993,Khatkar et al. 1996,
7.0
6.0
5.0
4.0 C' U) 3.0
2.0
1.0
0.0 20 30 40 50 Swelling time, min
Fig. 4-1. Representative swelling curve showing measured indices of swelling time to peak (STP), swollen time (ST) and area under the curve.
Wang and Kovacs 2002a). Although our results revealed a strong relationship between glutenin swelling culves and dough mixing graphs, the two processes are based on different mechanisms. During the process of dough mixing, gluten protein (gliadin and glutenin) hydrates and foms a gluten "network" (MacRitchie 1980). In contrast, the swelling process is contributed only by glutenin (Echert et ctl. 1993), and it is assumed that glutenin molecules tend to dissociate to swell and partially dissolve in SDS solvent. The nature of the relationship between these two processes is currently unknown.
Correlation of Small-scale Tests with Gluten properties 69
The statistical relationships between small-scale tests and gluten quality parameters are
shown in Table 4-4.Wet gluten content mainly depends on protein content. Gluten index
(GI) is significantly correlated to the small-scale tests but not to protein content. Gi had the
strongest correlation with relative insoluble glutenin content (IGp). Cooked gluten
viscoelasticity (CGVS), developed and applied for evaluating gluten quality (Kovacs ar
Table 4-4 Corcelation Coefficients of Small-scale Tests with Gluten Quality parameters for 20 Wheat Flour Samplesr,2
CGVS Protein 0.93+:k:* -0.01 0.44*
SIG 0.64** 0.53 * 0.79*{.:r
SDSV 0.26 0.64** 0.78*:k'F
Zeleny 0.26 0.54* 0.6'7**
Gel Protein 0.65** 0.44x 0.82+{**
iGP 0.09 0.75*:>Fr 0.70*** IGF 0.51* 0.49+ 0.70*** SIG:swellingindexofgluteninwith20minswellinn : volume; IGP the percentage of insoluble glutenin in total flour protein; IGF : the percentage of insoluble glutenin in flour; WG : wet gluten content; GI : gluten index; CGVS : cooked gluten viscoelasticity. 'Thesignificanceisindicatedby*forthe 5%olevel,**forthelo/olevel,and**'r for0.Io/o level.
al- 1994, 1997a, b), was significantly related to small-scale tests. The highest correlation coefficient was obtained from the relation between CGVS and gel protein. As swelling time increased, the correlation coefficients of wet gluten content with SIG slightly decreased (Table 4-5). Insoluble glutenin content contributed to GI, because the correlation coefficients befween SIG and GI increased as swelling time increased. The correlation coefficients of SIG with CGVS decreased with the increase of swelling time, indicating that viscoelasticity of gluten is also affected by glutenin content and quality. 70
Table 4-5 Correlation Coefficients of Gluten Quality Parameters with SIG from Different Swelling Time for 20 Wheat Flour Samplesr,2
* 0 0.53 0.52* 0.81**>r 2 0.64'4* 0.46* 0.80***
5 0.63** 0.50* 0.79;r.**
7 0.64** 0.51* 0.90+:k*
10 0.61** 0.52* 0.75*r<+ 15 0.61** 0.55+* 0.J9***
20 0.64*x 0.53* 0.79*>ß* *+ 30 0.55 0.62'+ 0.76*+* 40 0.52* 0.64** 0.76***
50 0.46* 0.69*:k* 0.77.4-.k* 60 0.47* 0.69*'** 0.7J**+ 70 0.44* 0.69rF:e* 0.76*+* ' ST: swelling time; WG : wet gluten content viscoelasticity. 2 * The significance is indicated by for the 5%o level, ** for the I%olevel;and *'F* for 0.lo/o level. 3 Vortexing for 5 sec at beginning and ending of swelling period, and vortexing for 5 sec at every interval of 10 min.
The correlation of small-scale Tests with Breadmaking euality
SIG, sedimentation, and gel protein tests showed strong correlation with loaf volume (LV)
(Table 4-6). Zeleny sedimentation volume, however, had a higher cor¡elation coefficient than SDSV, and this is not consistent with the results of Axford et at. (1979). They fotmd the SDS sedimentation test superior fo Zeleny sedimentation test in predicting LV of bread produced both by mechanical development and long fermentation procedures. Similar to
Zeleny sedimentation test, gel protein was strongly correlated with loaf volume. Strong positive relationships befween insoluble glutenin content and loaf volume have been found 71 many by others (Orth and Bushuk 1972, Gupta et at. 1993, Preston et al. 1992,Bean et cil.
1998)' However, we found in this study that insoluble glutenin contents, expressed as IGF
or IGP, had no significant correlation with LV. This different result may be due to the
different set of samples and/or different baking procedures. The set of flour samples used
Table 4-6 Corcelation Coefficients of Small-scale Tests with Loaf Volume for 20 Wheat Flour Samplesl,2
Protein SIG SDSV Zeleny Gel protein lcp IGF LV 0.53* 0.54*" 0 !4ï 0.68*** 0.6g*** 0.3.1 0.43 'stc : u6on volume; IGP the percentage of insoluble glutenin in total flour protein; IGF : the percentage of insoluble glutenin in flour; LV: loaf volume. -'Ihe2-, * significance is indicatedby forthe 5o/olevel, ** for thelo/o level, and **+ for0.Io/o level.
in this shrdy possessed a wide range of dough strength (Table 4-1). Some varieties like
Glenlea (very high insoluble glutenin content but very low LV) are probably too strong
under the present baking test to get its potential LV (Tippl es 1979). The correlation
coefficient of LV with IGF and SIG increased to r:0.6g, p < 0.01 , and, r:0.70, p <
0'001, respectively, when Glenlea was omitted (i.e., 19 cultivars considered). Because the
correlation coefficients of LV with gel protein and, Zeleny sedimentation tests were higher than that with insoluble glutenin content or SIG, baking quality (LV) conesponds to glutenin content and its quality.
Cor¡elation coefficients of loaf volume with SIG declined as swelling time increased
(Table 4-7). Similarly, Khan et at. (1989) showed that the correlation coefficient of loaf volume with residue protein after 70o/o ethanol extraction (r : 0.49, p < 0.01) was higher 72
tlran that with residue protein after 1.5% SDS extraction (r : 0.32,p < 0.05). This is most
likely due to SDS solvent extracting more soluble glutenin than aqueous ethanol. These
results strongly indicate that baking quality is mainly determined by glutenin contributed
by both soluble and insoluble glutenins.
Table 4-7 Corcelation Coefficients of Loaf Volume with SIG with Different Swelling Time for 20 Wheat Flour Samplesr'2
min)3 0 5 7 '10 15 20 30 40 50 60 LV, 0.63** 0.69**" 0.65** 0.60** 0.62** 0.55* 0.54* 0.4g" 0.42 0.41 0.3g 0.36 ' ST: Swelling time; LV: loaf volumì. 'The2-, * ** significance is indicated by for the 5o/o level, for lhe Io/o level, and *** for 0.1olo level. 3 Vortexing for 5 sec at beginning and ending of swelling period, and vortexing for 5 sec at every interval of 10 min.
The Classification of Quality Parameters
Quality parameters in this study can be divided into two groups according to their
relationships with insoluble glutenin content or glutenin quality. The first $oup included parameters 'W, such as SiG, MDT, TEG, DDT, STAB, MTI, R''u*, p, and GI. The
parameters were directly related to insoluble glutenin content, and correlation coeff,rcients
of the parameters with SIG increased as swelling time increased. The second group
included parameters such as SDSV, Zeleny, gel protein content, SIG with short swelling
time, EXT, G, ART, LV, and viscoelasticity of cooked gluten. Unlike the first gr-oup, the
parameters in the second group cannot be explained simply by protein composition. The
parameters were based on glutenin content and quality, including both soluble and
insoluble glutenin. Because this classification is based on the relationships between quality 73 parameters and soluble and insoluble glutenin content and quality, the functionality of
soluble and insoluble glutenin is important for the explanation of both groups of quality
parameters. Soluble and insoluble glutenins are presumed to consist of low molecular
weight and high molecular weight glutenin, respectively (Gupta et al. lgg3, Bean and
Lookhart 2001). It is believed that small- and large-size glutenins have different
functionality in dough. The low molecular weight glutenin fraction showed viscous-like
behavior, and the high molecular weight glutenin fraction showed gel-like behavior
(Tsiami et al. l996a,b). Qualityparameters in the first group, which are based on large-
size glutenin fractions, may reflect the gel-like properties of large-size glutenin. These
parameters reflect the strength of the gluten network. On the other hand, the second group
based on both small- and large-size glutenin reflects both the viscosity and elasticity of the
gluten network. Because each group of parameters arises from similar physical processes,
the correlations of the parameters in the same group were generally significant as we found
in the current study. Conversely, correlation coefficients among parameters from different
goups were usually not significant, because these two goups of parameters arise from different physical processes.
CONCLUSIONS
Quality evaluation of wheat lines at early stages of their breeding is constrained by the small size of the samples and the large number of lines to be tested. The SIG test satisfies neatly all the requirements for the breeding program. It is rapid, simple, and needs only a smali sample size in the standard SIG test procedure. In the standard procedure, SIG values predict quality parameters based on the insoluble glutenin content, such as strength of the 74
gluten. Similar to sedimentation volumes and gel protein contents, SIG values obtained with short swelling time are correlated with quality parameters determined by glutenin content and quality, such as dough extensibility. Furthermore, similar to dough mixing graphs in mixograph and farinograph, the glutenin swelling curves have the potential to differentiate extra strong varieties or weak varieties that failed to be identified with sedimentation, gel protein, and insoluble glutenin content tests. 75
CHAPTER 5
Effects of Nitrogen Fertilization on Quantities and Proportions of Different Protein Types in wheat Flour
ABSTRACT
Five spring and five winter wheat cultivars were grown at six levels of nitrogen supply
for each of two years. Changes in protein fractions with protein content were monitored
by an extraction procedure and by SE-HPLC. Changes in quantity of glutenin subunits,
swelling index of glutenin with different swelling time, and dough mixing characteristics
were also assessed. In general, as protein content increased, the absolute content of all
protein fractions (percent of protein fractions in flour) increased. However, in terms of
the relative content of protein fractions (percent of each protein fraction in total protein),
spring wheat cultivars showed that the proportion of monomeric protein increased with a
decrease of insoluble glutenin, whereas winter wheat cultivars showed an increase in
soluble glutenin content which generally coincided with decreased insoluble glutenin.
Both variety and nitrogen had significant effects on the quantities of protein fractions. In
addition, the ratio of high molecular weight (HMW) to low molecular weight (LMW)
glutenin subunits increased with protein content in spring and winter wheat flours. Dough
mixing characteristics correlated better with the relative content of insoluble glutenin
than with the relative contents of soluble glutenin and monomeric protein in spring wheats' The associations among relative contents of insoluble glutenin and monomeric protein with dough mixing characteristics were different for spring and winter wheat cultivars. 76
INTRODUCTION
Cereal chemists have long studied the effect of environment on the protein composition
and the end-use quality of wheat cultivars. Understanding genetic and environmental
influences responsible for variation in end-product quality is important to millers and
bakers who want to produce a consistent product. Nitrogen fertilization is considered to
be the main environmental factor influencing storage proteins as well as the end-use
quality of wheat samples (Gooding and Davies 1997). An increase in the level of nitrogen
ferlilization can improve wheat breadmaking quality by increasing its protein content.
However, a very high rate of nitrogen fertilization was associated with a marked
weakening of dough properties and a deterioration of breadmaking quality (Tipples et at.
1 977; Bushuk et al. 1978).
Several reports suggested that nitrogen fert:irization could quantitatively affect storage
protein components (Tanaka and BushuklgT2; Bushuk et al. r97g; Gupta et al. 1992;
Scheromm et al. 1992; Iia et al. 1996a, b; Pechanek et al. 1gg7), however, reports have
been contradictory. Using a sequential extraction procedure, Tanaka and Bushuk (1972)
found that all protein fractions varied in proportion to the total protein content of the
flour, with the composition of the protein showing no net change. Later, Btshuk et al.
(1978) found that the proportions of albumin and soluble glutenin increased with protein and corresponded with a decrease of insoluble glutenin. However, Doekes and Wennekes
(1982) reported that the increase in protein content increased only gliadin content, and glutenin, albumin, and globulin contents did not change. 77
Size-exclusion high performance liquid chromatograph (SE-HPLC) has been widely used
in the separation of total wheat protein, by extractio¡ with SDS buffer under sonication.
Gupta et al. (1992) found that the proportion of glutenin remained constant, the
proportion of gliadin increased, and the proportion of albumin-globulin decreased as the
flour protein content increased with nitrogen fertilization. A similar result was obtained
by another lab (Jia et ctl. 7996a). Scheromm et crl. (1992) found that the proportion of
glutenin responded differently to nitrogen fertilization for different varieties. peltonen
and Virtanen (1994), using densitometric measurements of proteins separated by SDS-
PAGE, observed that an increase of gliadins was mainly due to an increase of low
molecular weight gliadin species. The effects on gliadins were larger than on glutenin,
and major protein types (c-gliadins, y-gliadins, and LMW-subunits of glutenin) were
more affected than minor types (o-gliadins, and HMW subunits) (V/ieser and Seilmeier
1998). Recently, Pechanek et al. (1997) found that the effect of increased protein content
on the gliadin-to-glutenin ratio was inconsistent for two varieties. While increased protein
content increased the ratio in one variety (Capo), the opposite was true for another variety
(Renan). They also found that the ratio of LMw-to-HMw glutenin subunits decreased as
total protein content increased by nitrogen fertilization.
The proportion of protein fractions has proved important in explaining the variation in flour quality (Orth and Bushuk 1972; Huebner and Wall 1976;Daclkevitch and Autran
1989)' Relationships between protein fractions and flour quality, however, varied from significantly negative to significantly positive, probably due to the differences in protein fiactionation procedures or use of samples from different sources. 78
In the present study, both extraction and SE-HPLC procedures were appli ed, to analyze
the protein composition of spring and winter wheat cultivars, each grown at six nitrogen
fertilization levels during each of two years. This allowed us to see whether a change in
protein content induced by nitrogen ferlllization caused a change in specific protein
fractions, and whether a change could be related to changes in dough mixing
characteristics.
MATERIALS AND METHODS
Samples
Flours were Buhler-milled (65%o extraction rate) from five spring and five winter wheat
varieties glown at six nitrogen ferlilizer levels coresponding to additions of 0, 40, 80,
I20, 160, and 240 kg of nitrogen per hectare applied as ammonium nitrate (34-0-0) at
Saskatoon, SK, Canada for two years. The cultivars were chosen to represent quality
types and protein concentrations ranging from low protein soft white wheat (SWV/) and
Canada Western Soft White Spring (CWSWS) through to Canada Prairie Spring (CpS),
and Canada Westetn red winter (CWRW) to high protein Canada hard red spring
(CWRS) ancl Canada Westem Extra Strong (CWES) wheat. The spring wheat varieties were AC Reed (cwsws), Katepwa (cwRS), AC Taber (cps), Roblin (cwRS), Bw90
(CWRS and only for 1994), and Glenlea (CV/ES and only for 1995), and the winter wheat varieties were 586-375 (Sww), CDC Kesrrel (CwRw), 5g6-101 (CWRW),
Norstar (CWRW), and Winalta (CWRV/). The grain yield, protein content, and protein content effects on wheat quality have been reported previously (Fowler et al. 1998;
Fowler 1998). One hundred twenty flour samples were obtained and stored in sealed plastic bags < -5oC. 79
Quality Measurements
Mixograph data were obtained according to Pon et at. (1989) using a 10-g mixograph at
62%o water absorption as described previously (Fowler et al. 1998). A 10-g farinograph
was used and the data were determined by AACC methods (2000). Flour protein content
was measured by a near-infrared (NIR) reflectance method (Fowler et ct\.,1998). SDS
sedimentation volume was determined by using the procedure by Kovacs (1985). The
swelling index of glutenin (SIG) was determined with swelling times of 0, 5, and 20 min,
expressed as SIGO, SIG5, and SIG20, respectively, and their relative values (percentage
of SIG value in flour protein) were termed SIGOP, SIGSP, and SIG2OP, respectively
(Wang and Kovac s 2002a).
Analysis of Protein Composition
Protein composition was obtained using two procedures. In the f,rrst, sequential extraction
was used (Fu et al 1998a) and the protein content of each fraction was determined by
turbidity measurement (Wang and Kovacs 2002a). Three protein fractions were obtained
in this procedure, including monomeric protein (including gliadin, albumin, and globulin
according to Fu and Kovacs (1999)), soluble glutenin, and insoluble glutenin.
The second technique used was SE-HPLC, where the procedure of Bean et at. (1998) was used for soluble protein. For insoluble glutenin, the residue was extracted with 50% propanol containing 0.4% dithiothreitol (DTT) at 60'C for 30 min. Reduced glutenin was separated by SE-HPLC using the same condition (Beans et al 1998) after centrifugation
(15,000 x 9, 5 min) and filtration with a 0.4 prm filter. For soluble proteins, three 80
fractions, (the polymer-first peak, gliadin-second peak, and albumin and globulin-third
peak), were obtained with different retention times. For reduced glutenin, HMW and
LMW glutenin subunits were obtained. Protein fractions from SE-HPLC were quantified
by the method of Gupta et al. (1992), and were presented both in terms of absolute
content and relative content. The absolute amount of each protein fraction was the
percentage of protein fraction in flour, while the relative content of the protein fraction
was the percentage of protein fraction in total protein.
Glutenin Subunit Analysis by RP-HPLC
Quantitative values for glutenin subunits of insoluble glutenin were determined by
reversed-phase HPLC (RP-HPLC) according to the procedure of Fu and Sapirstein
(ree6).
Statistical Analysis
Determination of SIG values and protein fractions from both sequential extraction and
from SE-HPLC were the average of two determinations. Data were statistically analyzed using the SAS software (vers. 8.2, SAS Institute, Cary, NC) for computing analysis of variance factorial effects in a randomized complete block design (nine varieties were used since Glenlea and BW90 were grown for one year), and Fishers' least significant difference (LSD) of multiple comparisons, and Pearson's correlation coefficients were calculated. 81
RESULTS AND DISCUSSION
variation of Protein composition with Nitrogen Fertilization
The effects of nitrogen fertllizafion on protein fractions within varieties, determined by
the sequential extraction procedure for spring and winter wheat, are shown in Appendix I
Tables 1-4, while the effects on protein fractions determined by SE-HPLC are listed in
Appendix I Tables 5-8. Protein contents for the flours are presented in Appendix I Tables
10-13. The different yields of each protein fraction for flours varying in nitrogen
fertilization levels were compared with Fishers' least significant difference procedure
(LSD). The absolute amounts of monomeric protein (MPF), soluble glutenin (SGF), and
insoluble glutenin (IGF) obtained from both measurements (sequential extraction and SE-
HPLC) were the same order of protein content rather than of nitrogen fertilization levels,
especially at low levels of nitrogen fertilization (0, 40, and 80 kglha, N). Nitrogen
fertilization stimulated large increases in grain yield that produced a lag phase in the
protein content-nitrogen fertilization response curve, especially, under low level of
available soil nitrogen for varieties with high grain yield potentials (Fowler 2003
accepted). Similar to the order of protein content previously reporled by Fowler (2003),
the absolute amount of protein fractions with different nitrogen fertilization was in the
orderof 240> 160 > I20> 80 > 0 > 40kg/haforspringwheat cultivars and,240> 160 >
120 > 0 > 80 > 40 kglha. N for winter wheat cultivars. Protein content and absolute amounts of protein fractions increased progressively with an increase in nitrogen feÍilization afier initial low level of nitrogen rertjlization Ø0 kg/ha). 82
For the relative contents of protein fractions in spring wheat, the monomeric protein
(MPP) showed a signif,rcant increasing trend with increased protein content for most
varieties (Appendix I Tables 1 and 2). Although a slight increase of soluble glutenin
(SGP) was obserued for some 1994 spring wheat, the increase was only significant for
Katepwa and AC Reed. A significant decrease in insoluble glutenin (IGp) was observed
for most varieties, except for 1995 AC Taber and AC Reed. This was in agreement with
some previous results (Bushuk et al. 1978; Jia et al. 1996), but contrasted with the results
from Tanaka and Bushuk (1972) who found that quantitative distribution of the flour
proteins mong five solubility fractions was not affected by protein content. For winter
wheat, the changes of protein fractions with protein content were different from that of
spring wheat (Appendix I Tables 3 and 4). An increase of MPP was only observed for
some winter wheat varieties, such as i995 Norstar, 586-101, and Winalta. A decrease in
IGP of winter wheat cultivars was observed in concert with an increase in SGp. A similar
increase of SGP with protein content was also observed by Tanaka and Bushuk (1972)
for acetic acid extractable glutenin of flours from two varieties grown with different nitrogen application rates. Obviously, under different levels of nitrogen fertilization, the synthesis of different protein components was affected differently.
When the monomeric proteins were divided into two parts using SE-HPLC, proteins with smaller molecular weight (albumin and globulin) and gliadin, the relative amount of gliadin (GLIP) slightly increased, although the differences in response to each nitrogen level were within experimental error (Appendix I Tables 5-8). The results obtained for the effect of changes in protein on gliadin were in agreement with those of Tanaka and
Bushuk (1972) and Peltonen and Virtanen (1994). The increase in the relative amount of 83
albumin-globulin (AGP) with protein content was significant for most varieties.
However, exceptions occurred, such as with the highest AGP being obtained from
samples with 0, 40, or 80 kglha N for some varieties. Similar to the results from
sequential extraction, the SGP was significantly increased for winter wheat, and a trend
toward a decrease in IGP was observed for both spring and winter wheat cultivars.
The effect of protein content on SIG values is shown in Appendix I Tables 1-4. SIG20,
SIG5, and SIGO values increased as protein content increased, although the differences in
some SIG0 values were within experimental error. A decreasing trend was observed for
SIG2OP, SIG5P, and SIGOP values in response to increased protein content.
Relationships among Protein Fractions Determined by Two procedures
From the above discussions, it is apparent that the two protein fractionation procedures
yrelded some consistent results, although the trends seen in changes of protein fractions
with protein content were slightly different between the two different methods used.
Strongest correlations of each protein fraction (absolute amount) with the two procedures
used are shown in Fig.5-1. The MPF from SE-HPLC measurement was the sum of
gliadin, albumin and globulin. Only the soluble glutenin plot had a wide spread between
the two procedures.
Statistical Analysis Between Protein Composition and Flour Protein Content
It was indicated that eachprotein fraction within varieties varied significantly and 84
R2 = 0.93 A
6:e È_ -l l!I tll c lJ. E3o- b MPF(extraction)
R2 = 0.75 o À ¡Z I gtrJ l! o Ø1 ,_L _ 1.5 SGF(extraction)
R2 = 0.90 c
C)"^Ã JA +3o- lll o u) z- E1 9s 2.5 IGF(extraction)
Fig.5-1. Percent monomeric protein (A), percent soluble glutenin (B), and percent insoluble glutenin in flour (C) determined by SE-HPLC as a functíon of percent corresponding protein fractions determined by sequential extraction (n:120). MpÈ, SGF and IGF represent the percentages of monomeric protein, soluble glutenin ánd insoluble glutenin in flour, respectively. 85
responded differently to increased N levels of fertilization. Therefore, it was hoped that
statistical relationships between protein content and the amount of each protein fraction
for all samples might provide information on the effect of nitrogen fertilization on protein
composition. Figure 6-2 shows the relationship between protein content and the amount
of each protein fraction. As flour protein increases, the absolute amount of each protein
fraction showed very strong linear relationships with flour protein content. A similar
trend was observed by Gupta et al. (1992) and,Iia et al. (r996a,b).
Previous studies showed that GLIP was positively correlated with flour protein, and AGp was negatively correlated with flour protein (Gupta et al., 1992; Jia et al., 1996a).
However, this was not found in the cument study when MPP was plotted with flour protein content (Fig. 5-28). It should be noted that monomeric protein includes albumin-
globulin and gliadin in the sequential extraction procedure. When GLIp and AGp
determined by SE-HPLC were considered, signifìcant correlations of GLIp or AGp with
protein content were not observed.
The SGP and IGP were not significantly related to protein content (Fig. 5-2D and.6-2F), which was in agreement with other reports (Gupta et at. 1992; Jia et al. l996a,b). As discussed above, the relative amount of protein fractions in spring and winter wheat cultivars responded differently to a change in level of nitrogen fertilization. The cor¡elation coefÍicients between plotein fractions and flour protein were calculated for each set of samples. Significant correlations between SGP and flour protein content existed in both spring and winter wheat cultivars (r: -0.34,p < 0.01 and r:0.64, p < 86
B 65 = 0.00 7 o¡ OU gu a^. lr5 ; o- so E¡ =t ÀE J
2 40 51015
Pfotein (%)
19 17 ^15 .i: ù13 aa t Øtrr
7 5
10 51015
Protein (o/") Protein (%)
R2 = 0.75 R2 = 0.00 35
4 30 s o- zc & o l! o 20 -2 t' 15 1 51015 10 Protein (%) Protein (%)
Fig. 5-2. Percent monomeric protein in flour (A), percent monomeric protein in protein (B), percent soluble glutenin in flour (C), perceni soluble glutenin in piotein (D), percent insoluble glutenin in flour (E), and percent insoluble glutJrin in protåin lnjlouiained by sequential extraction) as a function ofpercent protein in flour qì:f àO;. 87
0'01, respectively), while only in spring wheat cultivars, the IGP was significantly related to flour protein content (r: 0.31, P < 0.05). No significant correlation between Mpp and
flour protein was found in any case, which was in agreement with the results from all
samples combined.
Effects of Genotype and Environment on protein Fractions
Analysis of variance was used to determine the respective influences of nitrogen
fertilization and genotype (variety) on protein content, protein composition, and SIG
values. Results from the sequential extraction procedure are presented in Table 5-1, and
and results from SE-HPLC are shown in Table 5-2. The F ratios for the effects of
environment and genotype were all significant except for the effect of nitrogen on AGp.
Table 5-1 Influence of Nitrogen Fertilization Level and Variety Determined by Analysis of Variance (F-test)' on Protein Content, Protein Fractions (Sequential Extraction), ánd SIG Values for Nine Wheat Cultivars Grown at Six Nitrogen Levels in Each of Two Years.
Pro MPF SGF IGF MPP SGP IGP SIG2O SIG2OP Variety 115.8*'r' 59.2** 34.1** Nitrogen 123.3** 84.0** 71.5** 71.0** 4.4** 25.6** 17.0** 140. 1+* 5.g** VxN ns 1.9* ns ns r*, 2.0** 2.96** ** : F-test significance : at 5 and 1%, resp Pro protein content; MPF and MPP : the percentage of monomeric protein in flour and protein, respectively; SGF and SGP : the percentãge of soluble glutenin in flour and protein, respectively; IGF and IGP : the percentage of insoluble glutenin in flour and respectively; : q1o_tein, : SIG20 swelling index of glutenin with 20 min swelling time; SIG2OP the percentage of SIG values in protein; V*N : interaction between variety and nitrogen fertilization.
Genotype x environment (GxE) interactions for protein content and most protein
fractions were not significant, except for monomeric protein determined by sequential 88
extraction and the absolute amount of insoluble glutenin determined by SE-HpLC (Table
s-2).
Table 5-2 lnfluence of Nitrogen Fertilization Level and Variety Determined by Analysis of Variance (F-test) ' on Protein Content and Protein Fractions (SE-HPLC) ior Nine wheat cultivars Grown at Six Nitrogen Levels in Each of Two years.
SGP GLIP AGP IGP MPP SGF GLIF AGF IGF' MPF Variety 764.5'4* 21.5'+* 4.7** 111.2*-+ 91.3** 32.g++ 33.3*+ 25.4,k* 215.g*+ 66.6+* Nitrogen 3.7** 2.5** ns J.2-++ 70.2** 69.0** 64.7+* 33.6++ 60.4*+ 123.6",+ ns VxN ns 1.6*'k ns ** : F-test significance -*, at 5 and t %, t SGP and SGF : the percentage of soluble glutenin : in protein and flour, respectively; GLIP and GLIF the percentage of gliadin in protein und flonr, respectively; AGp and AGF : percentage the 9f albumin and globulin in protein and flour, respectivéþ; IGp and IGF : the percentage of insoluble glutenin in proteìn and flour respectiiely.
Effect of Protein Increase on Glutenin Subunits
The ratio of HMW-to-LMW glutenin subunits was found to be related to breadmaking
quality (Kruger et al. 1988; Gupta et ctl. 1992; Huang and Khan 1gg7). From the survey of Weegels et al. (1996), however, the variation in wheat quality data can hardly be explained by the quantity of one HMV/ glutenin subunit. To investigate the effect of protein content on glutenin subunits, soluble proteins were removed with aqueous propanol. Insoluble glutenin was reduced to glutenin subunits and extracted with 50% propanol' The glutenin subunits of insoluble glutenin were then separated by Rp-HpLC, and the ratio of HMW-Io-LMW glutenin subunits was derived from the areas beneath peaks in the appropriate chromatogram regions. The ratio for one year spring and one year winter wheat are shown in Appendix I Table 9. Results indicated that as protein content increased, the ratio of HMWLM'W subunits increased significantly. This agrees 89
with the reports from other researchers (Pechanek et al. 7997; wieser and seilmeier
1 998).
Table 5-3 Multiple Comparisonsr of HMWLMW Glutenin Subunit Ratio2 Determined by RP-HPLC among Cultivars
Variety Ratio of HMWLMW Glutenin subunits 95 Spring Wheats Glenlea 0.62a Roblin 0.56b AC Taber 0.47c Katepwa 0.46c AC Reed 0.42d 95 Winter Wheats s86-375 0.49a Winalta 0.41b Norstar 0.46b CDC Kestrel 0.39c s86-101 0.39c 'Means i" tn e letter are not statistically different (P<0.05) by t multiple range test. - Average of six nitrogen levels.
The average of HMWLMW ratios among six N levels for individual varieties is
calculated and the multiple comparison among the average ratios for different varieties are presented in Table 5-3. The data indicated that in spring wheat cultivars the order of the ratio was consistently in order of gluten strength. Glenlea, Canadian Western extra 'Western strong variety, had the highest ratio, whereas AC Reed, Canada Soft White
Spring, had the lowest ratio. This indicated that a variety with high strength gluten had a high ratio of HMWLMW glutenin subunits, supporting previous studies (Gupta et al.
1991, 1992; Huang and Khan 1997). However, different observations for winter wheat cultivars occurred, in which a soft white winter wheat cultivar, S86-375, which possessed the weakest gluten of five varieties, had the highest HMWLM'W ratio. Omitting this 90
varieTy, the ratio matched well with dough strength for the other four varieties.
variation of Quality Parameters with Nitrogen Fertilization
Appendix I Tables 10-13 show quality data for spring and winter wheat, including particle size index, protein content, SDS sedimentation volumes, mixograph, and
farinograph parameters. A slight increase of kernel hardness with protein content was observed for both spring and winter wheat cultivars. As with SIG values, sedimentation value increased with protein content. As expected, dough strength as determined by
mixograph and farinograph for each cultivar increased more or less linearly with protein content' Mixograph dough development (MDT), however, were markedly reduced with
increasing protein content.
Table 5-4 lnfluence,of Nitrogen_Fertilization Level and Variety Determined by Analysis of Variance (F-test) ' on Dough Mixing Characteristics for Nine'Wheat Cultivárs Grown at Six Nitrogen Levels in Two years.
Mixograph Farinograph MDT ETP TEG DDT ART MTI STA Variety20.11**28.28**44.23,N+80.21**50.43**ffi FAB 171.46** Nitrogen 9.09** 3.45** 24.93*'+ 5g.72** 33.75-+* 15.19** 34.13** 23.44** VxN 2.77** ns ns 2.65** 4.54+* 2.82** 4.30** NS : F-test significance Ii.T : MDT mixograph dough development time; ETÞ : energy to peak; TEG : total energy (area under curve); DDT : : farinograph dough oevãtopment time; ART = arrival time; MTI mixing tolerance index; sTA J stability;^ pag : farinograph absorption.
Analysis of variance was used to determine the respective influence of environment (nitrogen fertilization) and genotype (variety) on dough mixing characteristics (Table 5- 4). The ratios F for the effects of environment and genotype on main mixograph and farinograph parameters were all significant, and GxE interactions for mixograph dough 9l
development time (MDT) and farinogïaph parameters were signi frcant, except for
farino graph absorpti on.
Relationships between Protein Fractions and Dough Mixing Characteristics
Examination of the conelation matrix among protein composition d.ata determined with
two procedures showed strongly significant associations between the two procedures (Fig. 5-1). Therefore, only correlations with protein fractions determined by the sequential extraction procedure are considered in this section. The relationship between
dough mixing characteristics and the protein fractions are shown in Table 5-5 for all flour
samples together (n : I20) and in Table 5-6 for the spring and winter wheat, respectively. The correlations of the absolute amount of protein fractions were included in the Tables
as well for comparison with the relative protein content. It was observed that quite different relationships could be derived between the dough mixing parameters and the protein fractions quantification as absolute or relative amounts. The most appropriate and commonly used quantification of the fractions was the relative amount (proportion in protein), because the relative amount can avoid the effect of differences in total flour protein and provide information on the balance of protein fractions in flour protein. Flour protein content had a great influence on the relationship between the absolute amount of protein fractions and the dough mixing data. The dough mixing parameters that were highly and significantly correlated with flour protein content, e.g., mixograph energy to peak (ETP), total energy (TEG), farinograph arrival time (ART), dough development time (DDT), stability (srA), mixing tolerance index (MTI), and absorption (FAB), were also significantly correlated with the absolute amounts of protein fractions (Table 5-5)" This indicated that dough strength parameters increased with protein content. Most of the 92
discussion below will be based on the relative amount of protein fractions.
Table 5-5 Simple Correlation Coefficients between Protein Fractions and Dough Properties for Two years Spring and Winter Wheat.
MPF SGF IGF MPP SGP IGP Protein 0.96** 0.JJ*+ 0.96*'F NS NS NS MDT -0.2J** -0.47** NS -0.34** -0.55'k* 0.65** ETP 0.29** *'k NS 0.63 -0.35*x -0.32+* 0.56x* TEG 0.16** 0.53 ** 0.77*+ NS NS NS DDT 0.J4+* 0.64'+* 0.78** NS NS ns ART 0.90** 0.63 ** 0.73+* ns NS ns STA 0.60** 0.54** 0.7 5** -0.30*'k NS 0.23* MTI -0.37'*+ NS -0.79*'4 0.51** 0.41 ** -0.69** FAB 0.7J** 0.49*'" 0.84** -0.1g* NS 0.25** *, ** : significant p at P < 0.05, < 0.01, respectively; ns : not significant (n : 120). Abbreviations as defined in Tables 6-i and 6-4.
The MPP and SGP from total wheat samples (Table 5-5) showed significant negative correlations with MDT and ETP, and positive correlations with MTI. The IGp gave better corelations with dough strength parameters compared to the Mpp and SGp, which was in agreement with many other reports (Orth and Bushuk 1972; Huebner and Wall 1976; Gupta et al- 7992; Beans et al. 1998). The IGP was better than its absolute amount in predicting MDT, but IGF appeared to prevail for farinograph dough development time, which was in agreement with the results from Gupta et al. (1992).
When correlation coefficients weïe calculated separately for spring and winter wheat cultivars (Table 5-6), the MPP was a better parameter for predicting dough mixing properties for spring wheat than for winter wheat. For winter wheat flours, the Mpp was positively correlated with MTI and negatively correlated with TEG and ART, but had lower r-values than those obtained from spring wheat flours. However, a higher correlation coefficient of MPP with farinograph absorption was observed for winter 93
wheat flours.
Table 5-6 Simple Cor¡elation Coeff,icients between Protein Fractions and Dough Properties for Spring and Winter V/heat
MPF SGF IGF' MPP SGP IGP Spring Wheat (n :60) Protein 0.97** 0.69** 0.gg** NS -0.34+* 0.31 * MDT NS -0.49'k* 0.30* -0.57** -0.62** 0.J7*+ ETP NS ** NS 0.62** -0.61 -0.67** 0.g l ** ** TEG 0.65 NS 0.91** -0.33'k'+ -0.59** 0.54** DDT 0.69** 0.50'r'* 0.80** -0.36** -0.31* 0.39** ART 0.g ** l 0.61** 0.76** NS NS NS STA 0.55'k* 0.35** 0.'14** -0.45+* -0.37** 0.49'ß* MTI -0.51** 11s -0.87** 0.63** 0.74** -0.81*+ FAB 0.81*+ 0.33** 0.90** -0.29* -0.63** 0.54** Winter Wheat (n:60) Protein 0.92** 0.87** 0.89** NS 0.64'4* ns MDT nS -0.30* NS NS -0.36*+ 0.34** ETP 0.52** NS 0.66** NS ns 0.30* TEG 0.93x* 0.79** 0.96** -0.26* 0.59x* ns 0.16+* DDT 0.81** 0.74** NS 0.64** NS ART 0.63+* 0.70'k* 0.69** -0.26* 0.54+* NS STA 0.80*'k 0.79+* 0.90*'r NS 0.61 ** NS MTI -0.69** -0.66** -0.81** 0.31x -0.49** NS FAB 0.49*'+ 0.50** 0.84** -0.49** *, ** 0.32* 0.49** : significant at P < 0.05, P < 0.01, respectively; ns : not significant; Abbreviations as defined in Tables 6-1 and 6-4.
The SGP was not correlated with ART for spring wheat, and not with ETp for winter wheat (Table 5-6). lnterestingly, the SGP for spring wheat flours was negatively cor¡elated to dough strength parameters, while the SGP for winter wheat flours was positively correlated with dough strength. The results from spring wheat flours were consistent with the report of Orth and Bushuk (1972) who showed that the proportion of acetic acid soluble proteins (similar to the soluble glutenin in this study) was negatively correlated with DDT and positively correlated with MTI.
The IGP was significantly correlated with dough mixing parameters for spring wheat 94
flours, while it was only significantly correlated with MDT and ETP and showed small r-
values for winter wheat (Table 5-5). For winter wheat cultivars, IGP was a most effective parameter for predicting dough strength compared to MPP and SGp, which was in
agreement with the results from Chapter 4 and many other reports (Orth and Bushuk
1972; Huebner and Wall 7976;Huang and Khan 1997; Sapirstein and Fu 1998; Bean et al.1998).
The variation of corelation significance between spring and winter wheat flours may be
attributed to growing season effects for samples included in this study. The r-values of
technological quality with protein fractions were substantially higher for the spring wheat
flours compared to winter wheat flours, particularly for MPP and IGp. As mentioned
previously, the significant differences in terms of the effects of protein content on protein
fractions for each of the varieties could have led to the differences observed in
correlations among flours from the two sets of samples. Nevertheless, the results
indicated an accurate assessment of the technological quality of flours in terms of
biochemical factors might be obscured for different sources of wheat.
The correlations between SIG and dough mixing parameters for all flours are presented in
Table 5-7. Previous research indicated a strong relationship between insoluble glutenin content and SIG20 (Wang and Kovacs 2002a). As expected, significant correlations were detected between SIG20 and dough strength parameters. Actually, SIG29p values had higher r-values for correlations with dough mixing data compared with those of IGp (Tables 6-5 and 6-7). Similar to the results of the effect of swelling time on SIG values discussed in Chapters 3 and 4, SIGO was not as good as SIG20 and SIG5 in predicting 95
dough strength parameters. Since negative correlations of SIG5P and SiGOp with flour protein content existed, SIG5P and SIGOP values were negatively related to dough
mixing data, and SiGOP had the highest significant correlations with TEG, DDT, ART, and STA.
Table 5-7 Simple Correlation Coefficients between SIG Values and Dough properties for Two Years Spring and Winter V/heats
SIG2O SIG5 SIGO SIG2OP SIG5P SIGOP Protein 0.94** 0.97*'+ 0.40*'r 0.1 9* -0.51*'r -0.97'F+ MDT 0.20* NS ns 0.60** 0.46+* 0.18'F ETP 0.66*+ 0.53*'¡' 0.19* 0.65** NS -0.38** TEG 0.JJ** 0.74** 0.21* 0.31** -0.35** -0.79** DDT 0.79** ** 0.81 0.32*'+ 0.29** -0.29** -0.71 ** ART 0.71 ** 0.74'++ 0.24'+ NS -0.39'k* -0.73** STA 0.79** 0.80** 0.43'+* 0.42*'* NS -0.59** MTI -0.91*'r. ** -0.75** -0.43 -0.79'** -0.26*'+ 0.36'k* FAB 0.82'r'* 0.76** NS 0.39** -0.35** -0.83** *, ** : < significant at P 0.05, P < 0.01, respectively; ns : not significant (n : 120). Abbreviations as defined in Tables 6-1 and 6-4.
CONCLUSIONS
Different N fertilization levels had strong influences on the quantity of protei' and its composition (monomeric protein, soluble glutenin, insoluble glutenin, and glutenin subunits) in wheat flours, but with respect to the quantities of protein fractions, samples demonstrated significant individual properties. The very strong relationships between the same protein fractions obtained by both sequential extraction and SE-HpLC procedures indicated that the two procedures could yield similar results from protein fractionation. The results showed that the proportion of protein fractions responded differently to nitrogen fertllization for different wheat types. As flour protein increased, tlie IGp 96
decreased systematically in both spring and winter wheat cultivars. The Mpp
significantly increased in spring wheat cultivars but not in most winter wheat cultivars.
The SGP increased signifìcantly in winter wheat cultivars but not significantly in most spring wheat cultivars. The ratio of HMW-to-LMW glutenin subunits significantly increased in both sets of samples.
Most dough mixing parameters (TEG, DDT, ART, STA, and MTI) conelate with absolute amounts of protein fractions. For the relative amounts, IGp showed better correlation (higher r-value) with dough mixing parameters than did the other protein fractions, especially for spring wheat flours. If we compare the r-values of correlations
(Table 5-4), it is seen that MDT correlated better with IGP, and poorly with IGF. DDT correlated better with IGF and poorly with IGP. IGP was significantly correlated with most dough mixing parameters in spring wheat flours, but in winter wheat flours, it was only correlated with MDT and ETP. The differences of the correlation coefficients between protein fractions and dough mixing properties in spring and winter wheat may be due to the proportion of protein fractions which responded differently to nitrogen ferttlization for different wheat types. 97
CHAPTER 6
Effects of Protein Content and Quality on White Noodle Making euality: Colour
ABSTRACT
Wheat cultivars, representing fìve winter and five spring wheats were grown in western
Canada with six levels of nitrogen fertilizer and flours were prepared from them with an
extraction rate of 65 %. Using a chromameter, flour colour and the colour of raw white
noodle sheets made from these flours with different resting times, and the cooked noodle
sheet colour were assessed. Both genotype (variety) and environmental (¡itrogen
fertilizatton) effects were signiñcant on flour and noodle colour, but their interactions
were not significant. As protein content increased with the increase of nitrogen
fertilization, brightness (¿*) of flour decreased and red-green chromaticity (a*) and
yellowness (å*) increased. Positive correlation coefficients of flour colour with particle
size index (PSI) were observed. The a* and å* values for flour were affected by moisture
content, whereas Z* values were not significantly correlated with moisture content. As protein content increased, the noodle brightness decreased with an increase in cf and b*
for the raw white noodle sheet. L* for noodle sheets was negatively correlated with its r¿*
and b* values. The percentage of monomeric protein in flour was equal to or better than protein content in terms of influencing most noodle sheet colour characters. Raw noodle brightness decreased, while red-green chromaticity and yellowness increased with rest time. In general, raw white noodles prepared from different wheat flours were ranked in terms of brightness and yellowness with flour protein content. 98
INTRODUCTION
Abont 40o/o of wheat products in Asian countries are consumed in the form of noodles
(Crosbie 1991). Noodle appearance is a key quality determinant of white salted noodles
prepared from wheat flour followed by cooked noodle texture. The most extensively
studied type of white salted noodle is Japanese Udon, but Chinese and Korean white
noodles have also received consideration (Hou 200I). Although all are made using only
flour, water, and salt, the traditional and regional preferences for each vary markedly in
texture, but slightly in appearance.
All types of noodles require good brightness, and the best white noodle colour is a bright
creamy white with the absence of any undesirable discolouration. Noodle colour and
discolouration depend on several factors, e.g., flour colour (Miskelly 1984), ash content
(Crosbie et ctl. 1990), flour extraction rate (Yasunaga and lJemura 1962; Hatcher and
Symons 2000), flour particle size (Hatcher et al. 2002), sprout damage (Kruger et al.
1995), protein content (Miskelly l9B4), and enzynes (Hatcher and Kruger 1996).
The flour used for Japanese white noodles is predominantly made from relatively soft wheat of low to medium protein levels (8-10%),low flour ash content (0.36-0.40o/o),low damaged starch, and a good colour grade (Nagao et al. 1977; Crosbie et at. l99O), giving a bright creamy appearance to the noodles. The lower damaged starch suggests that wheat hardness and granularity of the flour may be important. Entire starch granules reflect more light, and this reflectance decreases with increasing starch damage in the milling 99
process (Miskelly 1984). Flour particle size affects reflectance, and coarser flour appears
darker because of the shadow cast by the larger par-ticles (Hou 2001). Noodles from
flours with fine particles had the high brightness compared to that of noodles from coarse
particles (Hatcher et aL.2002). Therefore, soft wheat flour with fine particle size and flour
colour L* > 90 measured with a Minolta Chromameter is often required for Japanese
Udon noodles (Hou 2001). Because flour extraction rate and flour colour are closely
related to ash content, ash content is widely used as a key element in development of
specifications for noodle flours. However, in some studies colour appeared to be
influenced more by protein than ash content, because colour displayed abetter correlation
with protein content in flour (Miskelly 1984) and in white noodles (Jun et at. l99g).
ln an evaluation of the quality of Japanese noodles made from different wheat varieties,
Moss (I97I) found that brightness was inversely proportional to flour protein content.
Low protein level results in a less tough and lighter coloured dough for noodle formation
(Oh et al. I985b). Toyokawa et al. (1989a) carried out a fractionation and reconstitution
interchange of gluten, primary starch, tailing starch, and water solubles to investigate the role of each in Japanese noodle quality. They found that the gluten fraction of the flour mainly affected white noodle colour.
In addition to the effects of protein content on flour and its end-product colour, protein content also affects the water activity of a dough, which in turn influences the discolouration of noodle dough (Baik et at. 1995). Discolouration was associated with the degree of darkening developed by gluten washed from the samples, after drying under 100
standard conditions (Moss 1971). Within cultivars, discolouration of noodles was
affected more by protein than by enzymes such as polyphenol oxidase (Baik et al. 1995).
Across cultivars that vary widely in protein, discolouration was affected more by
cultivar-governed enzymes than by protein.
Our objective was to evaluate the effect of protein content on flour and noodle colour by
using a set of samples with wide protein content obtained from wheat varieties grown
with varied levels of nitrogen fertilization. In addition, the effects of variety and
environment on flour and noodle colour were investigated.
MATERIALS AND METHODS
Samples
Trials consisting of five spring and five winter wheat cultivars from two years (1994 and
i995) representing quality types and protein concentrations ranging from low protein soft
white wheat through Canada Prairie Spring (CPS) wheat and hard red winter wheat to high protein hard red spring wheat grown on dry land at Saskatoon and Clair,
Saskatchewan, and described in Chapter 5. Fowler (1998) described their growth conditions, nitrogen fertilization rates, yields, and protein content of wheats. Mr. Hideki
Okusu, Nippon Flour Mills, Japan provided commercial Japanese white flour (Udon) milled from Australian Standard White with an extraction of about 50-60 o/o, and this flour was used for comparison. r01
Moisture, protein content and PSi were determined and results were published (Fowler et
al. 1998). Protein fractions were determined by turbidity measurement as described in
Chapter 3.
FIour Colour Assessment
Flour colour was measured in triplicate by using the Minolta Colour Meter (model
CR210, Minolta, osaka, Japan), and Flour L*, a*, and, b* values were taken. Brightness * was measured by L+ and a higher Z value indicates better brightness. Flour ¿* indicates
red-green chromacity, and is also simply termed redness, with a high positive value
indicating increased redness. Flour å* indicates yellow-blue chromacity with a high
positive value indicating increased yellowness. Flour colour was measured for th¡ee
spring and three winter wheat flours grown at different nitrogen levels for two years.
Noodle Sheet Preparation
The noodle dough sheet was prepared as described by Kovacs et al (accepted 2002). For fresh noodle preparation, 20 g of flour was mixed with 6.4 mL deioni zed water containing 0.4 g sodium chloride for l0 min using a 30 gram pin mixer. The crumbly dough was rested for 40 min and then sheeted seven times using a bench-top noodle machine (Kovacs accepted, 2002). After the hrst pass (roll gap : 4 mm) the dough was folded once, passed through the same gap. The dough was reduced six times to a thickness of 1 mm. The final noodle sheet was sealed in a plastic bag and rested at room 102
temperature for colour measurement.
Colour Measurement
L*, a*, and b* values of noodle sheets were measured using the chromameter at 0,2, and
24 h aftet dough sheets were made. Colour measurements were made in three locations
across a dough sheet. After 24-h measurements, the noodle sheet was cooked in 500 mL
distilled water to the optimum cooking time as determined in Chapter 7, and the cooked
noodle sheets were rinsed in 300mL distilled water (room temperature) for 5 min and
then drained. Excess water on the surface of the noodle sheet was wiped off gently with
fìlter paper. Cooked noodle sheet colour was measured using the chromameter in three
locations across the cooked dough sheet. Noodle sheet colour was measured for five
spring and five winter wheats grown in 1995, and for three spring and three winter
wheats grown in 1994.
Statistical Analysis
Colour measurements of noodle sheets are averages of three determinations. Data were statistically analyzed with the SAS software (version g.2, sAS Institute, cary, NC) as described in Chapter 6. r03
RESULTS AND DISCUSSION
Effects of Flour Protein content and Characteristics on Flour Colour
As with all food products the quality of the starting material dictates the performance of
the final end product. Flour colour is regarded as one of the most important factors in
assessing the value of flour for Japanese udon noodles (Miskelly rgg4).
To adequately describe flour colour, brightness, and yellowness of flours from 72 flour
samples (three spring and winter wheats for each of two years) were evaluated. The flour
colour characters are shown in Appendix I Table 14. Almost all their Z* values were >
90, which indicated the flour extraction rate used in this study satisfied the Japanese
requirements for noodle production (Hou 2001). Generally, for most varieties, flour
colourZ* decreased, while a* and å+ values increased with increasing levels of nitrogen
fertilization to the highest level used Qa\ kglha). Some exceptions were also observed.
For example, the Z* value of 1994 spring wheat, Katepwa, with 240 kg/ha was the highest among the six levels of nitrogen fefülization used. Furtherïnore, the orders of colour value (L*, ct*, and å*) for most varieties used in this study were not exactly similar to the order of nitrogen fefülizer levels (protein content).
Two commercial noodle flours, one for Chinese white and another for Japanese white, were used in the present study as described in Chapter 8. The Chinese white noodle flour,
Lamian, was straight flour with 7 5% flour extraction rate, and its flour colour and noodle 104
colour were poor compared to those of the Udon noodle flour. On the other hand, both
Chinese white and Japanese Udon noodles have similar requirement on noodle
appearance, creamy bright. For comparison purposes, therefore, the commercial Udon
flour is used as a standard, and its colour values are presented in Appendix I Table i4.
Most nitrogen fertilization samples had L* and ó* values similar to that of Udon flour,
but Udon flour had a lower ¿* value. The reason for this difference is probably the use of
different flour extraction rates or other unknown factors; Udon had a 50-60% extraction
rate according to the information from the miller, while for nitrogen fertthzation samples
a 65%o extraction rate was used. When all colour characters were considered and
compared by multivanate analysis of variance (MANOVA), the colour of all flour
samples was significantly different from that of the Udon flour (results not shown).
Results in Table 6-1 report the pertinent features of the correlation matrix linking flour
quality and colour measurements on samples from two years harvest of three spring and
winter wheats grown with different amounts of nitrogen fertilization. protein content
showed a significant negative correlation with flour brightness, and positive correlations with redness and yellowness. Actually, flour colour grade is strongly associated with protein content (Bushuk et al. 7969; Miskelly 1984), and high protein content is often associated with increased redness (Barnes 1989). On the other hand, pSI showed significant positive correlations with flour brightness and negative correlations with redness and yellowness, supporting the results of Symons and Dexter (1991). pSI showed better correlations with brightness and yellowness than that of protein with flour colour
(Table 6-1). This indicated that granularity influenced flour colour; flour with finer t0s
particle size is brighter and whiter.
Table 6-1 Simple Correlation Coefficients of Flour Colour with Flour protein Content, Moisture, and Particle Size Index
L* a^ b* L* 1.00 -* -0.34** 1.00 b'É -0.60'F* NS 1.00 Prol -0.62** 0.56*r' ** ) 0.61 PSI- 0.70+'+ -0.46++ -0.96** Moisture NS 0.42*'+ 0.42** ns: not significant,*P 0.05, **P < 0.01 (n:72). 2Í ' Pro : protein content; PSI : particle size index.
Although moisture is not significantly correlated with flour brightness, it is signif,rca'tly
correlated with flour redness and yellowness (Table 6-1). This agreed with results of
Symons and Dexter (1991) who found that a* and. b* values were sensitive to moisture.
Among the flour colour characters, brightness was inversely related to yellowness and
redness, while no significant correlation between yellowness and redness was obserued
(Table 6-1).
Effects of Flour colour and Protein content on Noodle colour
It has been observed in previous studies that the brightness of noodle sheets is strongly affected by water absorption (Hatcher et at. 1999; Morris et at. 2000). To avoid this, wheat flours in the present study were all processed to give the same water absorption
(32%)' The flours had no difficulty passing through the sheeting rolls of the bench-top laboratory noodle machine at absorption. The colour of noodle sheets was measured at 0, 106
2, and 24 h and after being cooked. The multiple comparison using Fisher's least
significant difference test for raw white noodle colour of the samples from the different
wheat varieties employed in this study is shown in Appendix I Tables 15, 16 and 17 and
in Fig. 6-I. For all varieties, raw noodle brightness decreased, and redness and
yellowness increased with an increase in flour protein content resulting from nitrogen
fertilization. Generally, in spring wheats, the brightness (such as measurements at 2 h) of
noodles from samples with the 40 kglha, N treatment was the highest within a cultivar.
The L* value of noodles from samples with 0 kg/ha, N was lower than that of noodles
from samples of the 40 kglha, N treatment (Fig. 6-1 and Appendix I Table 15). Beyond
the three lower levels of nitrogen fertilization (0,40, and 80 kg/ha), the Z* value of
noodles decreased for samples with higher nitrogen levels. In contrast, the lowes t a* and
åx values were observed in noodles from samples grown with 40 kg/ha, N. The reason
for the brightest colour of noodles from samples with the 40 kglhawas believed due to its
low protein content (Fowler 1998).
In addition, for winter wheat, the brightness, redness and yellowness of noodles
corresponded with the protein content of flours. Protein content of winter wheat corresponded with nitrogen fertilization in a different quantitative way from spring wheat
(Fowler 1998). ln the winter wheat, although the lowest protein content of samples was observed at 40 k/ha nitrogen fertilization, protein content in samples of 0 kg/ha N treatment was higher than that of 40 and 80 kg/ha samples, and was similar to 120 kg/ha samples (Fowler i998). Therefore, noodles from samples with 40kglhaN treatment had the highest brightness and the lowest redness and yellowness. Noodles from samples 107
grown with 0 kglhaN were less bright than noodles from samples with 80 kglha, and had
similar brightness to noodles from samples with I20 kglha, N (Fig. 6-i and Appendix I
Table 16).
The changes in raw white noodle colour (L*, a*, and å* values over time) are also shown
in Appendix I Tables 15, 16, and 17. Two varieties, 1995 spring wheat Katepwa and
1995 winter wheat Norstar were also chosen as an example to show visible trends of
CHEW with protein content (Fig. 6-1). For all wheat varieties, raw noodle brightness
decreased with time, but increased after cooking. On the other hand, for all samples,
yellowness increased faster in the first 2 h and then increased slightly up to 24 h. This
result was similar to the observation by other researchers on alkaline noodles (Kruger el
al' 1994)- A marked decrease of bx values was observed after cook for all samples.
Redness increased with rest time, and obvious increases occurred between 2 and,24 h
determinations (Fig. 6-1). Similar to b* values, it was found that cooking can
dramatically decrease ¿* value.
For comparison, noodle colour from Udon flour is presented in Appendix I Tables 15,16,
and 17 ' Noodles from Udon flour had unique characteristics with high brightness and yellowness and low redness compared to noodles from other flours. At 0 h rest time, noodle colour from some test samples was very close to that of Udon flour noodles, e.g., brightness of CDC Kestrel, and Norstar. However, after 2 h rest, no noodle sheet possessed similar colour characters to those of the Udon noodle sheet. The changes in raw noodle coiour (L*, a*, and b* values over time) for the Udon sample were small 108
A D E]hr Ø2hr H24hr ñ24hr&cooked Z]h Ø2hr B24hr NI24hr&cooked 90
85 :BO
75
70
a 4
3 2 2
(! I 1
0 0
-1 -1 Nitrogen (kg/ha) Nitrogen(kg/ha)
24 24 aa 22 20 20 ä18 1B 16 Iu 14 14 12 12 10 10
Fig. 6-1. colour changes in noodles from Katepwa (4, B, c) and Norstar (D, E, F) wheat flours over time; brightness (A,D), redness (8, E) and yellowness (c, F).
compared with other noodle sheets, which indicated that the Udon noodle colour stability was superior to other noodles. Amazingly, the Udon noodle sheet still retained very good 109
(high) yellowness after cooking (Appendix I Table 15). When colour characters were
considered and compared by MANOVA in SAS software, all flour samples were
significantly different from the Udon flour (results not shown) for noodle colour at 2,24
h and after being cooked. Based on noodle colour, there was no flour in the test samples
that satisfied the requirements for Udon noodle production, although some flours from
white wheat had lower protein content than udon flour (g.70o/o), e.g., AC Reed at 40
kglha N (1994) withT.Iyo protein. This indicated that there could be other factors which
also contribute to udon flour colour, in addition to protein content.
A correlation matrix linking flour colour and protein content with noodle colour values is * shown in Table 6-2. The flour Z values were significantly correlated with the brightness
of noodles assessed af 2, 24 h, and after being cooked, but not at 0 h. They were also
negatively related to a* and b* values of noodles rested for 2 h. Positive correlations
between a* values of flour and a* values of noodles were observed, except for noodles
rested for 24 h. Correlations of flour å* values with noodle sheet a* and, å* values were
significant at 0 and 2 h assessments, but not with Zx values of the noodles. However, protein content was the single most impoftant factor, rather than flour colour, affecting the colour of the fresh and cooked noodle sheets, because stronger relationships were observed between protein content and noodle colour parameters than those between flour colour and noodie colour.
The brightness of the noodle sheets was negatively correlated with yellowness and llu
Table 6-2 Sirnple Correlation Coefficientsl among Floul and Noodle Colour Parameters of Thr-ee Spring and Three Wi'tel.Var.ieties
Flour Color Noodle Color Proz 0h 2h 24h 24 h cooked L* a* b* L* a* b* L* a* b* L* a* b* L* a* -0.59"* ns -0.29* ns 1.00 0h 0.71** ns 0.41** 0.31** -0.63** 1.00 0.42** ns NS 0.24* -0.61** 0.62** 1.00 -0.69** 0.31** -0.37* NS 0.92** -0.64** -0.51"* 1.00 2h 0.76** -0.27* 0.43** 0.26* -0.71** 0.95** 0.54** -0.76** 1.00 -0.27* 0.64** NS 0.50** -0.59** 0.67** 0.Bg** -0.58** 0.61** 1.00 -0.64** 0.34** -0.32** ns 0.Bg** -0.63** -0.48** 0.94** -0.79** -0.53** 1.00 24h 0.38** ns NS NS -0.67*" 0.66** 0.47"* -0.62"* 0.77** 0.39** -0.77** 1.00 0.74** -0.42** NS 0 .71"* -0.41** 0.68** 0.75** -0.44"* 0.60** 0.90** -0.42"" 0.29* 1.00 -0.44** 0.31** NS NS ** 0.56** -0.24* NS 0 .71"* -0.4 1 24h NS 0.74** -0.35** NS 1.00 0.62"* ns 0.40*" NS -0.71** 0.77** -0.77** cooked 0.36*" 0.86** 0.37** -0.91** 0.76** 0.29* -0.66** 1.00 ns ns -0.25* 0.37*" ns ns 0.50** NS ns 0.51** NS NS 0.58** NS NS 'ns : not significant, *P < 0.05, **P < 0.01 (n:72). " Pro : protein content. 111
redness, except for cooked noodle sheet brightness. Cooked noodle brightness was
negatively correlated with its redness. Compared to the results from flour colour
(discussed in the first section), redness was more highly correlated with brightness at 2 h
rest (-0.76, P < 0.01), and was higher than that of flours (r : 0.34,p < 0.01). ln addition,
a stronger correlation of yellowness with redness was observed at 0 and 2 h
determinations, but the correlations decreased as rest time increased to generate no
significant correlation for cooked noodles (Table 6-2).
Relationships between Protein Fractions and Noodre colour
Table 6-3 shows relationships between noodle colour and protein fractions expressed as
the percentage of protein fraction in flour (absolute content of protein fraction) and in
protein (relative content of protein fraction) for 1995 spring and winter wheat samples.
For spring wheat (Table 6-3), protein content was a good predictor for noodle brightness, redness and yellowness at The 2 h assessment. However, protein content was not significantly correlated with noodle yellowness for 0 h and in the cooked noodle, or with redness at 0 and 24 h. Similar to protein content, the absolute content of soluble glutenin and monomeric protein were significantly cor¡elated with brightness of the noodle sheet aT 0,2, and 24 h determinations. Soluble glutenin and monomeric protein were equal to or better than protein content in predicting noodle sheet yellowness and redness at 0, 2, and
24 h assessments. In addition, the correlation coefficients of relative protein fraction contents with colour were not as good as the absolute content. 112
In winter wheat (Table 6-3), protein content was a good parameter for predicting noodle
sheet brightness, redness, and yellowness, except for the cooked noodle sheet. The
absolute contents of soluble and insoluble glutenin was worse than protein content for
predicting noodle colour, but the absolute monomeric protein content was equal to or
better than protein content for predicting noodle sheet brightness. Similar to the results
from spring wheat, the relative protein fraction content did not provide any advantage for
predicting noodle colour, except that the proportion of monomeric protein was useful in
predicting cooked noodle brightness.
Effects of Nitrogen Fertilization and Genetic Factors on Flour and Noodle Colours
In order to investigate the effects of variety and nitrogen fertilization on flour and noodle
colours, analysis of variance was carried out on the three spring (Katepwa, AC Reed, and
Roblin) and three winter wheat varieties (586-375, Winalta, and Norstar) for each of two
years. Significant f,tgures from the two-way ANOVA analysis are shown in Table 6-4.
Flour brightness and yellowness were significantly affected by variety, whereas redness was affected by vanety, nitrogen fertilization, and interaction between the two. For cooked noodle colour, no significant effect was observed for interaction between variety and nitrogen. Both variety and nitrogen fertilization influenced Zx, a*, and, å* values, except for b* values ofnoodles at 0 h and after being cooked. I tJ
Table 6-3 Simple Correlation Coefficientsr between Noodle Colour and Protein Content and Fractions2 for 1995 Spring and Winter Wheats
0h 2h 24h 24 h an¡J cooked L* b* L* a* b* L* b* L1' h* 95 spring rvheats (n :30)
Pro -0.75** ns ns -0.g2** * 0.42* 0.51*r -0.73** NS 0.49** -0.54** 0.63 * NS MP/F -0.79** 0.49** _0.g2** _0.75** 0.45* 0.57x* 0.63** 0.39* 0.59** -0.43* 0.73 ** lls sG/F _0.79** 0.45* 0.70** _0.7i** 0.7 i ** _0.6g** ** 0.69** 0,80** 0.51 lts 0.74** 11S iG/F -0.48*1' ns rls -0.62** rls lls -0.52** lls lts -0.69** 11S -0.31r'
MP/P ns 0.60{,* 0.75*+ ns + 0.62*+ 0.51'¡'* ns 0.95'¡.x 0.41'r' 0.44* 0.43 11s SG/P ns ns 0.44* ns rls 11S lls 0.60** Ì1S 0.50+,k NS lls IG/P lls ns rls -0.67** -0.48** -0.43* ns -0.78*+ 11S -0.60** 11s 11S 95 winter rvheats (n :30)
Pro -0.J2** 0.79** 0.45* -0.i4*t _0.70,r,r. *'k 0.79** 0.60r,* 0.51*,¡. 0.53 11S 0.65** lts MP/F -0.79'r"r' 0.84** ns -0.92** _0.91r+ 0.93** 0.44* 0.67*'r, 11S -0.58** 0.91*'r. NS sG/F _0.67'k'Ë 0.65** 0.65** _0.66** 0.62** _0.55** 0.75** 0.37* 0.65** 1-ts 0.39* 11S iG/F _0.42* -0.38* 0.56+* 0.3gx 0.57** 11S 0.55** 11S 0.59*'r, lls 11s lls MP/P -0.49** 0.44* -0.50x* rls 0.44* ns -0.60** 0.67*'r. -0.31* -0.93** 0.74** -0.50*i' SG/P -0.44* ns 0.63** -0.41* 0.68'¡'* lrs lls NS 0.59** lts ns t-ts IG/P 0.6-7** _0.47** ns 0.66** -0.46* ns 0.69** -0.'71,** lls 0.74** -0.69** lts ns : not significant, xP < 0.05, x*p < 0.01. Pro : protein content; MPF andano lvlrrMpp : thetne percentage ' of monomeric protein in flour and protein, respectively; SGF and SGp : the percentage of soluble glutenin in flour and protein, respectively; IGF and IGP : the percentãge of insoluble glutenin in flour a'd protein, respectively. It4
Table 6-4 lnfluence of Nitrogen Fertilization Level and Variety Determined by Analysis of Variance (F-Test)t on Flour Colour and Noodle Colour from Three Spring ând Three Winter Wheats for Each of Two years
VxN L>k 24.94** NS NS Flour Q* 51.83-+* 22.67*'* 2.66*'4 b+ 129.77** NS NS g** L* 4.1 20.56*'+ NS 0h ^lk 3.21'4 13.56+* NS b* NS 7.36*"^ NS L* 5.95+* 21 .46** NS 2h ^* 4.93*'+ 16.09+* ns b* 4.93** i 0.09+* NS L'F x* ** 5.93 1 6.93 ns 24h a* 12.69"^* 12.72** NS ** bx 9.83** 5.1 5 NS L* ** 24I't 6.61 5.81** NS a* cooked 4.44*'* 23.86** NS h* NS NS NS ns : not significant, *P 10.05, *8P < 0.01. : 'V " N Variety and nitrogen fertilization interaction.
CONCLUSIONS
Protein, particle size and moisture of flour influenced flour colour, and particle size was
equal to or better than protein and moisture content in prediction of flour colour (L*, a*,
and b*)- Flour colour characters were slightly correlated with noodle sheet colour, but protein content was more strongly correlated with noodle colour. Protein in fractions, expressed as absolute and relative contents, were significantly correlated with most noodle colour characters, and the absolute amount of monomeric protein was equal to or better than protein content for predicting most noodle sheet colour characters.
There was a noticeable decrease in white noodle sheet brightness and an increase in yellowness and redness when noodle sheets were rested. Cooking recovered some 115 brightness and redness in the noodle sheets, but there was loss of some yellowness. The correlation coeffrcients of protein and protein fractions with cooked noodle sheet colour decreased in most situations.
Substantial differences in flour and noodle colour were found between test samples and
Udon commercial flour. Although protein contents of some test samples were lower than that of Udon flour, no flour among the test samples possessed the noodle sheet colour characteristic of Udon flour. This showed that Udon flour had considerably different intrinsic colour properties beyond protein content. Compared to noodles from other low protein flours, noodles from Udon flour had signif,rcantly high brightness and low redness, and especially their yellowness was stable, even after being cooked.
Both genotype (variety) and environmental (nitrogen fertilization) effects were significant on flour and noodle colour, except on óx values of noodles after being cooked.
The effects of interactions between genotype and environment were not significant on noodle colour. 116
CHAPTER 7
Effects of Protein content and Quality on white Noodle Making euality: Processing, Cooking Quality and Cooked Noodle Texture
ABSTRACT
This study describes the effect of protein content and its quality on the quality of white
salted noodles. One hundred twenty flours were obtained from five spring and f,rve winter
wheat cultivars grown at six levels of nitrogen fertilization for each of two years, and
these were used to prepare white noodles. Noodle-making quality was determined as
processing quality (length and thickness of fresh noodle and fresh noodle extension test),
cooking quality (optimum cooking time, cooked noodle thickness, cooking loss, and
water uptake), and cooked quality (cooked noodle texture). As expected, an increase of
protein content improved processing quality, and decreased cooking loss. Hardness and
chewiness of cooked noodles increased with protein content. In an optimum cooking time
test, the surface firmness of cooked noodles decreased, but increased in the constant
cooking time test with increased protein content. Data analysis indicated that the absolute
amount of insoluble glutenin was equal to or better than protein content in predicting
most quality parameters for noodle-making. However, the relative amount of each protein
fraction was not as good a predictor for noodle making quality as its absolute amount.
Both variety and nitrogen fertilization had significant effects on most noodle-making quality parameters. Flour swelling volume was negatively correlated with cooked noodle cutting stress. Dough strength parameters determined by mixograph and farinograph had significant corelations with noodle-making quality, but this was not the case for mixograph dough mixing time. The far less expensive and technically simpler SIG test was a satisfactory predictor for most noodle-making quality parameters. rt7
INTRODUCTION
Wheat is unique among other cereals since its flour when mixed with water can form a
dough which consists largely of starch granules surrounded by a hydrated film of gluten
proteins, that yields an array of properties which in turn generates a wide variety of wheat
products, such as breads, steamed breads, and noodles. Proteins, in both content and
quality, are recognized as the most important components governing breadmaking quality
(Finney and Barmore 1948; Bushuk et al. 1969). ln contrast to the extensive literature on
flour protein content and quality for bread-making, little has been reported on flour
protein content and quality for noodle making (Kruger 1996).
Oriental noodles differ widely in many respects including ingredients, preparation
methods, size, colour, texture, and type. The criteria used for judging white salted noodle
quality are cooked noodle texture (eating quality), followed by colour, taste, surface
appearance, cooking loss, and noodle yield (Toyokawa et al. I989a). There are regional
preferences for noodle colour and cooked texture, which in tum depend on the flour
characteristics, method of preparation, and the inclusion of other raw materials or
chemical additives. Bright, smooth white noodles with medium flrmness and strong
chewiness is usually preferred in China, while the Japanese prefer white noodles with
bright, smooth, and soft characteristics (Huang and Morrison 1988).
Both starch and protein play major roles in governing textural properties, and their contributions to noodle texture are of great importance. Starch components in wheat flour arc a major determinant of textural qualities of Japanese white noodles. This is supported 118
by the results of studies on starch characteristics (Oda et al. 1980) and fractionation and
reconstitution (Toyokawa et al. 1989a, b). Several investigations reported that starch
pasting properties, including high peak viscosity, high breakdown, and swelling power,
are responsible for superior Japanese white noodle quality (Od,a et at. 1980; Crosbie
1989, r99r; Toyokawa et al. 1989a; Konik et al. rgg2, lgg3; Epstein et al. 2002).
However, other types of white noodles, e.g., Chinese white, are not as soft as Japanese
white noodles. Protein content and quality are, therefore, the most important contributors
of cooked noodle texture of Chinese white noodles (Oh et al. L983; Huang and Morrison
1988; Huang and Lin 1990).
The protein content of wheat flour has long been associated with the "bite" of cooked
noodles (Nagao et c¿|. 1977). Generally, it is accepted that flour protein content has a
significant effect on raw noodle processing quality, cooking properties, and cooked
noodle texture. An increase in flour protein content decreased water absorption, and
increased thickness of the final noodle sheet (Oh et at. 1983, 19g5b). High-protein
noodles required longer cooking time and gave a higher cutting stress (ñrmness) than
low-protein noodles (Oh et al. 1985b; Huang and Morrison 1988). Flours with high protein content, especially those with too strong gluten, produced excessively firm cooked noodle texture that yielded problems in appearance and processing (Li 1996).
However, low protein causes breakage during the dryrng process and generates soft texture in Chinese white noodles (Oh et at. I985b; Huang and Lin 1990). Extremely low protein content and abnormally weak gluten reduce the quality and acceptability of
Japanese white noodles (Nagao 1996). 119
In addition to protein content, protein quality is another important factor, especially from
the processing, and cooked noodle texture perspectives. Huang and Morrison (i988) and
Baik et al. (1994a) observed a stronger relationship between SDS sedimentation volumes
and cooked noodle hardness than exist in relationships of protein content with noodle
texture. Recently, Wang et al. (1995) reported that Chinese white noodle quality (sensory
evaluation) was significantly related to mixograph dough development time, SDS
sedimentation volume, and alveogaph W index.
The effects of nitrogen fertilization on the yield and the protein content of wheat grain,
and on the baking quality of wheat flour, have been studied on numerous occasions
(Tanaka and Bushuk 1972; Bushuk et al. 1978; Gupta et al. 1992; Scheromm et al. 1992;
Jia et al. I996a, b; Pechanek et al. 1997). By the judicious use of nitrogen fertilizers both
the yield and the baking quality are improved, the latter being mainly due to increased
grain/flour protein level. However, there is no published work relating the effects of
nitrogen fertilization to noodle making quality.
In Chapters 6 and 7 it was observed that nitrogen, when applied at different rates,
increased the grain protein content and depressed the brightness of fresh white noodles
for spring and winter wheat cultivars. ln this chapter, the results of experiments will be
reported in which the noodle-making quality of flours prepared from wheat grown at
different levels of nitrogen fertilization have been determined. In addition, protein fractions have been determined from the flours and their functionalities have been shown by their correlations with noodle-making quality parameters. 120
MATERIALS AND METHODS
Wheat Samples and Quality Analysis
Five spring and five winter wheat cultivars grown at six levels of nitrogen fertilization
were used in this study. The detailed information on samples was presented in Chapter 5
and by Fowler (1998). Methods for protein fractionation (sequential extraction) and
quality assessments were discussed in Chapters 3 and 5, respectively. Flour swelling
power (FSP) was measured with a small-scale test (Fu et ar. r99gb).
Noodle Preparation
White noodles were prepared as described by Kovacs et al (2003, in press) with some
modif,rcations. Flour (20 Ð was mixed with deionized water containing 0.4 g sodium
chloride for 10 min using a 30 gram pin mixer. The crumbly dough was rested for 40 min
and then sheeted seven times using a bench-top noodle machine (Kovacs 2003, in press).
After the first pass (roll gap : 4 mm) the dough was folded once, passed through the
gap. same The dough was reduced six times to a thickness of 1 mm. The prepared sheet was cut into noodles (6.25 x i mm cross section). To avoid handling problems during processing such as sticking on the rolls or to each other during the rest period, an optimum water absorption rate was used in this stud¡ based on flour protein content and dough sheet processing behaviours. The optimum absorption (in percent based on flour with l4Yo m.c.) for flours from different nitrogen rates are shown in Table 7-1. V/ith optimum absorption, noodles from all tested flours were successfully processed without r21
sticking at rest time.
Table 7-1 Optimum Water Absorption (%) for Flours with Different Nitrogen Levels
N Rate 0 kg/ha 40 kg/ha 80 kg/ha 120 kg/ha 160 kg/ha 240 kgtha Absorption 32.5 33.5 32.5 32.0 31.5 31.0
Raw Noodle Processing Quatity
The final dough sheet length (DSL) and thickness (THICK) (20 g flour) were recorded
immediately after noodle sheet preparation. Noodles were cut into pieces of 6.25 x 100
mm, and rested for 2 h in plastic bags.
AREA
DIST
Fig. 7-1. Representative fresh noodle extension curve showing measured indices of maxlmum resrstance, area, and distance.
Fresh noodle extension testing was performed using a TA.XT2i textur e analyzer equipped with a Kieffer Dough & Gluten Extensibility Rig (Stable Micro Systems, 122
Scarsdale, NY, USA). The hook speed was set up for 3.3 mm/s. One piece of fresh
noodle was placed in the Kieffer Rig, and was stretched by the hook for each
measurement. From the extension force-distance curve (Fig. 7-I), the maximum
resistance (Rmax), area (AREA), and distance (DIST) were determined.
Cooking Quality Assessments
The optimum cooking time (OCT), which was the time required for the white core in the
noodle strand to disappear, was determined by cutting a test piece (Huang and Morrison
i988)' Cooking loss (CL) was measured according to AACC Method 66-50 (AACC
2000). The ratio of cooked noodle weight to fresh noodle weight was defined as water
uptake (WT), which expresses the cooked noodle yield. The cooked noodle thickness
(CNT) was determined instrumentally in the TPA test described in the following section.
Noodle Texture Analysis
All textural properties of cooked noodles were determined on the TA.XTZ¡ texture
analyzer equipped with a windows version of the Texture Expert software package
(Stable Micro Systems, Scarsdale, Ny, USA). A 9 mm width flat probe was used in TPA, compression, and surface firmness tests.
Noodles were cooked at optimum and constant (10 min) cooking time, respectively.
Cooked noodles (15 g) were then rinsed in 300 mL distilled water (room temperature) for
2 min' The texture profile analysis (TPA) was done by the procedure of Balk et al. r23
(1994a)- A curve was generated by which many factors could be determined to provide
an assessment of noodle characteristics. Textural measurements included hardness
(HARD), adhesiveness (ADHE), springiness (SPRI), cohesiveness (COHE), gumminess
(GIIM), chewiness (CHEW), and resilience (RESI).
At 6 min after rinsing, the compression test was performed using the method described
by Oh et al. (7983). The recovery ßECOV) from compression tests correlated highly
with sensory perception of chewiness (Oh et al. 1983). At 8 min after rinsing, cooked
noodle surface firmness (SFM) was assessed by the procedure of Oh et at. (I9B5a).
Cutting stress (CS), a commonly used parameter for noodle hardness, was determined by
AACC Method 66-50 (AACC, 2000), at 10 min after rinsing.
Three replicates of TPA, RECOV, SFM, and CS measurements were taken for three
noodles each.
Data Analysis
Data were analyzed as described in Chapter 5.
RESULTS AND DISCUSSION
Effects of Protein content and composition on Noodle-making euality
The effects of protein content on fresh noodle characteristics (DSL, THICK, and 124
extension test) were determined as possible indicators of raw noodle processing quality
and results are presented in Appendix I Tables 18-21. The DSL and THICK (20 g flour)
were used to measure the influence of the protein content on the final product size. Flours
with 40 kglha, N, the lowest protein content (Appendix I Tables 10-13), gave noodles
with the greatest DSL and the smallest THICK among noodles prepared from wheat
samples treated with six levels of nitrogen fertilization. Significant decreases in DSL and
increases in THICK were observed when the protein increased. Extension tests on fresh
noodle strength were assessed as possible indicators of fresh noodle strength, which is
important to prevent the breakage of noodles during the drying process. Rmax, AREA,
and DIST increased with higher protein content when nitrogen fefülizer was applied from
80 to 240 kg/h for spring wheat cultivars and fforn I20 to 240 kg/ha for winter wheat
cultivars. For most varieties, the greatest changes in AREA occurred as nitrogen levels
increased fiom 160 to 240 kg/ha.
Cultivars also had significant effects on DSL, thickness, Rmax, AREA, and DIST
(detailed results not shown). In terms of AREA, the largest AREA was produced by
noodles from BW 90 among 1994 spring wheat cultivars, from Roblin among 1995
spring wheat cultivars, and from Norstar among both 1994 and 1995 winter wheat cultivars. Noodles from AC Reed and 586-375 gave the smallest extension areas for spring and winter wheat cultivars, respectively. Actually, raw noodle processing quality parameters were closely related to dough strength. The stronger the dough, the shorter the length of the fresh noodle sheet with high Rmax, large AREA, and long DIST. Trends of thickness decreases with dough strength were also observed by Oh et at. (I9g3). 125
CL and WT, commonly used indicators of noodle cooking properties, were measured for
three spring and three winter wheat cultivars of each for two years and results are
presented in Appendix I Table 22. Significant decreases in CL and WT were observed
when protein content increased. Both CL and WT were greatest in the nitrogen fertilizer
treatments applied at 40 kg/ha. Optimum cooking time increased with protein content,
although the differences in each nitrogen level for some varieties were within
experimental error (Appendix I Tables 18-21). Appendix I Tables 18-2i also give the results of thickness measurements of cooked noodles. Similar to the fresh noodle thickness, an increase in cooked noodle thickness was observed with increased protein content.
N katepwa(94) ØAC Reed (94) E Robtin (94)
27 .C E 22 E U, Ø 17 C) 12 80 120 160 240 N Rate (kg/ha)
Fig.7-2. Effect of nitrogen fertilization on cooked noodle-cutting stress (CS) for three 1994 spring wheat cultivars.
Cooked noodle texture, the most important factor in evaluation of noodle quality, was determined for both optimum and constant cooking time. In Appendix I Tables 23-27 and,
Tables 28-31 the results of cooked noodle texture are presented with constant and optimum cooking time, respectively. 126
Hardness is a measure of the firmness of the noodles and is probably the most important
texture parameter. Hardness was measured as the maximum cutting stress (CS)
determined in the cutting stress test of the AACC Method (AACC 2000) and as the
maximum peak (HARD) of the first compression in the TPA test. As expected, CS and
HARD for each variety increased more or less linearly with the total protein content.
Similar results for CS (Oh et al. 1983; Huang and Morrison i988) and for HARD (Baik
et al. I994a) were published. The effects of protein content (nitrogen fertllizatjon) on CS
are shown in Fig. 7-2 for tluee 7994 spring wheat cultivars as an example. The f,irmest
noodles were obtained from flours with 240 kg/ha, N, and the softest were from flours
with 40 kglha, N. However, exceptions were also observed, such as the same values of
CS for AC Reed with 160 and240kglha,N. More exceptions can be found in Appendix I
Tables 23-3I. However, differences of hardness between varieties showed that the softest
noodle texture was obtained from AC Reed and 586-375 which were the weakest
cultivars among spring and winter wheat, respectively. This indicated that gluten strength
also played a roie in cooked noodle hardness.
The chewiness of cooked noodles was determined by recovery (RECOV) in compression
tests developed by Oh et al. (1983) and chewiness (CHE'W) in TPA tests. In TpA tests,
CHEW is the product of (hardness x cohesiveness) x springiness; it is thus a single
parameter that incorporates three of the important textural characteristics (Baik et al.,
1994a)- Both RECOV and CHEW in the current study increased with flour protein content (Appendix I Tables 23-31), which was in agreement with other studies (Oh et al.
1983; Baik et al. 1994). Three varieties of 7994 spring wheat cultivars were also chosen as an example to show visible trends of CHEW with protein content (Fig. 7-3). The t27
chewiest noodles were obtained from flours with 240 kglhaN for AC Reed and Katepwa,
but from flours with 160 kglha N for Roblin. The lowest value of CHEW of AC Reed
was observed in agreement with its weak gluten property, but the chewiest noodle was
obtained from Katepwa although Roblin had higher protein content and stronger gluten
compared to that of Katepwa (Appendix I Table 10). This indicated that differences of
CHEW among varieties could not be completely explained by their protein content and
gluten quality. Other unknown factors, such as starch composition may be considered in
explaining this difference.
NKatepwa Qa) ØAC Reed (94) ERobtin (94)
o) 1400 th th o 1200 't o 1 000 o 800 B0 120 160 N rate (kg/ha)
Fig. 7-3. Effect of nitrogen fertilization on cooked noodle chewiness for three 1994 spring wheat cultivars.
Noodle springiness (SPzu) indicates the degree of recovery after compression by the probe, and cohesiveness (COHE) is a measure of the extent to which noodle structure is disrupted during the first compression by the probe. Noodle resilience is how much the noodle recovers its shape after first "bite". Although some trends in slightly higher Spzu,
COHE, and RESI were shown in the direction of the higher protein content, the differences between nitrogen levels for some cultivars were within experimental error 128
(Appendix I Tables 23-31). Adhesiveness (ADHE) showed a larger experimental error,
and no trends with protein were found although the differences among nitrogen levels for
some cultivars were significant. This was in agreement with the results of Balk et al.
(1994a).
A smooth surface is preferred in all kinds of cooked Asian noodles, especially when
noodles are served with soup. Cooked noodle surface smoothness was evaluated with
surface firmness (SFM) (Oh et al. 1985a). The smooth surface of cooked noodles should
have high SFM values. SFM in constant cooking time tests for each cultivar increased
significantly with protein content (Appendix I Tables 23-31). This was in contrast to the
result of oh et al. (1985a,b) who developed a sFM method and found that the surface
firmness of cooked white salted noodles made from high-protein flour in optimum
cooking time tests was poorer than that from low-protein flour. However, SFM in
optimum cooking time tests decreased significantly with protein content for most
varieties, especiallywhen nitrogen levels increased from 120 to 24Okglha. This was in
agreement with the results of Oh et at. (I985a,b), but was in contrast to the results of
Kruger et al. (1994) on alkaline noodles. Therefore, cooking time looks to be an
explanation for controversial results. Oh et al. (1985a) reported that the longer the
cooking time, the lower the surface firmness. For constant cooking time (10 min), low protein noodles cooked longer than necessary, and their surface structure was destroyed more than high protein noodles. The SFM of low protein noodles, therefore, was lower than that of high protein noodles. For optimum cooking time, however, the cooking time for high protein content was higher than that of low protein noodles, and the surface structure of high protein noodles was destroyed the longer the time. This was probably 129
the reason for the low SFM value for high protein noodles in the optimum cooking time
test.
Effects of Genotype and Environment on Noodle-making Quality Parameters
Effects of variety, nitrogen fertilization, and variety x nitrogen interaction on raw noodle
processing quality are presented in Table 7-2 as results of ,F-values from analysis of
variance. Variety accounted for the majority of the variability in fresh noodle processing
quality, including DSL, THICK, Rmax, AREA, and DIST. The variety x nitrogen
interaction had significant effects only on AREA.
Table 7-2 Analysis of Variance,F-Values and Level of Significance for Effects of Variety, Nitrogen Feftilization, and Their lnteractions on Raw Noodle processing Properties
NS THICK 21.91** I 1.90** NS RMAX 13.20** 7.36** NS AREA 43.7 5** 32.74+* 1.68* DIST 3 8.55** 25.17** *, ** : significant at P < 0.05 and < 0.01, respectiv sheet length (cm); THICK: fresh noodle thickness (mm); Rmax: maximum extension resistance (g); AREA : aÍea under the extension curve (g.s); DIST : extension distance (mm); V x N: variety and nitrogen interaction.
No significant variety x nitrogen interactions were found for noodle cooking quality
(Table 7-3)' Variety had a significant effect on CL, WT, OCT, and CNT, and accounted for the majority of variability in WT, OCT, and CNT. Nitrogen fertilization had a significant effect on CL, Vy'T, and OCT, and accounted for the majority of variability in CL. 130
Table 7-3 Analysis of Variance F-Values and Level of Signiñcance for Effects of Yariety, Nitrogen Fertilization, and Their Interactions on Nãodle Cooking properties
WT 18.36** 6.29++ OCT 2'l .'72+* 7.92+* ns CNT 4.69** *. ** NS NS - sisniflrgnrtrcant at P < 0.05 and < 0.01, respectively; ns@ cooking loss (%); wr : water uptake (gg); ocr: optimum cooking time (s); cNT: cooked noodle thickness (mm); v x N: variety and nitrogen interaction.
the In constant cooking time test, variations among varieties were highly significant on
cooked noodle texture parameters (Table 7-4), and. these were true in optimum cooking
time tests as well (Table 7-5). The effects of nitrogen on most cooked noodle texture
parameters in the constant cooking time test were significant, except for RECOV, ADHE,
Table 7-4 Analysis of Variance F-Values and Level of Significance for Effects of Variety, Nitrogen Fertilization, and Their lnteractions on Cooked Noodle Texture in the Constant Cooking Time Test
Variety Nitrogen VxN RECOV 4.32** NS NS CS 1 1.gg'k* 10.96** NS SFM 5.79** 20.74** NS HARD 3.gg** 6.01** 11S ADHE 2.69* NS ns SPzu J. /O"- 3.29* ns COHE 20.69*'" NS NS GUM 5.69+'k 6.90** NS '/.60'+* CHEW 6.24+* lls RESI 19.90** 2.59* NS *, ** : significant at P < 0.05 and < compression recovery; cs : cutting stress (g/^*'); : : sFM su.fuce firmness (g/s); HARD TPA hardness (g); ADHE : TpA adhesiveness (g.s); SpRI: TpA springiness; coHE : TPA cohesiveness; GUM : TpA gumminess (g);'cnpw: TpA cheiinÃs 1g;; RESI: TPA resilience; v x N: variety and nitrogen interaction. and coHE. However, only RECoV, cs, GfrM, and cHE'w were si.gnificantly i31
influenced by nitrogen fertilization, when noodles were cooked with optimum cooking
time. Two-way interactions among variety and nitrogen fertilization were not significant
in both cooking time tests.
Table 7-5 Analysis of Variance F-Values and Level of Significance for Effects of Variety, Nitrogen Fertilization, and Their Interactions on Cooked Noodle Texture in the Optimum Cooking Time Test
Ni VxN
I 1.50** NS CS 15.26** 15.49** NS SFM 5. I 8+* NS tls HARD 3.17+* NS ns ADHE 2.49* NS ns SPRI 2.41* NS NS COHE 13.59** NS NS GUM 4.16'+* 2.39+ NS CHEW 4.41** 2.59* NS RESI I 1.80** *, ** : NS NS significant at P < 0.05 and < 0.01, respectively; ns : not significant; Abbreviations as defined inTable 7-2.
correlations Between Protein Fractions and Noodle-making euality
The relationships between raw noodle processing quality and protein composition are
shown in Table 7-6 for spring (n:60), winter wheat (n:60) cultivars, and all of them together, respectively. It was observed that quite different relationships were derived between the raw noodle processing parameters and the protein fractions quantif,red as absolute and reiative amounts. For all samples together, the absolute amounts of monomeric protein (MPF), soluble glutenin (SGF), and insoluble glutenin (IGF), as with flour protein content, showed high to very high correlations with raw noodle processing quality. However, the reiative amounts of monomeric protein (MPP), soluble glutenin 132
(SGP)' and insoluble glutenin (IGP) showed much lower or no significant correlations
with raw noodle processing quality. IGF had higher r-values with dough sheet length (DSL) and thickness than protein content, which indicated that the absolute amount of
insoluble glutenin determined DSL and the thickness of fresh noodles. Higher r-values were also observed in associations of IGF with AREA and DIST compared to MpF and
SGF.
Table 7-6 Correlation Coefficients between Protein Parameters and Raw Noodle Processing euality
SGF MPP SGP IGP SIG2O SIGs SIGO SIG2OP SIGSP SIGOP Spring Wheat Gultivars (n=60) DSL -0.84". -0.74-'* -0.26* -0.94-- o.42* 0.70." -0.68.* _0.95** -0.93.. -0.43.. -0.7S-" ns 0.82.* THTCK 0.80* 0.71.- ns 0.89.- -0.38-. -0.70.. 0.64.. 0.9.1-. 0.88"* 0.40." 0.72.- ns -0.77*. Rmax 0.82* 0.79.. 0.29* 0.80.. ns -0.61.- 0.43* 0.79** 0.81.. 0.36.- 0.49." ns -0.82** AREA 0.90"- 0.81-. 0.60*. 0.84.. -0.30. -0.30- 0.34* 0.84** 0.88* 0.48.. 0.43.- ns -0.S0-- DIST O.B7*- 0.76". 0.62.. 0.86* -0.40--0.28.0.42'*0.87** 0.90* 0.48.. 0.51*. ns -0.78-. Winter Wheat Cultivars (n=60) DSL -0.90.. -0.78-. -0.76** -0.90." ns -0.55.- -0.28* -0.92.* -0.91** -0.68** -0.56.- ns 0.SS** THTCK 0.84-. 0.72.- 0.77** 0.83** ns 0.57.t ns 0.84*. 0.88.. 0.77*" 0.46.. ns -0.42-* Rmax 0.68-. 0.61.. 0.52-- 0.64** ns 0.34-. ns 0.66-. 0.64.- 0.41*. 0.40.. ns -0.48* AREA O.B9-- 0.78.- 0.83-. 0.83** ns 0.59.- ns 0.87.. 0.90** 0.70*" 0.39* ns -0.S8.- DIST 0,B6-- 0.75.. 0.81*. 0.80*. ns 0.59*. ns 0.8S.. 0.88*" 0.72*. 0.40". ns -0.53.- Total (n=120) DSL -0.84-- -0.73.- -0.46-" -0.93*. 0.22* ns -0.44.. -0.94*. -0.91** _0.43-. _0.60-" ns 0.66." THICK 0.80.. 0.69.. 0.46** 0.87." -0.22" ns 0.40.. 0.89*. o.B7*. o.4B-. 0.56-. ns -0.59*" Rmax 0.80.. 0.76-. 0.43.. O.zs* ns ns 0.19" 0.74** 0.73** 0.24,- 0.33-" _0.2s*. -0.70*. AREA 0.89". 0.79** 0.67.. 0.84"* ns ns ns o.B5** 0.Bg** 0.45.. 0.33." _0.23. -0.68-. Dlsr 0.83-. 0.71*. 0 66.* 0.84"* -0.2s"* ns 0.24* 0.86** 0.89** 0.52." * 0.41"- ns -0.60.- and ** : significance at P < 0.05 and p. protein content; MPF and MPP : the percentage òf monómeric proteir in flour and protein, respectively; SGF and SGP : the p.tc"ntage of soluble glutenin in flour and protein, respectively; : IGF and IGP the percentagJ of insoluble llutenin in flour and protein, respectivell;,llc : : swelling index of glutenin with 20 miriswelling time; SIGp the percentage of SIG values in protein; SIGZO, SIG5, and SIGO : sweli-ing index of glutenin with20,5, 0 1nd min swelling time, respectively; sIG2Op, sIG5p, and sIG0p: the percentage of SIG20, SIG5, and SIGO in protèin, respectively; ôth., abbreviations as defined inTable 7-2. 133
The SGP from spring wheat flours gave a significant positive correlation with DSL (r:
0.70, P < 0.01) and negative correlations with THICK, Rmax, AREA, and DIST.
However, the SGP from winter wheat flours was negatively correlated with DSL and
positively correlated with THICK, Rmax, AREA, and DIST. Since processing quality
parameters reflected dough strength, the quite different comelations of SGp with
processing parameters between the two sets of samples were quite similar to the different
correlations of SGP with dough mixing parameters as discussed in Chapter ó. The IGp
was significantly correlated with all processing parameters in spring wheat flour, but only
significantly with DSL in winter wheat flours. Similarly, the Mpp had significant
correlations with processing parameters except Rmax in spring wheat flours, but did not
correlate with any parameters in winter wheat flours.
The swelling index of glutenin (SIG) with different swelling time had significant
correlations with all processing parameters, and the r-values of SIG20 and SIG5 were
higher than that of SIG0. However, both SIG2OP and SIGOP were highly correlated with
raw noodle processing properlies, whereas SIG5P had very low levels of r-value or no
significant correlations.
Tables 7-7 and 7-8 give the relationships of protein parameters with noodle cooking properlies. Protein content had positive correlations with optimum cooking time (OCT)
and cooked noodle thickness (CNT). For both spring and winter wheat cultivars, IGF was equal to or better than protein content, MPF, and SGF in prediction of OCT and CNT.
OCT and CNT were correlated with SGP and IGP significantly for spring and winter cultivars, whereas MPP had no such correlations. Table 7-8 gives correlation coefficients between protein and cooking parameters (CL and WT) from samples of three spring and 134
three winter wheat cultivars for each of two years. Protein content and the absolute
amount of protein fractions were negatively correlated with CL and positively with WT,
Table 7-7 Conelation Coefficients between Protein Parameters and Noodle Cooking Properties
Pro MPF SGF IGF MPP SGP IGP SIG2O SIGs SIGO SIG2OP SIGsP SIGOP Spring Wheat Cultivars (n:60) OCT 0.65*'ß ns _0.J2** 0.64++ 0.70** ns 0.47*'" 0.66+* 0.69r* ns 0.46** ns -0.77** cNT 0.43** 0.41'k'k ns _0.69** 0.53+* ns 0.45** 0.54.+* 0.50** ns 0.49** ns -0.50** Winter Wheat Cultivars (n:60) 0.44** 0.36** ocT 0.29* 0.54** ns 0.32* 0.42** 0.52** 0.45** ns 0.48** ns -0.35 ** cNT 0.60** 0.49** 0.51+* 0.66'3'k ns 0.34** 0.26* 0.6g** 0.62** 0.40** 0.49** ns -0.42** Total (ll :120) OCT 0.65** 0.63** 0.29*'F 0.65*+ ns ns 0.20+ 0.61** 0.5'7** ns 0.26* -0.24+ -0.71 ** CNT 0.52** 0.48** 0.21* 0.57*'4 ns ns 0.25++ 0.58** 0.53** ns *, ** : 0.37** ns -0.51** significant at P <0.05, P < 0.01, respectivelyi ns : not significant. Abbreviations as defined in Tables 7-3 andT-6.
Table 7-8 Correlation Coefficients between Protein Parameters and Noodle Cooking Properties
Pro MPF SGF IGF MPP SGP IGP SIG2O SIG5 SIGO SIG2OP SIG5P SIGOP cL _0.47** -0.42** _0.50*x -0.41 ** ns -0.29* ns -0.45x* -0.51** -0.54** -0.25* ns 0.26* WT ns 0.31** 0.27* 0.35** -0.29* ns ns 0.31** 0.26* NS ** *, ** : NS ns -0.41 significant at P < 0.05, P < 0.01, respectively; ns : not significant (n : 72). Abbreviations as defined in Tables 7-3 andT-6.
except for the SGF' In contrast, the relative amount of protein fractions was poor in predicting CL and WT. SIG20 and SIG5 also showed strong relarionships with OCT,
CNT, CL, and WT (TablesT-7 and 7-8), but only SIG2OP and SIG9p had significanr correlations with most cooking quality parameters.
Simple linear correlation coefficients between protein parameters and cooked noodle texture with constant and optimum cooking times for spring and winter wheat cultivars are both summarized in Tables 7-9 and. 7-lO. For all flours, the results showed that 13s
protein content was positively correlated with cooked noodle hardness (CS and HARD),
chewiness (RECOV and CHEV/), and several other texture parameters such as GIIM,
SPzu, COHE, and RESI. This was in agreement with other previous reports (Oh et al.
i983; Huang and Morrison 1988; Balk et at. 1994a). Similar results were obtained from
the correlations between absolute amounts of protein fractions and cooked noodle texture
parameters. IGF was superior to SGF and MPF in relation to RECOV, CS, COHE, GftM,
CHEW, and RESI in constant cooking time tests, and in relation to CS, COHE, and RESI
in optimum cooking time tests. Actually, IGF had even higher r-values with COHE and
RESI than with protein content in constant cooking time tests. The correlation
coefficients of protein content with SFM were positively significant in the constant, but
not in the optimum cooking time test. This result was in agreement with Kmger et ctl.
(1994) and Oh et al. (I985b). Generally, protein content and absolute amounts of protein
fractions had higher r-values with cooked noodle texture in constant cooking time tests
than that in optimum cooking time tests. Furthermore, values of correlation coefficients
of cooked noodle texture parameters were substantially higher with absolute amounts of
protein fractions compared with their relative amounts. The relative contents of protein
fractions had significant correlations with some cooked noodle texture parameters, but in
general, almost all significant correlations were low with r < 0.50.
Values of CS for cooked noodles in constant and optimum cooking time tests were
plotted as functions of flour protein content, IGF and IGP (Fig. 7-4).Itwas found that the
CS was highly correlated with protein content and IGF, but very low r-values occurred
with IGP. Similarly, the CHEW of cooked noodle with constant and optimum cooking time correlated better with protein content and IGF than with rGp €ig. 7-5). t36
R2 0.63 = A R2 = 0.52
40 40 ^35 35 E30 'E so F tr ã25 ózo õzo o 'f5 o1s
10 10 7 I 11 13 15 579111315 Protein Content (%) Protein Content (%)
R2 = 0.68 R2 = 0.54
'E 40 ^30 ^35 830 F 9zo <"ÃÐ-" o1sU) o1sózo
10 10
R2 = 0.05
35 40 30 a ^35 aa a E E30 E 25 -î o) 20 ;25 U) (J õzo 15 o15 t 1.. tt' 10 10
Fig. 7-4- Cooked noodle cutting stress (CS) in constant cooking time as a function of percent of protein (A), insoluble glutenin in flour (B) and in protein (C); and in optimum cooking time as a function of percent of protein (D), insolublè glutenìn'in flour (É) and in protein (F) for total samples (n: l2O). 137
R2 = 0.55 A R2 = 0.20
2000 2200 1 800 2000 I tooo 6; 1800 r+oo a rooo fr urI 1400 1200 ï '.ctl. 8 ¡l' o 1200 t. I 000 .t' 1 000 o a 800 800 5 7 I 11 13 15 I 11 Protein Content (%) Protein Gontent (%)
R2 0.50 = R2 = 0.'13
2000 2200 1 800 2000 O rooo Þ) 1 800 r+oo 3 1 600 fr ut 1400 1200 ! ð (J 1200 1 000 Y.' 1 000 800 800 1.5 2 2.5 3 3.5 4 tcF (%)
R2 = 0.03 C R2 = 0.00
2000 2200 1 800 2000 g aaa. .l rooo €; 1800 t r+oo B 1600 ¡t fr rrr 1400 a 1200 ¿ ð -.. o 1200 1 000 õ. " 1 000 .-'.í;ff;-"' 800 800 17 22 27 32 tcP (%)
Fig. 7-5. Cooked noodle chewiness (CHEW) in constant cooking time as a function of percent of protein (A), insoluble glutenin in flour (B) and in proteìn (C); and in optimum cooking time as a function of percent of protein (D), insolublè glutenìnin flour (É) and in protein (F) (n :120). 138
Nonsignificant correlations between IGF and CHEW were actually found for constant and optimum cooking times. A clustering of points in optimum cooking time tests
observed for CS and CHEW was spread out more than that in constant cooking time tests.
Similar to correlations with protein content in total samples, the absolute content of
protein fractions for flours from both spring and winter wheat cultivars were significantly
correlated with most parameters of cooked noodle texture except for ADHE (Tables 7-9
and 7-70). For constant cooking time of spring wheat cultivars, MPF and IGF had strong
correlation with cooked noodle texture, and only SGF was not significantly correlated
with some texture parameters, including cs, HARD, coHE, GII\4, and GHEV/. Some
significant corelations of absolute contents of protein fractions with cooked noodle
texture in constant cooking time tests were lost in optimum cooking time tests, such as
that with SPzu, RESI for spring wheat cultivars, and SPRI, COHE, and RESI for winter
wheat cultivars. Similar to the results of relationships between raw noodle processing
quality and relative amounts of protein fractions, variation between spring and winter wheat cltltivars in the level and significance of the correlation results was also observed in both optimum and constant cooking times. In the constant cooking time tests (Table 7-
9), IGP had higher correlation coefficients and significantly cor¡elated with more cooked noodle texture parameters for spring wheat cultivars than winter wheat cultivars (Table 7-
9). SGP was negatively correlated with noodle texture parameters with constant cooking time for spring wheat cultivars, but positively for winter wheat cultivars. For optimum cooking time (Table 7-I0), weak correlations between relative amounts of protein fractions and cooked noodle texture parameters were observed, and IGp was significantly correlated with CS, SFM, SPRI, COHE, and RESI for spring wheat cultivars, and only t39
with COHE and RESI for winter wheat cultivars.
The SFM of cooked noodles were positively correlated with absolute amounts of protein
fractions for both spring and winter cultivars in constant cooking time tests, while only
SGP of winter wheat cultivars had significant correlation with SFM in all three relative
amounts of protein fractions (Table 7-9).In optimum cooking tests, SFM was positively correlated with SGF and negatively with IGF for spring wheat cultivars, and
nonsignificant correlations were found in winter wheat cultivars. This was in agreement
with the changes of SFM with protein content within individual varieties discussed in
previous sections.
The correlation coefficients of SIG20 and SIG5 with most texture parameters were equal
to or better than that of IGF in both constant and optimum cooking tests (Table s i-9 and 7-10)' This indicated that the far less expensive and technically simpler SIG test was a
satisfactory predictor for most cooked noodle texture parameters.
Conelations between flour swelling power (FSP) and cooked noodle texture parameters in constant and optimum cooking time tests are listed in Table 7-8 andT-10, respectively.
It is generally accepted that FSP (or starch swelling power) is the major factor contributing to Japanese white noodle texture (Crosbie, 1989, 1991; Crosbie et at. 1992;
Konik et al. 1992, 1993, 1994; Ross e/ a/. 1gg7). For example, previous research indicated that high swelling volume was negatively correlated with the firmness of cooked alkaline noodles (Ross et at. 1997) and positively correlated with the softness of 140
cooked Japanese white noodles (Crosbie 1991).ln ourresearch, FSP was not as effective
as protein content in predicting cooked noodle texture parameters (Tables 7-9 and,7-10).
Table 7-9 Conelation coefficients between protein parameters and cooked noodle texture ln constant cooking time tests
Pro SGF MPF IGF MPP SGP IGP SIG2O SIGs SIGO SIG2O SIGsP SICOP FSP Spring Wheat Cultivars (n:60) RECO 0.86+* 0.80** 0.33* 0.g7** _0.26+ _ 0.53,i* 0.96** 0.95** 0.51** 0.59** ns - ns cF 0.9 i ** 0.75+"" 0.g2** _ ns ns 0.49** 0.93** 0.93** 0.38* 0.58** ns - ns sFM 0.g l ** 0.g l ** 0.59*x 0.66x* ns ns ns 0.65** 0.J4** 0.44** 0.25* -0.29* - ns HARD 0.67** 0.70,r,* 0.62** _ ns ns 0.29* 0.59** 0.63** ns 0.33* ns - ns ADHE ns ns 0.37** ns ns 0.49*'" - -0.25* ns ns ns -0.33* sPRI 0.44^** 0.42** 0.37** 0.30* * ns ns ns 0.27* 0.3 1 0.37++ ns - -0.30* ns coHE 0.52-** 0.41** ns 0.62** 0.54** 0.66** 0.60** 0.48+* 0.63*'k ns - 0.36** GU}/f 0.72** 0.73*:* 0.68** _ ns ns 0.35** 0.66** 0.69** ns 0.40* ns - ns CHEW 0.74*r' 0.7s'ts* 0.68** ns ns - 0.31* 0.66** 0.69** ns 0.37'k+ ns - ns RESI 0.50** 0.39+* o.2g* 0.59*.4 _ _0.32* 0.49** 0.60*+ 0.55** 0.55** 0.55** ns - 0.21'+ Winter Wheat Cultivars (n:60) RECO 0.'16** 0.61** 0.73** 0.J2** ns 0.59** ns 0.71** 0.79** 0.64** 0.38** ns 0.93+* 0.64** cF 0.92** 0.93** ns 0.67-*+ 0.29* 0.gl** 0.gg** 0.75** 0.46** ns sFM 0.69** 0.59** 0.71** 0.53** ns 0.61+* ns 0.51** 0.67** 0.61** ns ns -0.31* HARD 0.76*-4 0.65+* 0.72** 0.6g* ns 0.59** ns 0.67** 0..14** 0.64** 0.27* ns ADHE ns ns ns NS ns NS NS NS NS NS NS NS NS NS SPRI 0.45** 0.43** 0.33* 0.42** ns ns ns 0.51x* 0.44** 0.32* 0.37*+ ns -0.31* NS coHE 0.50** 0.26* 0.51** 0.6g** _ 0.45** 0.54** 0.64** 0.62** 0.46** 0.60** 0.31* NS GUM 0.79** 0.64,;<* 0.75** 0.75** ns 0.61** ns 0.73** 0..79+* 0.6'/** 0.35** ns CHEV/ 0.79""¿, 0.65*^* 0.75*,k 0.75** ns 0.60** ns 0.75*x 0.79** 0.6'l** 0.39** ns RESI 0.53*,t 0.29.* 0.56** 0.67** _ 0.49** 0.46-+-+ 0.62** 0.63** 0.49** 0.52** 0.27* NS Total (z :120)
RECO 0.80x+ 0.70*'k 0.56** g1g** NS ns 0.26** 0.78*x 0.80** 0.4J*+ 0.40** ns NS cF 0.79** 0.69,r* NS 0.50** 0.g3** ns 0.33** 0.82** 0.85** 0.4J*+ O.4J*á. ns -0.1g* sFM 0.75** 0.70** 0.62** 0.61** ns 0.19* ns 0.60*x 0.71** 0.44** ns _0.20* HARD 0.69** 0.65** 0.41** 0.64** ns ns ns 0.62t* 0.67** 0.3 l** 0.25** ns ns ADHE ns ns ns ns ns 0.20* NS NS NS NS NS SPRI 0.47** 0.44** 0.36't* 0.35*x ns ns ns 0.35** 0.35** 0.25*.+ ns NS coHE 0.49** 0.33** 0.35** 0.63** ns 0.47** 0.64** 0.61** 0.43x* 0.56** NS NS GUM 0.72*:k 0.67+-* 0.45** 0.70*x ns ns ns 0.6gx* 0.72** 0.37** 0.33** NS NS CHE\M 0-74** 0.69*x 0.48** 0.71** ns ns ns 0.69*x 0.i2** 0.3g** 0.30*x 11s NS RESI 0.46** 0.30** 0.35*x 0.60** ns 0.45** 0.60** 0.58** 0.50** 0.52** ns +, ** : p NS significant at P <0.05, < 0.0 FSP: flour swelling power; other abbreviations as defined in TablesT-4 and,7-6. t4I
Table 7-10 Correlation Coefficients between Protein Parameters and Cooked Noodle Texture in the Optimum Cooking Time Test
IGF MPP SGP IGP SIG2O SIGs SIGO SIG2OP SIGsP SIGOP FSP Spríng Wheat Gultivars (n=60) REcov 0'71.. 0.66** 0.6B.. 0.60". ns ns ns o.s7-" 0.60-" 0.66.. ns -0.39. -0.54*. ns cF 0.76.. 0.67.. 0.63.* 0.7s.. -0.37.. ns 0.39.. 0.77** 0.77"- 0.59.. 0.50." ns -0.65*. ns SFM ns ns 0.30" _0.47** _0.3g". -0.37-. ns 0.64** _0.33- ns _0.48-- ns 0.30. -0.29. HARD 0.44.- 0.48.. 0.49*" ns ns ns ns ns O.2g 0.54.- ns -0.41.. ns ns ADHE -0.31. -0-26. ns -0.39"- ns 0.42.. -0.32" -0.38** -0.39"" -0.31- -0.31. ns ns ns sPRl ns ns ns -0.26- 0.34.. 0.35.. -0.36*. -0.29* -0.26" -0.26- -0.40.. -0.32. ns -0.40.- coHE 0 49* 0-41* ns 0.61-- -0.37.' -0.47*" 0.s2.. 0.59.- 0.56-- ns 0.50.. ns -0.58". ns GUM 0.56** 0.59* 0.S3.- 0.41'- ns ns ns 0.38* 0.43-. 0.58.- ns -0.42.. -0.39.* ns CHEW 0.49** 0.54-- 0.SS." 0.29. ns ns ns ns 0.31, 0.44-" ns -0.52-. -0.36.* ns RESI 0'43.. 0.35** ns 0.s4"- -0.37"* -0.37** 0.47* o.s2-* 0.46-. ns 0.46.. ns -0.50*. ns W¡nter Wheat Cultivars (n=60) REcov 0.77.. 0.69** 0.65.- 0.75.. ns 0.48* ns 0.67.. o.T1-. 0.59.. ns -0.32* -0.57.* -0.43.. cF 0.77." 0.64** 0.79.- 0.74.- -0.35-* 0.67* ns 0.77". 0.84". 0.77* 0.36." ns -0.41** -0.55t* SFM ns ns ns ns _0.2g ns ns ns ns ns ns ns ns ns HARD 0.42.. 0.40"* 0.40.- 0.39.- ns 0.30. ns 0.31* 0.39.. 0.43.. ns ns ns ns ADHE ns ns ns ns ns ns ns ns ns ns ns ns ns ns SPRI ns ns ns ns ns ns ns 0.28" ns ns 0.33- ns -0.32" ns coHE ns ns ns 0.37.- -0.43-. 0.26. 0.32- 0.37"* 0.3s-- ns 0.40.. 0.27" ns -0.30. GUM 0.50.. 0.44.- 0.48." 0.48." ns 0.37* ns 0.40** 0.48"- 0.48.. ns ns -0.27" -0.27. CHEW 0-52..0'46** 0.47* 0.s1* ns 0.34.* ns 0.44** 0.49** 0.43.- ns ns -0.34* ns RESI ns ns ns 0.26. -0.40* ns 0.2g 0.26- ns ns O.32" ns ns ns Total (n=120) REcov 0.69.. 0.62.. 0.62** 0.64". ns 0.22. ns 0.60*" 0.6s.. 0.s6.. ns _0.30-- -0.48* -0.26.. cF 0-72." 0.60-.0-65*- 0.74.. -0.33-. o.zi. 0.23.. 0.76*. o.B0*. 0.62",0.41* ns -0.47*- -0.31." sFM ns ns ns -0.23.' ns 0.26.. -0.20- -0.26* ns ns -0.26"- ns 0.25** NS HARD 0.33.. 0.32.- 0.34.. 0.27* ns ns ns 0.24". 0.32-* 0.49'- ns ns ns ns ADHE -0.24.. -0-22- ns -0.29.- ns 0.19. ns -0.2g*. -0.2g. -0.22" -0.19. 0 ns NS SPRI ns ns ns _0.23*. ns ns 0.22. ns ns _O.22" _0.23* _0.24* ns NS coHE 0.47.. 0.3s.- 0.30.. o.sz." -0.27"* ns 0.24* 0.50.. 0.45** ns 0.30** ns -0.51.* -0.19- GUM 0'46.. 0.43** 0.42** 0.41.- ns ns ns 0.32.. 0.44** 0.52-- ns ns -0.23-* NS CHEW 0.44..0.43** 0.44* 0.36* ns 0.21. ns 0.31** 0.39*.0.42". ns _0.24* -0.26* NS RESI 0.40.. 0.33.. 0.30** 0.42." -0.23** ns ns 0.4.r." 0.34*. ns 0.21. ns -0.48* *, ** : p ns significant at P <0.05, < 0.01, flour swelling power; other abbreviations as defined in Table s 7-4 and 7-k. r42
This was in agreement with the results of Baik et al. (7994a). Negative correlations of FSP with CS in both constant and optimum cooking time tests were observed, which
indicated that a high swelling power of starch gave cooked noodles a soft texture.
Relationships Between Dough Mixing Characteristics and Noodle-making euality
Significant relationships between dough mixing parameters and raw noodle processi¡g properties for spring and winter wheat cultivars together are shown in Table 7-11. All
dough mixing parameters except MDT were highly correlated with raw noodle processing parameters, including DSL, THICK, Rmax, AREA and DIST. This indicated that raw noodle processing properties were mainly determined by dough strength.
However' MDT, a parameter of dough strength in mixograph tests, had significant
cor¡elations only with DSL and THICK, because the MDT decreased with protein
content (Kovacs et al. unpvblished data).
Table 7-11 Correlation Coefficients between Dough Mixing Characteristics and Raw Noodle Processing euality
MDT ETP TEG DDT ART STA MTI FAB DSL ns _0.65,k* _0.91** _O.JJ** _0.73.+* _0.74** 0.77.+.4 _0.g1** THICK ns 0.60+* o.JJ** 0..73** 0.71** 0.71** _0.71** 0.12** Rmax -4 ns 0.41- 0.69** 0.61** 0.6g** 0.54** _0.55** 0..7g** AREA ns 0.39** 0.73** 0.95** 0.g7** 0.74** _0.55** 0]2** DIST ns 0.41** 0.69** 0.92** 0.79** o.JJ** _0.60** 0.66** *, ** : significant at P < 0.05, p : MDT mixograph dough development time; ETp : energy to peak; TEG: total (area under curve); DDT : farinograph dough developmãit : "rrrrgy : time; ART arrival tim*€, MTI mixing tolerance index; STA : stability; FÆ : farinograph absorption; othei abbreviations as defin ed in T able 7 -2. t43
Noodle cooking properties also had significant correlations with dough mixing parameters as shown in Tables 7-12 and 7-13. The OCT and CNT had significant
correlations with almost all dough mixing parameters except MDT. Significant correlations were only observed for ETp, TEG, DDT, and sTA with cL, and for TEG,
ART, and FAB with WT. Again, MDT was a poor parameter for predicting noodle-
cooking properlies.
Table 7-12 correlations of Dough Mixing characteristics with ocr and cNT
MDT ETP TEG DDT ART STA MTI FAB OCT 0.42** 0.69** 0.53,r.* 0.57** 0.41** -0.43** 0.93** CNT ns 0.49** 0.62.** 0.46** 0.49** 0.43** -0.46** 0.63** *, ** : significant at P < 0.05, p < Abbreviations as defîned in Table 7-3 and,7-11.
Table 7-13 Correlations of Dough Mixing Characteristics with cL and wr
MDT ETP DDT ART STA F'AB
CL NS _0.29'É _0.34** _0.34*x NS -0.3 1** NS NS
WT NS ns 0.32** ns 0.3 1** NS NS 0.46*x *, ** : significant at P < 0.05, p < Abbreviations as def,rned in Table 7-3 and 7-1I.
Correlations between dough mixing parameters and cooked noodle texture in constant and optimum cooking time tests are listed in Table 7-14. The results showed that dough strength was the major factor contributing to greater hardness, chewiness, COHE, and
RESI texture of cooked noodles. However, MDT had no significant comelations with almost all cooked noodle texture parameters in constant and optimum cooking time tests except for SFM in the constant cooking time test. SPRI was only significantly correlated with MTI in the optimum cooking time test. Although both CS and HARD are used as 144
parameters for cooked noodle hardness, the r-values of dough mixing parameters with CS
were higher than that with HARD, and HARD was only significantly correlated with
DDT and STA in optimum cooking time tests. The SFM was positively correlated with
dough strength in constant cooking time tests, but negatively with dough strength in
optimum cooking time tests.
Table 7-74 Conelation Coefficients between Dough Mixing Characteristics and Cooked Noodle Texture
MDT ETP TEG DDT ART STA MTI FAB Constant cooking time (ll : 120) RECOV *+ NS 0.41 0.64'4* 0.63** 0.60*,k 0.63*+ _0.61** 0.70** CF NS 0.49'** 0.14** 0.70** 0.69** 0.70** -0.65** 0.71** SFM -0. 1g* 0.22'+ 0.55** 0.59*,k 0.59** 0.53 ** _0.39** 0.52**
HARD NS 0.31** 0.57** 0.49** 0.53** 0.47** _0.49** 0.62** ADHE NS -0.19* NS NS NS NS 0.24** ns SPzu NS NS 0.31** 0.33** 0.39** 0.21** ns 0.36**
COHE NS 0.36*'¡. 0.37** 0.39** 0.34** 0.49** -0.59 ** 0.47**
GUM NS ** 0.35 0.59** 0.52** 0.55** 0.52'+* -0.54** 0.66**
CHEW NS 0.34** 0.60** 0.54** 0.57** 0.52** _0.53** 0.6Jo;*
RESI NS 0.3 1** 0.29** 0.35** 0.30*x 0.46+* _0.59** 0.41*-4 Optimum cooking time (z:120) RECOV NS 0.27'+* 0.42** 0.51** 0.44+* 0.52*+ _0.43** 0.46*-4 CF 11s 0.36** 0.52** 0.63** 0.5g** 0.64** -0.58** 0.52**
SFM NS _0.32'"* _0.31,k* ns -0.19* NS 0.20* -0.36**
HARD NS ns ns 0.19+ NS 0.22* NS NS
ADHE NS -0.26+* -0.30* _0.22* _0.22-+ -0.21* 0.21* -0.30**
SPRI NS NS NS NS NS NS 0.21* ns
COHE NS 0.34** 0.47** 0.42*.* 0.39** 0.39x* _0.43** 0.64*x
GUM NS ns 0.25** 0.30** 0.27*.+ 0.31** -0.25** 0.29**
CHEW NS ns 0.22* 0.30** 0.29,k* 0.29** ns 0.29**
RESI NS 0.25** 0.39** 0.32x* 0.3 I ** 0.29** _0.32** 0.56** Abbreviations as defined in Table 7-4 andl-].]r. r4s
CONCLUSIONS
Protein content is an important factor in determining fresh noodle processing quality,
cooking quality and cooked noodle texture. The gluten strength of a cultivar also influences the noodle making quality. Although the strong correlations of monomeric protein, soluble glutenin and insoluble glutenin with total protein content prevents establishing which one of the three is more important for noodle making quality, the r- values of IGF with noodle making quality parameters were equal to or higher than those with SGF and MPF, particularly for COHE and RESL Protein and protein fraction contents were better than FSP in predicting cooked noodle quality in the test samples.
Dough strength parameters from mixograph and farinograph tests were highly correlated with most noodle making quality parameters, but this was not the case with MDT in the test samples- Furthermore, the small-scale test, SIG, which is based on glutenin content and quality and reflects gluten strength, is a satisfactory predictor for most noodle making quality parameters. t46
CHAPTER 8
A Comparative Study on the Noodle Making Quality of wheat Flours from canada and China
ABSTRACT
Using selected Chinese and Canadian wheat varieties, protein parameters and functional
measurements were studied in relation to Chinese white noodle quality. protein
composition in the Chinese flours was significantly different from that in the Canadian
flours. The Chinese wheat varieties were characterized. by shorter stability and lower gluten index in comparison with the Canadian varieties. The weaker gluten strength of the Chinese flours was also observed from values of the swelling index of glutenin. Higher RVA peak viscosity was found in the Chinese flours compared to their Canadian
counterparts. Noodles from Canadian wheat varieties, except for Fielder, had a stronger
dough sheet and harder cooked noodle texture with lower cooking loss and longer cooking time which differentiated them from the Chinese varieties. The statistical data based on all Chinese and Canadian flours showed that protein content and quality were significantly correlated with raw noodle processing quality, cooking quality, and cooked noodle texture. Most noodle making quality parameters were more highly and meaningfully correlated with insoluble glutenin content than they were with protein content. The results suggest that both protein content and quality should be considered in evaluating suitability of flours for making white noodles. Cooked noodle hardness and chewiness were positively correlated with dough strength parameters determined by the farinograph, but they were negatively correlated with high pasting properties, such as
RVA peak viscosity and flour swelling power. 147
INTRODUCTION
One of the major applications of wheat in China is the production of white noodles (Ding
and Zheng 1991)' Based on the regional differences in preference, noodles can be classified into two major types: white and alkaline. White noodles are more popular in
china than alkaline noodles, particularly in the northern part of china.
Several tesearchers have examined the requirements of traditional Chinese food using
wheat from different countries (Huang and Morrison 1988; Lukow et al. 1990;Blacket a|.2000, Zhu et a|.2001).It was reported that Chinese wheat varieties had higher protein content, but lower SDS sedimentation volume than British wheat varieties (Huang and Morrison 1988). Compared to Canadian wheat flours, flours from the Chinese varieties
had short mixograph dough development time, and short farinograph stability (Lukow er
1990). al' Wheat samples from China had lower dough strength even at a higher protein
levels than wheat from Australia (Zhu et at.200L). The weak dough strength of Chinese varieties is due to the high percentage of lB/lR translocation wheat, which has good agronomic properties but is associated with poor dough properties (Zhu et at.200l).
China is a wheat importer and Canada has been one of the major countries exporting wheat to China. Therefore, it would be of interest to know the differences between
Canadian and Chinese wheat varieties in their end-use quality. This type of information would be useful in wheat breeding programs for breeders in targeting improved end-use quality, and to provide guidelines for both import and export markets. Hovrever, there is 148
no report which compares the noodle making quality of Canadian and Chinese wheat
varieties. The present study was conducted to determine the comparative compositio' and
functional properties of representative Chinese and Canadian wheat varieties. ln terms of
end-use, the research focused on the desired quality characteristics of white noodìes.
Results were also examined by statistical correlation to determine which tests could be
used to predict white noodle quality.
MATERIALS AND METHODS
Samples
Twenty-seven varieties of wheat flour were obtained from Canada and China. Canadian
flour with 650/o extraction were from fwo canada western Red Spring (cwRS) cultivars
(AC Domain and Neepawa), one Canada Vy'estern Soft White Spring (CWSWS)
(Fielder), eight canada Prairie spring (cps) (Biggar, Genesis, Hy367,AC Karma, Alpha
16, HY616, HY627, and AC Vista), one Canada Western Extra Strong Red Spring
(CWES) (Glenlea), and one unknown class of breeding line (90W950). The growth and
milling conditions for Canadian wheats were described in Chapter 3. Twelve Chinese
varieties represented the main varieties in one of the major wheat production provinces,
Henan, and were obtained from Henan Seed Company, China. Their straight flours
(extraction rate about 75o/o) were milled by a Buhler laboratory mill in Zhengzho' Grain
College, Henan, China. Two commercial flours, one with flour extraction rate of 50-60% used for Japanese white noodle (Udon) and one with aroun d, 75% extraction for Chinese white noodle (Lamian) production were obtained from Nippon Flour Mills Co. Ltd., 149
Atsugi, Japan, and Xinjiang Flour Mills, urumqi, china, respectively.
Protein Parameter and Analyses
The protein content of flour was determined by AACC Method 46-lI (AACC 2000).
Protein fraction composition was determined by the sequential extraction procedure as
described in Chapter 3. Three protein fractions were obtained from this procedure,
monomeric protein, soluble glutenin, and insoluble glutenin. Protein fractions were
expressed as the percentage of protein fraction in flour (termed the absolute amount) and
percentage the of protein fraction in total protein (termed the relative amount). The
swelling index of glutenin (SIG) was determined at three swelling times 0, 5, and 20 min,
expressed as SIGO, SIG5, and SIG20, respectively, and their relative values (percentage
of SIG value in flour protein) were described as SIGOP, SIGsP, and SIG2Op, respectively
('Wang and Kovacs 2002a).
Functional Measurements
Farinogram values were derived from a 20-min mixing curve for l0 g of flour (I4% moisture content basis) and sufficient distilled water to give a maximum do,gh cotrsistency centered on the 500 BU line according to AACC method 54-2I (AACC
2000). Gluten index was determined according to AACC Method 3B-IZ (AACC 2000).
Flour Swelling Power and Pasting properties 1s0
Flour swelling power was determined by the method of Fu et al. (1998b). The pasting
properties of flours were measured with a Rapid Visco Analyser (RVA, Newporl
Scientific Pty. Ltd., warriewood, Australia). Flour, 3.5 gea% m.c.), was dispersed in
25.0 mL of distilled water. The suspension was heated at 15"C/min from 50 to 95o C,
held at 95' c for 10 min, and cooled from 95oc to 50"c for 11 min.
Noodle Making Procedure and euality Measurement
Noodles were made according to the procedure described in Chapter 7. Noodle making
quality parameters were determined by the methods described in Chapter 7.
Statistical Analyses
General linear models used for all statistical analyses were fi'om the SAS 8.0 statistical
analysis software program package (version 8.0, SAS Institute, Cary, NC). M¡ltiple
comparisons were made using Fisher's least significant difference (LSD) test, and CORR
(correlation analysis) was used for comparative analyses of paired data sets.
RESULTS AND DISCUSSION
Protein Parameters
Protein parameters of Chinese and Canadian wheat flours are presented in Appendix I 151
Table 31. The protein content ranged from 8.30 to 12.38o/o for Chinese wheat flours and
10.17 to 14.270/" for Canadian wheat flours. ln terms of protein composition, the absolute
content of monomeric protein (MPF) was between 4.45 to 6.810/o for Chinese wheat
flours and between 5.31 to 7.94o/o for Canadian flours. The relative amount of monomeric
protein (MPP) varied between 39.43 to 58.29o/o for Chinese flours, and between 49.0g to
64.80% for Canadian flours, contributing the higliest proportion of total protein. The
absolute (SGF) and relative (SGP) contents of soluble glutenin contributed the least to the
total protein extracted, varying between 0.72 and 1.80% and 6.62 and l7.7oo/o,
respectively, for Canadian flours, and changing between 0.99 and 2.71% and 10.26 and
23.840Á, respectivelY, for Chinese flours. The absolute amounts (IGF) and the relative
amounts (IGP) of insoluble glutenin ranged from 1.80 to 4.00% and 19.40 ro 32.31(%, respectively, for chinese flours, and from r.j6 to 3.95% and, 17.31 to 3l.5zoÁ,
respectively for Canadian flours.
In a comparison of protein content and protein fractions between Chinese and CpS
wheats in this study (Table 8-1), it was found that Udon flour had the lower protein
content' CPS and Chinese varieties were similar in protein content. The order of MpF among wheat classes was similar to that of protein content. Chinese varieties were higher on average in SGF compared to CPS varieties. There was no significant difference of IGF between Chinese and CPS wheats. As expected, the highest IGF was observed from
CWESRS variety Glenlea, and the lowest from CWSWS variety Fielder (Appendix I
Table 31). in Chinese varieties, only Wen 2540 was fully equal or superior to CWESRS variety Glenlea ìn IGF. On the other hand, CPS varieties had higher Mpp than Chinese 152
wheat varieties (Table 8-1). Chinese flours contained a higher proporlion of soluble
glutenin than did CPS wheat. There was no significant difference of IGp between CpS
and Chinese wheat varieties. It was interesting that the iGP in Udon flour was higher than
that in Chinese and CPS wheat, and actually as high as that in the extra strong variety
Glenlea (Appendix I Table 31). It has long been known that IGP is a parameter of gluten
strength (Orth and Bushuk 1972). Although Udon flour contained the lowest level of
protein, it possessed extremely strong gluten based on the IGP. According to the miller,
the Udon flour had been milled from Australian blended wheats. Previous studies also
obserued the high proportion of insoluble glutenin of pure Australian Standard White
wheat with quite low levels of protein (Nemeth et al. lgg4), as well as higher SDS
sedimentation volume and GI for Udon noodle flours (Jun et ctl. 1998). This indicated
that Udon flour is not only specifìc in starch composition and pasting properties, but also
unique in protein content and quality. For other low protein content varieties, the IGp was
obviously lower than that of Udon flour, such as that in Fielder, Nongda 010, yumai 1g,
Linfeng 7203, and Alpha 16 (Appendix I Table 31).
SIG20, SIG5, SIGO, and their relative values (percentage of SiG value in total protein) are listed in Appendix I Table 31. SIG20 ranged from 3.46 to 5.37 for Chinese flour and from3-26 to 6.38 for Canadian flour. SIG5 ranged from 3.51 to 5.33 for Chinese flour and from 3.73 to 6.02 for Canadian flour. SIGO ranged from 2.87 to 4.50 for Chinese flour and from 3.89 to 5.25 for Canadian flour. Overall, SIGO had the smallest values compared to SiG20 and SIG5. Canadian variety Fielder had the smallest SIG20 value, 153
Table 8-1 Variationsl of Plotein Palametels2 of Chinese and Canadian Wheat Classes
Pro MPF SGF IGF' MPP SGP IGP SIG2O SIG5 SIGO SIG2OP SIG5P SIGOP Chinese 10.83a 5.52a 1.68a 2.72a 51.04b 15.50a 25.06b 4.t5b 4.29ab 3.5 5b 38.50b 39.90b 33.06b CPS l0.l8a 6.02a 1.37a 2.72a 55.85a 12.14b 25.12b 4.16a 5.0 1a 4.68a 44.l3ab 46.41a 43.42a UDON 8.87b 4.39b 1.19a 2.15a 49.58b 13.47 ab 31.13a 4.30ab 4.31ab 3.45b 48.59a 48.70a 3 8.93 ab Lamian 10.43a 5.24a 1.47a 2.90a 50.24b 14.09ab 27.80ab 4.06b 4.17b 3.94ab 3 8.8 8b 39.93b 31.73ab 'Mea,'sirrtlresamecolunrnandlreadin.gfollowedbythesarne1etterarenotstatistical1ydiffer.errt("@ test. tPro : protein content; MPF and MPP : the percentage of monomeric protein in flour and protein, lespectively; sGF a'd SGp : the percentage of soluble glutenin in flour and protein, respectively; IGF and IGp : the pelcentage of i'soluble gluteni' in flour and protein, respectively; SIG20 and SIG2OP : .*riling ino.* óf glutenin with 20 min swelling tirne in flour (g/g) and in protein (%), respectively; sIG5 and SIG5P : swelling index of glutenii with 5 min swelling time in flour (g/g) and i' protein (%), respectively; SIGO and SIGOP : swelling index of glutenin with 0 min swelling timJin flour (g/g) and in protei' (%), respectively; Chinese : Chinese variety; CPS : Canada Prairie Spring wheat variety; CryESRS : Canada Western Extra Red Spring wheat variety; CWRS : !-ï9ng Canada Western Red Spring wheat variety; CwSwS : Canada westem White Spring wheat variety. soft 154
while Chinese vanety Nongda 010 had the smallest values of SIG5 and SIGO. The
highest values of SIG20 and SIGO were observed from Canadian variety Glenlea, but the
highest SIG5 value was from AC Domain. The differences in SIG values from different
swelling time were due to the different swelling properties of glutenin (Wang and Kovacs
2002a). As with IGP, the SIG2OP of Udon flour was similar to that of Glenlea. Although
Chinese variety Wen2540 had similar levels of insoluble glutenin content with Glenlea,
Wen 2540 had quite low levels of SIG values compared to that of Glenlea. Therefore,
SIG values were not only based on insoluble glutenin content, but also on the swelling
properties of insoluble glutenin (Wang and Kovacs2002a).
Further comparison of CPS and Chinese wheat flours (Table 8-1) showed that the SIG20
average of Chinese varieties was lower than that of CPS, indicating its weaker gluten
strength. The lower values of SIG5, SIGO, SIG2OP, SIGsP, and SIG0p were obserued in
Chinese flour compared to CPS wheat flours. Udon flour exhibited the highest SIG29p,
similar to that of CWESRS variety Glenlea (Appendex I Table 31). Compared ro CWSWS variety Fielder, Udon flour had high SIG20 but not high enough ro be
considered as "strong" wheat flour. However, when SIG2OP was compared, Udon flour
had a very high SIG2OP value which was similar to that of Glenlea and, therefore, gluten
in Udon flour could be considered to be ,'strong".
Dough Strength Parameters
Farinograph parameters and gluten index (GI) for Canadian and Chinese wheat flours are listed in Appendix I Table 32. Water absorption values of the Chinese and Canadian 155
wheat flours were 57.5-63.1% and 57.3-64.4%o, respectively; dough development times
(DDT) were 1.7-9.5 and2.1-15.6 min, respectively; mixing tolerance index (MTI) values
wete 20-95 and 0-142 BU, respectively; and stability values were 1 .Z-IO.B and,3.2-17.7
min, respectively. It was not possible to differentiate between CPS and Chinese wheat
varieties with the farinograph DDT (Table 8-2). However, the Chinese flours exhibited
the lower STA, while CPS and Udon flours had obviously highest STA. Therefore, CpS
wheat varieties exhibited stronger dough properties. The results from STA demonstrated
that Chinese flours could be classif,ied as "weak" gluten flours. This was in agreement
with other studies (Huang and Morrison 1988; Zhu et at.2001). Based on DTT, Udon
flour exhibited as a weak wheat class, similar to the CWSWS variety Fielder (Appendix I
Table 32). However, based on STA and MTI, Udon flour exhibited mixing characteristics
of a very strong wheat class, similar to the CWES variety Glenlea. No other varieties in
this study had the unique property of quick dough development and high tolerance to mixing.
Flour Pasting Properties
Flour pasting properties are shown in Appendix I Table 32. Two parameters of the pasting properties of flour were measured by RVA. Peak viscosity (pV) range was 239-
287 and 131-322 RVA units (RVU) for Chinese and Canadian flours, respectively.
Breakdown (BKD) range was 89-116 and 68-192 RVU for Chinese and Canadian flours, respectively. Flour swelling power (FSP) had ranges of 6.48-10.02 and 6.98-1 I.l4 g/gfor
Chinese and Canadian flours, respectively. PV and FSP are commonly used to evaluate 1s6
starch pasting properties for noodle rnaking quality. Chinese varieties had higher pV than
that of CPS and CWRS, but CPS flours had higher FSP than that of Chinese flours (Table
8-2)' Udon flour had significantly higher PV and FSP values compared to Chinese and
CPS flours, mainly due to its starch content and composition, and therefore is particularly
suitable for Japanese white noodles (Oda et at. 1980; Crosbie 1991; Konik et ctl. 1993;
Yvn et al.1996).
Table 8-2 Variationsr of Flour Quality Parameters2 of Chinese and Canadian Wheat Classes
Farinograph RVA FSP DDT STA
Chinese 4.3a 3.9b 7.89b 263ab
CPS 5.3a 14.0a 8.43ab 235b
Udon 2.rb 18.5a 10.49a 280a
Lamian 1.80b 4.\b 8.20ab 231b 'Means in the same column and h"uding y different (P > 0.05) by / multiple range 2DDT : ^develJpmenttest. farinograph dough time (min); srA : srability (min); MTI : mixing tolerance index (BU); GI : gluten index (%); pv : RVA peak viscosity (RVA unit (RVU)); FSP: flour swelling power (dÐ; Other abbreviationi as defined in Table 8-1.
Noodle Making Quality
Noodle making quality generally includes noodle colour, raw noodle processing, cooking, and cooked noodle quality. Because flour extraction significantly influences noodle colour and flours from Chinese and Canadian wheats had different extractions, comparison of noodle colour between Chinese and Canadian wheats were impossible. On 157
the other hand, the differences in cooked noodle texture between different flour
extraction rates within the same class were not statistically significant (Oh et ctl. I9g5b;
Lee et al. 1987; Kruger et al. 1994). Therefore, the effect of different flour extractions on
cooked noodle texture was ignored.
Appendix I Table 33 presents the processing quality parameters of each variety, including
noodle sheet length (DSL), thickness (THICK), maximum extension resistance (Rmax),
extension area (AREA), and extension distance (DIST). DSL ranged from 47.g0 to 63.g0
and from 58.50 to 65.50 cm for Canadian and Chinese flours, respectively. The ranges of
THICK were from 1.19 to 1.52 and from 1.i0 to 1.36 mm for Canadian and Chinese
flours, respectively. The ranges of DSL and THICK for noodles from Chinese flours
were smaller than those from Canadian flours. The range of maximum extension
resistance (Rmax) for all varieties was 61.55-ll4.7O g, and Linfeng 7203 and,Wen2540
had the lowest and the highest Rmax, respectively. Extension area (AREA) ranged from
69'34-1779.88 g' s, and Linfeng 7203 and, Glenlea had the lowest and highest values,
respectively. The range of extension distance (DIST) was 7.34-70.72 mm, with extremes
corresponding to Linfeng 7203 and Glenlea, respectively. Among the varieties evaluated, significant differences in raw noodle processing quality were seen between Chinese and
CPS wheat (Table 8-3). Noodles from Chinese flour had higher DSL than did the noodles from CPS flours' ln contrast, low DSL noodles were observed to have higher dough sheet thickness. Chinese flours exhibited similar Rmax to their CpS and CWRS counterparts.
Noodles from CPS flours had higher AREA and DIST than those from Chinese flours.
However, there were no significant differences of AREA and DIST among Chinese and 1s8
two standard commercial flours. Therefore, it is suspected that flours with strong gluten
are correlated with low DSL, thicker dough sheet, higher AREA and DIST, but not with
Rmax. Noodles from commercial Chinese white noodle flour (Lamian) had similar
AREA and DIST to noodles from commercial Japanese white noodle flour (Udon).
However, Lamian noodles had slightly shorter DSL and higher Rmax than did the Udon
noodles.
Table 8-3 Variationsl of Raw Noodle Processing Quality Parameters' fo. Chinese and Canadian Wheat Classes
CLASS DSL THICK Rmax AREA DIST Chinese 63.29a 1.19b 94.t9b 25291b 13.84b CPS 56.68c 1.34a 87.46b 136.44a 35.95a Udon 60.50ab 7.24a 92.79b 238.20b l 3.83b Lamian 59.15bc 1.22b 113.27 a 160.12b 8.78b 'Means in the same column and heading f" different (P > 0.05) by I multiple range test. 2DSL : dongh sheet length (cm); THICK : dough sheet thickness (mm); Rmax : extension maximum resistance (g); AREA : extension aÍea (g); DIST : extension distance (mm); Other abbreviations as defined in Table g-1.
Noodle cooking quality parameters are shown in Appendix I Table 34, including cooking
loss (cL), water uptake (wr), optimum cooking time (ocr), and cooked noodle thickness (CNT). The range of CL was 6.62 to lO.02oÁ, with extremes corresponding to
CWRS variety Neepawa and Chinese variety Yumai 18, respectively. The WT had a range 2.55-2.94 of glg, with extremes conesponding to Chinese variety Wenmai 4 and,
CWSWS variety Fielder, respectively. The longest OCT (7i0 s) was obtained from
CWRS variety AC Domain and the shortest was obtained from Chinese variety Nongda
010 (250 s). The range of CNT was 1.7I-2.16 mm, with extremes conesponding to
Chinese variety Yumai 10 and CWESRS variety Glenlea. A wider range of CNT from 1s9
Canadian flours (1.85-2.16 mm) was observed, compared to the smaller range of CNT from Chinese flours (1.71-1.76 mm). This indicated that noodles from Chinese flours had
more uniform final product size, despite their wide range of protein parameters and
dough strength. There are no significant differences between noodles from Chinese and
cPS flours in cL, wT, and ocr (Table 8-4). Noodres from cpS, however, had higher
CNT than noodles from Chinese flours. Differences between Lamian and Udon noodles
were significant only for WT.
Table 8-4 Variationr of Noodle Cooking Quality Parameters2 of Chinese and Canadian Wheat Classes
CLASS CL Chinese 8.07a 2.73b 401a 1.73c CPS 7.27a 2.72b 456a 2.04a Udon 8.04a 2.91a 390a 1.86b Lamian 7.88a 2.tlh 410a 1.91b 'Means in the same column and heading follow different (P > : 0.05) by / multiple'wr range test. '9t cooking loss (%); : water uptake (gg); ocr : optimum cooking time (s); CNT: cooked noodle thickness (mm); Other abbreviations as defined in Table g-1.
Cooked noodles from different wheat sources were assessed for various textural
attributes. Maximum cutting stress (CS), compression recovery (RECOV), surface fitmness (SFM) (Oh et al. 1983, 1985a), and texture profile analysis (TpA) were measured af both constant and optimum cooking times. Cooked noodle texture parameters in the constant cooking time test are listed in Appendix I Table 35. The ranges were 42.90-62.58% for RECov; 13.76-2g.56 glmmz for cs; 122.94-237.95 g/mm for
SFM; 1328.21-2337.20 g for HARD; -23.61 to -216.66 g. s for ADHE; 0.89_1.05 for sPRI; 0.60-0.73 for coHE; lr27.02-1633.02 g for GIrM;9r0.87-t6t4.09 gfor GHEW; 160
and 0.31-0.38 for RESL
Overall comparisons of Chinese and CPS wheat are shown in Table 8-5. Both HARD
from TPA (Baik et al. 1994a) and CS (Oh et al. 1983) from cutting tests were used to
evaluate the hardness of cooked noodles. The decreasing order of cooked noodle CS was: > CPS Chinese : Udon : Lamian. This order corresponded to the order of protein content
and gluten strength. However, cooked noodle hardness determined with TpA had a
different decreasing order: Chinese : Lamian : CPS > Udon, and this order could not be
explained by protein content and gluten strength. Both commercial flours produced
noodles with CS similar to that from Chinese flours. Noodles from Lamian had similar
HARD as from Chinese flour, but Udon noodles had a softer texture according to HARD.
This indicated that there were differences between CS and HARD. Cooked noodle
chewiness was also measured by both RECOV and CHEW (Oh et at. 19g3; Balk et al.
1994a). The RECOV of cooked noodles from Chinese flours was significantly higher
than that of noodles from CPS wheat, but the CHEW of cooked noodles from CpS flours
was higher than that from Chinese flours. Lamian noodles had significantly higher
chewiness than that of Udon noodles. The SFM of cooked noodles among different flours
had a decreasing order of CPS > > Chinese > Lamian : lJdon, and noodles from the two
commercial flours had the smallest values of SFM. Noodles from Chinese flours had the lowest ADHE compared to that of CPS flours. There were no significant differences of
SPRI between Chinese and CPS, and only Lamian noodles had higher Spzu. Lower
COHE and RESI of Chinese flour noodles were observed compared to noodles from CpS flours. 161
Tabte 8-5 Variationsr of cooked Noodle Texture Parameters2 in constant cooking Time for Chinese and Canadian Wheat Classes
CLASS RECOV CS SFM HARD ADHE SPRI COHE GUM CHEW RESI Chinese 54.60a 17.52b 144.35b 2024.96a -57.22.a 0.91b 0.65b 1317.94bc 128 I .65bc 0.34b 45.61b CPS 23.50a 193.30a 1955.2ia -16i.14c 0.95b 0.71a 1395.12ab 1325.18b 0.36a Udon 45.88b l9.3lb 81.76c 1642.38b -126.56bc 0.95b 0.72a I 188.59c 1129.56c 0.31a Lamian 56.56a 19.51b 90.15c 2070.82a -95.92ab 1.05a 0.7la 1467.24a 1535.71a 0.38a Means in the same collrmn and heading followã by the same letter are not statistically different (P > 0.05) by r multiple range 2Rgcov : tesr. recovery (%); cs : iraximum cutting stress (d*-t); SFM : surface firmness (g/s); HARD : TPA hardness (g); ADHE : TpA adhesiveness (g.s); SpRI : TPA springiness; coHE : TPA cohesiveness; GlrM : TpA gumminess (g); GHEW: TPA chewiness (g);RESI: TPA resilience; other abbreviations as defined in lable 8-1.
Cooked noodle texture parameters in the optimum cooking time test are listed in
Appendix i rable 36. The ranges were 49.65-64.94% for RECov; 13.18-2g.00 g/^^,
for cS; 91.72-148.15 g/mm for 1838.04-27g7.80 sFM; g for HARD; -42.80 to -209.g2
g' s for ADHE; 0.94-r.18 for SPRI, 0.65-0.75 for coHE; t2o2.g1-tgg4.45 g for GlrM;
1134.46-1984.24 g for CHEW; and 0.28-0.42 for RESI.
Overall comparisons of Chinese and CPS wheat are shown in Table 8-6. In optimum
cooking time test, the differences of cooked noodle CS between CPS and Chinese flour
were not significant, but noodles from Chinese flours had higher HARD than noodles
from CPS flours. Two commercial flours produced noodles with the same hardness when
the hardness was evaluated by CS, but the harder texture of Lamian noodles was
observed using HARD. Cooked noodle chewiness based on RECOV and CHEV/ had no
significant differences between CPS and Chinese flours. Noodles from Lamian flour had
higher chewiness than that from Udon flours. Different orders of hardness and chewiness
between two cooking time tests indicated that cooking time had a significant effect on r62
cooked noodle texture parameters. Cooked noodle SFM for Chinese flours was
significantly higher than that of noodles from CPS flours. Noodles from the two
commercial flours still had the lower level of SFM, while Udon noodles had a higher
SFM than did Lamian noodles. The increasing order of ADHE was: Lamian < Chinese < : CPS Udon. Similar to the result from the constant cooking time test, noodles from
Lamian flour had lower ADHE than that from Udon flour. There was no signif,rcant
difference of SPRI Chinese and CPS flours and between two commercial flours.
Compared to CPS flours, Chinese wheat flours produced noodles with obviously low
levels of COHE and RESI.
Table 8-6 Variations of Cooked Noodle Texture Parameters in Optimum Cooking Time for Chinese and Canadian Wheat Classes
CLASS RECOV CS SFM HARD ADHE SPRI COHE GUM CHEW RESI Chinese 56.10a 19.66a 129.85a 2211.31a -110.91ab 0.98a 0.68b 1508.96a 1474.14b 0.34b CPS 55.37a 21.12a 114.05ab 2009.82ab -149.42b 1.00a 0.7lab 1496.20a I s33.1 0b 0.35ab Udon 50.53b 19.82a 98.5lbc 1838.04c -152.62b 0.98a 0.74a 1351.16a 1319.17 a 0.38a Lamian 19.59a 56.54a 91.12c 2018.80ab -66.81a 0.96a 0.70b 1406.25a 1350.65a 0.38a Abbreviations as defined in Table 8-5.
Relationships Between Protein Parameters And Noodle Making euality
Relationships between protein parameters and raw noodle processing quality are listed in
Table 8-7. Protein content was significantly correlated with DSL, THICK, AREA, and
DIST. MPF was only significantly correlated with AREA and DST, while SGF was not signif,rcantly correlated with any raw noodle processing quality parameters. IGF was significantly correlated with all processing parameters, and in general, its correlation coefficients were higher than those of protein content to DSL, THICK, and AREA.
Therefore, protein quality defined as insoluble glutenin was a better potential predictor of 163
raw noodle processing quality than protein content. There were no significant correlations
of SGP and MPP with raw noodle processing quality parameters. Because SIG values
were based on glutenin content and quality (Wang and Kovac s 2002a), SIG values
(including SIG20, SIG5, and SIGO) were also related to raw noodle processing quality
parameters. The correlation coefficients were higher than those with IGF and IGp, except
for Rmax. The r-values with SiG in flour were higher than that with SIG in protein.
Generally, the data indicated that flours with high insoluble glutenin content and strong gluten type produced fresh noodles with low DSL, high THICK, Rmax, AREA, and
DIST.
Noodle cooking loss (CL) was negatively correlated with protein content, MpF, IGF, and
SIG values (Table 8-8), while the highest r-value was observed with protein content. The
WT was also negatively correlated with protein content, IGF, IGP, and SIG values, while
the IGF had the highest r-value. Similarly, the OCT was predicted best by IGF. Although
fresh noodle thickness was related to cNT (r : 0.g5, p < 0.01), protein content and
absolute amounts of protein fractions had no signif,rcant correlation with CNT, and only
SGP was negatively related to cNT. sIGO was better than sIG20 and sIG5 in predicting
CNT, and CNT was mainly affected by glutenin content and quality, since SIGg reflected both the content and the quality of glutenin (wang and Kovac s 2002a).
The correlation coefficients of protein parameters and cooked noodle texture parameters in both constant and optimum cooking time tests are listed in Table 8-9. Variation between the two cooking time tests in the level and the signifìcance of the correlation 164
Table 8-7 correlation coefficients between protein parameters and Noodle Making Quality
DSL THICK Rmax AREA DIST Pro -0.46x 0.45* NS 0.60** 0.59** MPF NS NS NS 0.50'k* 0.52+* SGF NS NS NS ns NS IGF -0.65** 0.63+* 0.55*+ 0.63 ** 0.57+* MPP NS NS ns NS NS SGP NS NS NS NS NS IGP -0.55** 0.53** 0.51*'+ 0.39* NS SIG2O -0.99*+ 0.82*'k NS 0.96** 0.93 *+ SIG5 -0.99'k* 0.85+* NS 0.96** 0.84** SIGO *'* -0.99** 0.gg NS 0.81*'r' 0.82** SIG2OP -0.'7 5** 0.67** NS 0.58** 0.55** SIG5P ** -0.63 0.59+* NS 0.46+ 0.45'+ SIGOP -0.63** 0.64*+ NS 0.45* x ** : 0.47'+ and significance at P < 0.05 and p. O¡f , ,"rp""tiu"ly; ns : not significant (n : 27). Abbreviations as defined in Table 8-1 and Table g-3.
Table 8-8 correlation coefnicients between protein parameters and Noodle Cooking Quality
CL WT OCT CNT Pro -0.67** -0.55*'k 0.65** NS MPF -0.60x* NS 0.42* NS SGF NS NS NS NS IGF -0.59** -0.72** 0.76** NS MPP NS NS NS NS SGP NS nS NS -0.45 * IGP ns -0.54** 0.52** NS SIG2O -0.63** -0.67** 0.79** 0.62** ** SIG5 -0.63 -0.62** 0.77** 0.6J** SIGO -0.65** -0.45* 0.71** 0.83** SIG2OP NS NS 0.43x 0.61** SIG5P NS NS NS 0.59** SIGOP * ** : NS NS ns 0.74** and significance at P < 0.05 and P < 0.01, respectively; ns : not significant (n : 27). Abbreviations as defined in Table 8-1 and Table 8-4.
results was obvious in the Table 8-9. This variation reflected the effects of cooking time on cooked noodle texture. cooked noodle cS had positive corelations with protein content, IGF, IGP, and SIG values in both constant and optimum cooking time tests, 165
while IGF had better r-values than did the protein content. The highest r-value with CS
was observed from SIGO in the constant cooking time test, and from SIG20 and SIG5 in
the optimum cooking time test. Cooked noodle HARD was significantly correlated with
protein content, iGF, IGP, SiG20, and SIG5 in the constant cooking time test, but only
with protein content in the optimum cooking time test. The r-values for relationship
between protein fraction content and cooked noodle hardness were substantially higher in
the constant cooking time test compared with those from the optimum cooking time test.
In the constant cooking time test, protein content was significantly correlated with
CHEW, but not with RECOV (Table 8-9). However, in the optimum cooking time test, protein content was significantly correlated with both RECOV and CHEW, while the
higher r-value was observed between protein content and RECOV. The IGF was superior
to other protein fraction contents in predicting CHEW in the constant cooking time test,
but this was not true in the optimum cooking time test. Similarly, SIG values were only
significantly corelated with CHEW in the constant cooking time test, but not in the
optimum cooking time test. The RECOV in the constant cooking time test was
significantly correlated with soluble glutenin content and MPP. Because SIGO reflects both soluble and glutenin content and quality (Wang and Kovacs2002a), and only SIGg was related to RECOV in constant cooking time, the RECOV was determined by both soluble and insoluble glutenin contents and quality.
These results are generally consistent with previous findings by others workers. Oh et al.
(1985b) compared the properties of white noodles made from wheat with a wide range of 166
protein content. High protein flours gave noodles with higher cutting stress, and lower
surface firmness. Balk et al. (I994a) found high correlations of the chewiness of cooked
noodles to protein content and more linearly to sodium dodecyl sulfate (SDS)
Table 8-9 Correlation Coefnicients between Protein Parameters and Cooked Noodle Quality in Constant and Optimum Cooking Time Tests
RECOV CS SF'M HARD ADHE SPRI COHE GUM CHE}V RESI Constant Cooking Time Pro ns 0.50*+ 0.5-/** 0.45* ns ns NS 0.53** 0.51** ns MPF ns 0.43 * 0.64** NS NS NS NS NS NS NS SGF 0.58':'* ns ns NS NS NS NS NS NS NS IGF ns 0.39* 0.57** 0.54** ns ns NS 0.660.* 0.66,¡.* ns MPP -0.59*'k ns ns ns ns -0.40* NS ns ns ns SGP 0.56*+ -0.3 8 * ns ns 0.39* ns NS NS NS NS IGP ns ns 0.40+ 0.39* ns ns NS 0.50** 0.52-+ ns SIG2O ns 0.63** 0.83** 0.45* -0.39+ ns 0.49* 0.68** 0.64** ns SIG5 ns 0.87** 0.69** 0.45* -0.45+ ns 0.55** 0.71** 0.65** 0.40* SIGO _0.42* 0.92*-4 0..73** ns -0.66** ns 0.74** 0.59** 0.51** 0.56** SIG2OP ns 0.64*+ ns ns -0.42* ns 0.49* 0.41* 0.36 0.40* SIG5P ns 0.57*'t ns ns -0.44* ns 0.50** ns ns 0.44* SIGOP _0.50** 0.64** 0.39,k ns -0.66** ns 0.69** ns lìs 0.58** Optimum Cooking Time Pro 0.73+* ns 0.69+* 0.41* ns ns 0.42* 0.53** 0.46* ns MPF 0.46+ ns ns NS NS NS NS NS NS lls SGF 0.43* 0.39* ns NS NS NS NS 0.39* ns NS IGF 0.64** 0.79'k'k ns NS NS NS 0.50** 0.41-+ NS NS MPP ns ns ns NS NS NS ns ns NS ns SGP NS NS NS NS NS NS NS ns NS NS IGP ns 0.55** -0.48* ns -0.38* ns 0.38* NS NS NS sIG20 0.4J"' _0.39* ** 0.92** ns -0.48* ns 0.53 NS NS NS SIG5 0.45* 0.82*+ ns ns -0.49** ns 0.55** NS NS NS SIGO ns 0.74-+- _0.47+ ** ns -0.46* ns 0.63 NS NS NS SIG2OP NS 0.44* -0.62** ns -0.59*'r ns NS ns ns NS SIG5P NS ns -0.56** ns -0.56** ns NS * *x : NS NS and signifìcance at P < 0.05 and p@ ns : not significant (n : 27). Abbreviations as defined in Table 8-1 and Table g-5. sedimentation volume, which is known to be associated positively with protein quantity and dough "strength" (Axford et at. 1979). Higher correlations of cooked noodle finnness 167
and chewiness with SDS sedimentation volumes were also reported by Huang (1996),
using Chinese wheat varieties.
There were no significant correlations between SPRI and protein parameters in the
optimurn cooking time test, and only MPP was related to SPRI in the constant cooking
time test (Table 8-9). The COHE was not significantly related to protein content and
fraction in the constant cooking time test, but was related to protein content, IGF, and
IGP in the optimum cooking time test. The COHE correlated with all SIG values with
different swelling times, while the highest r-value was from SIGO. This indicated that the
COHE was related to glutenin content and quality. No significant correlations were found
between RESI and protein parameters in the optimum cooking time test. In the constant
cooking time test, the RESI was only correlated with SIG5, SIG0, and the relative SIG
values.
Cooked noodle ADHE was correlated to only SGP and IGP in both constant and
optimum cooking time tests and with quite small r-values (Table 8-9). In both cooking time tests, however, the ADHE liad significantly negative correlations with SIG values.
This indicated that noodles from strong gluten flour were more highly adhesive than those from weak gluten flour.
Protein content was not significantly correlated with SFM in the optimum cooking time test, but positively correlated with SFM in the constant cooking time test (Table g-9). In the constant cooking time test, SFM was also positively correlated with MpF and IGF, but negatively with SGP. However, the SFM was only negatively correlated with IGp in 168
the optimum cooking time test. On the other hand, the SFM was positively correlated with SIG values (SIG20, SIG5, SIG0, and SIG0P) in the constant cooking time test, but it
was negatively correlated with SIG values (SIG20, SIGO, SIG20P, SIG5P, and SiGOp) in
the optimum cooking time test. This indicated that noodles from flours with strong gluten
had surface firm properties in constant cooking times, but had soft surface f,rrmness at
optimum cooking times, because a longer cooking time was required for noodles from flours with strong gluten. This was in agreement with the results of Oh et al. (l9g5a) who
observed that protein content was negatively correlated with white noodle SFM in the
optimum cooking time test.
As discussed above, two cooking times influenced correlation results between protein
parameters and cooked noodle texture. The reason for the differences was probably d¡e
to effect of cooking time. According to ohet at. (19g3,19g5a), CS, recovery and SFM decreased as cooking time increase. For constant cooking time (i 0 min), cooking time for noodles from low protein flour was longer than they required, and cooking time for noodles from high protein and strong gluten flours was not enough. Therefore, noodles from protein low flours were over-cooked and had softer texture and lower SFM than those obtained from optimum cooking time. On the other hand, noodles from high protein and strong gluten flours had harder texture and high SFM than those obtained from optimum cooking time. The differences of cooked noodle texture between low protein flour and high protein flour were enlarged in the constant cooking time test, and were narrowed in the optimum cooking time test. This was why some correlation coefficients between protein parameters and cooked noodle texture in optimum cooking time tests 169
were not significant.
Relationships Between Functional Measurements And Noodte Making euality
Correlations \¡/ere found between dough properties and the parameters of raw noodle
processing quality (Table 8-10). Farinograph DDT, stability, MTI, and gluten index (GI)
were found to be significantly correlated with raw noodle processing quality parameters,
including DSL, THICK, AREA, and DIST, indicating that the raw noodle processing
quality parameters reflected gluten strength. The highest r-values with these processing
parameters were obtained from STA. The farinograph STA is usually used as a main
predictor to evaluate noodle making quality in China (Ding and, Zheng Ig91). The Rmax
of the fresh noodle extension test was only related to DDT. On the other hand, raw
noodle processing quality parameters, except for Rmax, were significantly correlated
with PV, but the correlation coefficients were quite small (r < 0.50).
Farinograph parameters (DDT, STA and MTI) and GI were significantly correlated with
most parameters of noodle cooking quality (Table 8-11). CL and Vy'T were negatively related to dough strength parameters, whereas the OCT and CNT were positively correlated with dough strength parameters. On the other hand, flour pasting properties were not as good as dough strength parameters in predicting noodle cooking quality. As expected, the FSP was positively related to V/T. An unexpected result was that the pV was negatively correlated with CNT. Usually, flours with high peak viscosity related positively to the high swelling power of its starch and negatively to its protein content 170
(Wang and Seib 1996), which means that noodles from high peak viscosity flour should have higher cooked thickness. The opposite result indicated that protein content and quality in the test samples were more important than starch pasting properties in
determining CNT.
Table 8-10 Correlation Coefficients between Functional Measurements and Raw Noodle Processing Quality
THICK Rmax DIST DDT -0.12** 0.67'k'? 0.39'k 0.69** 0.62** STA -0.94** 0.-/6** NS 0.72** 0.72** MTI 0.71** -0.66+* NS -0.61*'+ -0.56'r.+ GI -0.66'** 0.70** NS 0.55+* 0.54** PV 0.44+ -0.45* NS -0.46* -0.45* BKD NS NS NS NS NS FSP NS NS +* NS ns NS 'F and : significance at P < 0,05 and P < 0.01, : respectivelyl ns not srgnificant 1r, : 27). Abbreviations as defined in Table 8-2 and Table 8-3.
Table 8-11 Conelation Coefficients between Functional Measurements and Noodle Cooking Quality
CL OCT CNT DDT -0.52** -0.74+* 0.59** 0.39* STA -0.49** NS 0.51** 0.91** ** MTI NS 0.65 -0.63** -0.49* Gi NS -0.42* 0.51** 0.63** PV NS NS NS -0.54** BKD NS NS NS NS FSP ns ** * ** : 0.53 NS NS and significance at P < 0.05 and P < 0.01, respectively; ns : not significant (n : 27). Abbreviations as defined in Table 8-2 and Table 8-4.
Cooked noodle CS was significantly correlated with farinograph DDT, STA, and MTI in both cooking time tests (Table 8-I2), and the highest r-values were with STA in the constant cooking time test but with DDT in the optimum cooking time test. The HARD was related to only MTI in the constant cooking time test, and no significant correlations 171
were found in the optimum cooking time test. The RECOV was only significantly
correlated with STA and GI in the constant cooking time test, and it was not correlated
with any dough strength parameters in the optimum cooking time test. The CHEW was
significantly correlated with DDT and MTI in the constant cooking time test, but no
significant correlations between CHEW and any dough strength parameters were found
in the optimum cooking time test. The relationships between dough strength and cooked
noodle hardness was in agreement with the results of Huang (1996) who reported that
farinograph DDT and STA were significantly conelated with hardness and f,rrmness of
cooked white noodles.
The SPRI in both constant and optimum cooking time tests had no significant correlation
with any dough strength parameters. The COHE was significantly related to STA and
MTI in the constant cooking time test, but only to STA in the optimum cooking time test.
The RESI was only correlated with STA in the constant cooking time test. The ADHE
was negativeiy correlated with STA and GI in the constant cooking time test, and negatively with DDT, STA, and GI in the optimum cooking time test. The SFM was positively cor¡elated with dough strength parameters (DDT, STA, MTI, and GI), but negatively correlated with dough strength (STA, MTI, and GI) in the optimum cooking time test.
Several investigations reported that starch pasting properties, including high peak viscosity (Moss 1980; Crosbie 1991; Crosbie et at. 1992), high breakdown (Od,a et al.
1980), and swelling power (Crosbie et at. I99Z; yun et al. 1996), are responsible for superior noodle quality. However, no significant correlations between flour pasting 172
properties and cooked noodle texture parameters were found in the optimum cooking
time test in this study (Table 8-I2). ln the constant cooking time test, the pV was
positively correlated with RECOV and ADHE, and negatively with CS, SFM, GIIM, and
CHEW. The BKD was only negatively correlated with HARD, GLIÀ4, and CHEW, while
FSP was negatively related to HARD, and positively to COHE and RESL The statistical
results showed that flours with high pasting peak viscosity or flour swelling power
produced noodles with soft texture, soft surface firmness, low adhesiveness, and low
chewiness.
Table 8-12 Correlation Coefficients between Functional Measurements and Cooked Noodle Quality
RECOV CS SFM HARD ADHE SPRI COHE GUM CHE1V RXSI Constant Cooking Time Test DDT ns 0.63** 0.47* ns ns ns ns 0.45* 0.40* ns ART ns 0.40* 0.51** ns ns ns ns 0.42* ns ns srA -0'57** 0.8i** 0.48* ns -0.65** -0.2r 0.69x* 0.39* ns 0.5r** MTI ns -0.57** -0.48* -0.66** ns ns ns -0.75** _0.71** ns FAB ns ns ns ns ns ns ns 0.3g+ 0.41* ns GI -0.48* 0.56*+ 0.50** ns -0.52** ns 0.41* ns ns ns PV 0.38+ -0.44¿' -0.52** ns 0.52'+'+ ns ns _0.45* _0.40* ns BKD ns ns ns -0.60** ns ns ns _0.5g** _0.57** ns FSP ns ns ns -0.53** ns ns 0.44* ns rls 0.4g* Optimum Cooking Time Test
DDT ns 0.69** ns ns -0.50** ns ns NS NS NS ART 0.56** 0.62** ns 0.38* ns ns ns 0.48* 0.44* ns STA ns 0.53** -0.55** ns -0.56** ns 0.60** ns ns ns MTI ns -0.53** 0.51** ns ns ns ns ns ns ns FAB ns ns rÌs ns GI ns ns -0.50** ns -0.57** ns ns ns ns ns PV 11s NS NS NS ns 11S ns NS NS NS BKD NS NS NS NS NS NS NS NS NS NS
FSP NS NS NS NS 11s NS NS NS * x* NS NS and : significance at P < 0.05 and p < 0.01, respe 27). Abbreviations as defined in Table B-Z and.Table 8-5. 173
CONCLUSIONS
purpose The of this study was to compare the protein content and protein composition as
functional properties of representative Canadian wheat varieties with those of China in
their white noodle making quality. Results of this study showed some significant
differences among Chinese and Canadian wheat. There was no significant difference of
flour protein content between Chinese varieties and CPS varieties, but there appeared to
be differences in protein composition. Chinese wheat varieties had higher levels of
soluble glutenin and lower levels of monomeric protein, compared to Canadian varieties.
Protein quality determined by SIG values for Canadian wheat varieties was superior to
that for Chinese varieties. Chinese wheat was associated with weaker dough properties
compared to their Canadian counterparts.
Compared to Canadian flour noodles, noodles from Chinese flour had weak properties
defined as smaller values of AREA and DiST. However, a highly uniform thickness of
final noodles was obtained from Chinese flour varieties. Chinese flour noodles had higher
CL and short OCT compared to their Canadian counterparts. The soft texture of Chinese
flour noodles was also observed compared to their CPS counterparts. Except for the
effect of flour quality on cooked noodle texture, cooking time also played a role in cooked noodle texture. Cooked noodles from Chinese flours were softer and less chewable in the constant cooking time test.
Compared to commercial Lamian noodle flour, commercial Udon flour had lower levels 174
of protein, but higher FSP and PV. The lower protein content of Udon flour is a key
factor for desired noodle colour, and high FSP and PV detemine noodle eating quality
with soft and elastic texture. Furthermore, other unique properties of Udon flour for
noodle production were also reflected by its protein composition. Very strong gluten based on IGP, SIG20P, and farinograph stability was found in Udon flour, which was different from other low protein wheat flours.
Noodle making quality is determined by content and quality of both protein and starch.
The statistical results showed that protein content and quality were significantly correlated with most noodle making quality parameters. Flour pasting parameters were not as good as protein parameters in prediction of noodle making quality, although some significant correlations existed between flour pasting parameters and cooked noodle texture parameters. The insoluble glutenin content was superior to protein content in predicting noodle making quality. Compared to other protein parameters, the far less expensive and technically simpler SIG test was more highly correlated with most noodle- making quality parameters. lls
CHAPTER 9
General Discussion
Differences in both protein quantity and quality are important attributes of wheat
cultivars which influence end-use applications of their flours (Finney and Barmore l94g;
Btrshuk et al. 1969). The insoluble glutenin content, which is considered an expression of
protein quality, is directly related to dough strength and breadmaking quality (Orth and
Bushuk 1972; Gupta et al. 1992; Bean et al. 1998; Sapirstein and Fu l99g). In this thesis,
a test termed the swelling index of glutenin (SfG) was developed for evaluating glutenin
content and quality, and its application for predicting dough strength in common and
durum wheats was investigated. The effects of nitrogen fertilization on wheat protei¡
content and composition were studied using sequential extraction and SE-HpLC
procedures. White noodle quality was studied in relation to protein content and quality
using Canadian cultivars grown with different nitrogen levels, and a comparison of
Canadian and Chinese cultivars was made.
SIG Test as a Fundamental Measurement of Glutenin Content and euality
Wheat quality testing is carried out routinely to evaluate the end-use potential of a cultivar. The development of quick and small-scale methods for predicting end-use quality continues to be a major focus in wheat breeding progïams. In Chapter 3, a new small-scale method was developed, and the effect of several factors on SIG values was 176
evaluated. The method was successfully used for predicting dough properties and end-use
quality parameters for common (Chapter 4) and durum wheat (Appendix II). It was found
that SIG20 (with 20 min swelling time) was superior to SDS sedimentation volumes and
insoluble glutenin content in predicting dough strength parameters both for common and
durum wheat.
The SIG test is different from current well-known small-scale tests, such as Zeleny, SDS
sedimentation, and gel protein content tests, although these were based on the same
swelling properties of glutenin in non-reducing solvents. SIG values obtained using
strong swelling conditions (long swelling time and strong mixing energy) were closely
related to insoluble glutenin content, while SIG values obtained using mild or ,.gentle,,
swelling conditions (short swelling time) were similar to SDS or Zeleny sedimentation
volumes' When glutenin swelling curves were obtained from SIG values against swelling
time, they provided more information than SIG values alone. It was concluded that the
curves were able to differentiate wheat quality more effectively. With short swelling time
(0-5 min), SIG values were probably affected by both soluble and insoluble glutenin, while with longer swelling time (> 10 min) SIG values could be used to assess both the amount and quality of insoluble glutenin present. For example, three varieties (Glenlea,
Suneca, and Wen 2540) had similar insoluble glutenin content, but different SIG20 values (6.34,6.81, and 5.34, respectively). This indicated that not only did insoluble glutenin content influence SIG values after swelling for 10 min, but also insoluble glutenin quality was a factor. The different swelling curves between Glenlea and Suneca after 20 min showed different characteristics of insoluble glutenin in two cultivars. 177
Insoluble glutenin in Glenlea swelled much slower than that in Suneca (Fig. 3-5).
A SIG test was developed and compared to protein fractions based only on the results
from 20 wheat varieties (Chapter 3). To investigate the relationships between SIG values
and protein content and composition, protein content and composition (MpF, Mpp, SGF,
SGP, IGF, and IGP) were included as predictors in stepwise multiple regression analysis,
using all samples from three groups (as described in Chapters 3, 5, and 8). The selected
models were used to exclude protein parameters that did not contribute significantly to
SIG20, SIG5, and SIGO. The selected models provided three values that can be used for
subset selection (R2, adjusted R2 1R2¡¡:), and Co) indicating how well the data (SIG value
vs. protein parameters) are fitted by a straight line, and this can be used as an indication
of how well the prediction is working. A good prediction equation will have large R2,
R'oo:, and small Co (Schulm an 1992). Stepwise multiple regression models explained
almost 90Yo of the variation in SIG20 values and almost 80o/" of the variation in SIG5
values. However, only 24o/o of the variability in SIGO could be explained with the model.
Table 9-1 Predicting SIG Values Using Stepwise Multiple Linear Regressions of protein Content and Composition
0.891 -1.351 SIG5 IGF 0.789'r.* 0.787 2.122 SIGO SGF/IGF 0.237*'* 0.221 - 1 .528 *'F : significance al P < 0.01 (n : 154); IGF : t in ¡¡ flour; SGF : the percentage of soluble glutenin in flour; R2eor: adjusted-co R2 (ìstimating the proportion of population variability that can be predicted); : a statistic that includes both variance (overfit model) and bias (undernit model).
Effect of Nitrogen Fertilization on Protein Composition 178
Nitrogen fertilization is considered the main environmental factor influencing protein
quantity as well as the end-use quality of wheats (Gooding and Davies lg97).ln general,
protein content increased with an increase in nitrogen ferrllizer level, while protein
composition is genetically determined (Payne et al, Ig87). However, the relative quantity
of specific proteins does vary due to nitrogen fertilization (Tanaka and Bushuk I97Z;
Bushuk et c¿\. 1978; Gupta et c¿L. 1992; Scheromm et al. 1992; Jia et al. l996a,b;
Pechanek et al. 1997; Johansson et al.2001). In Cliapter 2, the review indicated that
reports on the effect of nitrogen fefülization on protein composition were contradictory,
probably due to the different varieties or the different protein fractionation methods used
in different studies. Chapter 5 showed influences of cultivar and nitrogen application on
protein composition determined by both extraction and SE-HPLC procedures. The very
strong relationships between the same protein fractions from the two procedures
indicated that both procedures could yreld similar results on protein ffactionation. The
results demonstrated that nitrogen fertllizer increased wheat protein content and the
absolute amounts of protein fractions, which was in agreement with many previous
reports (Gupta et al. 1992; Jia et al. I996a,b; Gooding and Davies lg97). However, with respect to the relative amount of protein fractions, varieties demonstrated significant individual properties. Generally, increasing nitrogen fertilization resulted in decreases of
IGP, which corresponded to increases of MPP in spring wheat cultivars, and to increases of SGP in winter wheat cultivars. This could probably explain the contradictory effects of nitrogen fertilization on protein composition from other reports. In addition, the ratio of
HMW to LMW glutenin subunits increased with protein content. The SIG29p values in flour protein decreased with an increase in protein content. As protein content increased, 179
dough strength was improved, and dough strength parameters determined by farinograph
and mixograph (except for MDT) were highly correlated with flour protein content and
insoluble glutenin content.
Relationships Between Protein and Noodle Colour
Noodle colour is one of the most important parameters used to evaluate Asian noodle
quality. It is determined by many factors, including flour colour (Miskelly 1984), ash
content (Crosbie et ctl. 1990), flour extraction rate (Yasunaga and Uemura 7962; Hafcher
and Symons 2000), flour particle size (Hatcher et al. 2001), sprout damage (Kruger et al.
1995), protein content (Miskelly 7984), enzqe activity (Hatcher and Kruger 1993;
Hatcher and Kruger 7996), and cultivars (Baik et c¿\. 1995). In Chapter 6, the effects of
protein content on flour and noodle colours were investigated. The results showed that protein content was an important determinant of flour brightness, but PSI also affected its brightness. This suggested that wheat hardness and granularity of the flour are important to flour brightness. Prediction equations for colour parameters incorporating protein content, PSI, and moisture content were obtained from stepwise multiple linear regressions (Table 9-2). Flour PSI and moisture content accounted for 50o/o of the variation in flour Zt value, while flour PSI and protein content accounted for 74yo of the variation in flour å*. However, only 36o/o of the variation in flour a* was accounted for by flour moisture content and protein content. 180
Table 9-2 Predicting Flour Colour Using Stepwise Multiple Linear Regression of protein Content, PSI, and Moisture Content
Selected Parameters R2 Rtoo.l cp L'+ PSVMoisture 0.519** 0.s04 3.6T1 a^ Moisture/Pro 0.375++ 0.3s7 2.s02 **:significanceatP<0'01(n:72);PSI:Particleslze-¿enffib>k PSI/Pro 0.749,F'+ 0.742 2.230 content; Pro : Flour protein content; Other abbreviations as defined in Table 9-1.
Because of the significant effects of flour extraction on noodle colour and the different
flour extraction rates used for three sets of samples included in this study, it was
impossible to use all samples in stepwise multiple regression. Table 9-3 shows stepwise
multiple linear regressions between noodle colour parameters and protein parameters
(including protein content and protein fractions) for three groups of flour samples,
separately. The results from Chinese and Canadian grown wheat flours reflected the
relationships of noodle colour with protein parameters across varieties, and the results
from nitrogen ferlllization samples reflected the relationships across and within varieties.
For Chinese flours, selected protein parameters only acconnted for 40o/o of the variation
in noodle L* value at 0 min rest time. After 2 h rest, protein parameters lost their ability
to account for variation in Z* and a*.In addition, protein parameters accounted for more
than 600/o of variation in noodle L* after 0 min and, 2 h rest for noodles from Canadian
grown wheat flours. Furthermore, the regression (Rt : 0.670) of L* for nitrogen
fertilization samples indicated a good fit to the data and suggested that noodle Zx value
after 24 h rest can be accurately predicted by the protein parameters alone. Similarly, noodle a* and å* values had better correlations with protein parameters in samples grown with different nitrogen levels. 181
Table 9-3 Predicting Noodle Coloul Using Stepwise Multiple Linear Regressions of protein Conte¡t and Compositionl
0h 2lt 24h 24 h and cooked
Chinese Wheat (rz = Selected Pro MPF/SGF parameters MPP ns ns MpF ns tls MpF tls R2 o.4og* 0.572* 0.433+ 0.459* Rtoo, 0.41'3* 0.397 0.417 0.37 6 0.405 0.421 cp -0.391 0.477 0 .529 1 .65 9 _1 ,204 Canadian Grorvn Wheat @:20)3 Selected Pro P.o lls Pro Parameters Pro ns ns ns ns ns pr.o ns R2 0.660** 0.294* 0.634*x 0.252* 0.245+ R'oo, 0.641 0.255 0.614 0.210 0.204 ce -3.169 1.564 -2.037 4.250 0.592 N Fertilization Samples @:96)a MPF/scP MPF/scF scF MpF/scp MpF/scF scF/Mpp scF/MPP r:,*:#, ,S,[fl,I,H scF/rcp MpF/scF ffi,#å? rGF/Mpp R2 0'560** 0.551*'r' 0.253+o' 0.595*+ 0.631+4' 0.3261'* 0.670*r, 0.r42+* Rtoo, 0'550 0.541 0.245 0.586 0.424,.-+ 0.2g4*'r 0.7i r*,r, 0.r09** 0.629 0.311 0.656 0.735 0.4t2 0.219 0.698 0.090 co, 5'556 2'279 3.614 19.386 r ,, 0.431 6.301 16J87 4.263 7.ggs 36.161 2.225 6.160 * and ** : rign omeric protein in flour and in protein, respectively; SGF and : sGP the percentage ãf soluble glutenin in flour and protei', respectively; IGF and IGP : the percentage of insoluble glutenin in flour and 2Twelve proteiir, respect-ively; Other abbreviations as defined i' Table 9-1. chinese straight grade flours ás described in g. 3Twenty chapter canadian gtown wheat flours with 65% extraction rate. " Ninety six flours with 65% extraction rate from nitrogen fertilization samples (chapter 5) including 1995 spring, three winter wheats, of each 1994 spring and winter wheats with six nitrJgen fertilization levels. 182
As rest times increased, quick loss of relationships between noodle brightness and protein
parameters in Chinese flours showed that flour extraction rate has a considerable effect
on the prediction of noodle brightness using protein parameters. Noodles from Chinese
flours had high extraction (approximately 75%) and possibly contained high levels of
enzymes such as PPO. The enzyrnes are located largely in the bran and, hence, increases
exponentially with increasing mill extraction rate (Hatcher and Kruger 1993). Therefore,
it is highly likely that discolouration caused by PPO influenced noodle brightness more
than did protein content after 0-h measurement. Actually, strong visible changes of
noodle colour were observed from some Chinese flours but not from 20 Canadian wheat
flours and flours grown with different nitrogen fertilization levels.
Relationships of Protein Parameters with Noodre Making euality
In Chapters 7 and 8, the effects of protein parameters on noodle making quality were
discussed, using 120 nitrogen fertilization samples, as well as 13 Canadian and 13
Chinese wheat samples. Protein content was found to influence almost all noodle making
quality parameters.
In terms of noodle processing quality, an increase in protein content corresponded with an increase in noodle thickness, maximum resistance (Rmax), extension area (AREA), and extension distance (DIST), and to a decrease of dough sheet length (DSL) in cultivars. Across cultivars, the stronger flour produced noodles with low DSL, high thickness, Rmax, AREA, and DIST. This indicated that flours with high protein content 183
and strong gluten produced noodles which did not easily break during the drying process,
but which had variable size in the final products (Li 1996). Although both groups of
flours had wide ranges of protein content and dough strength, noodles from Chinese
flours had narower ranges of thickness even after cookin.g than noodles from Canadian
flours.
'WT, It was found that protein content was negatively correlated with CL and but
positively correlated with optimum cooking time (OCT). Cooked noodle thickness (CNT)
increased with protein content within a vanety, and a positive significant correlation
between SIG20 and CNT was observed in Chapter 8. This indicated that CNT was related
to fresh noodle thickness, which was determined by protein content and dor-rgh strength.
Cooked noodle texture is the most important noodle making quality parameter in relation
to noodle eating quality. To evaluate cooked noodle texture, noodles were cooked with
two cooking times, either optimum or constant, and cooked noodle texture \Ã/as
determined with two well-known instrumental measurements, the method of Oh et al.
(1983, 1985a) and the TPA method (Baik et al I994a). Variations between the rwo
cooking time tests in the level and the significance of the correlation coefficients were obvious as discussed in Chapters 7 and 8, and indicated substantial effects of cooking time on cooked noodle texture. Noodles from low protein flours were over-cooked and had softer texture and lower SFM from constant cooking time (10 min) than those obtained from optimum cooking time. On the other hand, noodles from high protein and strong gluten flours had harder texture and high SFM than those obtained from optimum 184
cooking time. Therefore, the differences of cooked noodle texture between low protein
flour and high protein flour were enlarged in the constant cooking time test, and were
narrowed in the optimum cooking time test. This was why some correlation coeff,rcients
between protein parameters and cooked noodle texture in optimum cooking time test
were not significant.
Generally, greater protein content improved cooked noodle hardness and chewiness. A
significant correlation between protein content and SFM was only observed in constant
cooking time tests. The insoluble glutenin content was superior or equal to protein
content in predicting cooked noodle texture parameters. Compared to protein content and
fractions, the far less expensive and technically simpler SIG test was more highly
correlated with most noodle-making quality parameters. This indicated that not only
glutenin content was important, but also glutenin quality should be considered in
evaluation of noodle making quality.
Because there was little effect of flour extraction rate on cooked noodle texture (Oh et al.
i985c; Lee et al. 1987; Kruger et al. 1994), stepwise multiple regression was used for all samples (Table 9-4). All protein parameters, including protein content and fractions, the absolute and relative values of SIG, and FSP were used as predictors. The selected model in the constant cooking time test explained more than 50o/o of the variation in RECOV, but only 35o/o was explained in the optimum cooking time test. Similarly, the selected model explained a higher percentage of the variation in CHEV/ in the constant cooking time than in the optimum cooking time test. More than 600/o of the variation in CS was 18s
explained by selected models in constant and optimum cooking time tests, but only 4lyo
and 8o/o of the variations of HARD were explained by selected models in constant and
optimum cooking time tests, respectively. The selected models explained 79o/o of the
variation in SFM in constant cooking time tests, but only 33o/o in optimum cooking time
tests. Similar results were obtained in selected models for ADHE, SPzu, COHE, GtIl\4,
and RESi. These results compared favourably with the results of Yun et at. (1996), which
were able to account for a similar amount of variation in noodle eating quality using
protein content, mixograph parameters, and FSP as independent variables and sensory
evaluation as a dependent variable.
Flour pasting parameters were not as good as protein parameters in predicting noodle
making quality (Chapters 7 and 8), although some significant correlations existed
between flour pasting parameters and cooked noodle texture parameters. Therefore, only
FSP was included in the selected models for RECov, SFM, COHE, and RESI. The results in Chapters 7 and 8 supported the results from other studies (Oh et al. I9B5b, c;
Huang and Morrison 1988; Batk et al. I994a), which showed that white noodle quality was related to protein content and quality. However, many other studies (Nagao er a/.
1977; Moss 1980; Oda et al. 1980; Toyokawa et al. I989a,b; Crosbie1990,1997;yuner al. 1996) have shown that variation in the eating quality of noodles is related to starch pasting properties and amylose content. The reason for the different conclusions was probably due to the fact that samples used in the different studies had different ranges of 'When protein and starch content and properties. samples had wide ranges of protein content and dough strength, such as the samples in the current study and in the studies of r86
Table 9-4 Predicting Cooked Noodle Texture Using Stepwise Multiple Linear Regressions of protei¡ parameters and FSp
RECOV CS SFM HARD ADHE SPRT COHE GUM CHEW RESI Constant Cooking Time Test SGP/IGP/ SGF/ SIGO/ Selected parameter.s SIG5P/ SIG5 SIGO/ IGF/FSP :,cf I9I1 .Ìå",i'. iGF iGF SIG2OP SIGOP/FSP SIG5P/FSP slc0 stc0 /FSP /FSP R2 0.526** 0.6474.,' 0.799*+ 0.410'o* 0.418** 0. 154* 0.614,¡.* 0.45 1*u,, 0.460** 0.505 ** R'oo, 0.493 0.640 0.172 0.387 0.395 0.1203 0.590 0.441 0.450 0.415 cp 3.623 0.535 1 8.899 4.951 1.362 -2.466 I .92 2.760 -1.836 5.119 Optimum Cooking Time Test SGF/ SIGO/ SGF/ Selected parameters Pro/IGF SIG5/ IGF IGF/ SIG2OP SIG5 ns SIG5/ IGF Pro/FSP FSP FSP FSP R2 0.349** 0.654** 0.330** 0.076* 0.257** 0.395** 0.192** 0.1 g6** 0.1 76+* Rtoo, 0323 0.633 0.303 0.058 0.242 0.352 0.171 0.1 54 0.144 C 1.647 2.001 1.572 * ** -2.352 -0.771 8.538 0.971 0.837 -2.248 and : significallce at P < 0.05 and P < 0.01, : respectively, ns rot signi in Tables 8-1,9-2, 8-5, and 9-1. t87
Oh et al- (1985b,c) and Balk et al. (I994a), the effects of protein parameters on cooked
noodle texture covered up the effect of starch. Similarly, when samples had wide ranges
of starch swelling properties but narrow ranges of protein content and quality, such as in
samples used in the studies by Toyokawa (1989a,b), the effects of starch pasting
properties were greater than that of protein content and quality.
Cooked noodle texture parameters were significantly correlated with farinograph
parameters. The STA provided a good prediction of cooked noodle texture, as indicated
by the correlation of STA and CS. This correlation result indicated that the STA is better
than the other farinograph values for predicting wheat flour quality for noodle eating
quality (Huang 1996).
Proposed Balance Between Starch and protein in Noodre Flour
Although both protein and starch in wheat flour are known to have important effects on
the eating quality of white noodles, the functionality of protein in noodles was opposite to
that of starch pasting properties. High peak viscosity and swelling power, which were
associated with low levels of amylose (Miura and Tanii 1994; Zhao et a/. 1998; Seib
2000), were related to the softness of noodles. ln contrast, an increase in protein content or increase in gluten strength caused noodles to have a harder texture. Therefore, a balance among protein and starch parameters should exist in different kinds of noodle flours. For instance, the soft texture of Japanese noodles was influenced by high flour swelling power and low protein content. The processing problems normally caused by 188
low protein content were resolved by the presence of strong gluten in Udon flour. The
balance of high flour swelling power, low protein content, and strong gluten in Udon
flour yields noodles with the appearance and soft texture desired by domestic consumers
in Japan.
As for Chinese white noodles, a more firm texture is preferred. According to the known
roles of starch in influencing pasting properties, and the roles of protein content and
gluten strength play in cooked noodle texture, the firmness of cooked noodles should be
improved by increasing amylose content, protein content, or gluten strength. lncreased
protein content through nitrogen fertilization is an expensive way to improve noodle-
making quality. Therefore, improvement of gluten strength and increase of amylose
content are suitable ways to improve the cooked noodle firmness. As discussed in
Chapter 8, Chinese flours have weak gluten strength and produce noodles with softer
textures compared to CPS flours. It is therefore understandable why millers in China
blend Chinese wheat with Canadian wheat to improve the gluten strength of noodle flour
(Wang er al. 1995).
Future Studies
After completing the writing of the research papers included in this thesis, several additional questions come to mind requiring additional studies.
In SIG method development, it was found that a small amount of flour (20 mg) could be 189
used to obtain different SIG values among varieties, and this sample size makes use of
single kernels possible for SIG tests. Since there is benefit from use of this sample size in
breeding programs, flirther study is needed to develop equipment for single-kernel
grinding.
As discussed in Chapter 3, soluble glutenin quality was reflected at short swelling times
and insoluble glutenin quality was reflected at long swelling times, based on the
explanation of swelling curves. Current knowledge of glutenin content and glutenin
subunit composition could not entirely explain the differences of glutenin swelling
properties among varieties. For example, why did varieties with similar insoluble
glutenin content, such as Suneca and Glenlea, have different curyes, especially afÍer 20
min swelling time? The different curves reflected different insoluble glutenin swelling
properties, defined as insoluble glutenin quality. However, insoluble glutenin quality may
be due to differences of insoluble glutenin molecular weight, which cannot be determined by current technologies; or may be due to differences of interactions of glutenin with other flour components, or even with themselves. Further study is needed to investigate factors that determine glutenin swelling properties. These factors will provide a better explanation of why Glenlea has extra strong gluten.
Results of this study showed some important effects of protein content and quality on cooked noodle texture as determined by instrumental measurements. Instrumental measurements work well in screening large numbers of plant breeder's samples and in understanding the role of specific components that govern the textural properties of 190
noodles, since they are reproducible and not subject to regional taste and evaluator
fatigue such as occurs in sensory evaluation. However, fuither study is needed to
investigate the relationships between instrumental measurements and sensory evaluation
of Chinese white noodle eating quality, because sensory testing of noodle texture is direct
and is the most comprehensive means of evaluating the f,rnal product (yun et al. 1996;
Ross er al. 1997).
In the general discussion the role of protein and starch in noodle making quality was
presented, and the concept of a balance between protein and starch was proposed. Based
on results from commercial Udon flours, two balances need to be achieved: one between
protein and starch, the other between protein content and protein quality determined by
IGP or SIG2OP. Data examined and those obtained in this study indicate that the most
preferred characteristics of noodles should be obtained by adjusting the balances. For
example, Udon flours contain 2-3% lower protein and, 5-6Yo lower amylose content than
alkaline noodle flours (Jun et al. 1998; Noda et al. 2001), and produce noodles with soft
texture. If reconstituted flour contains higher protein content and lower amylose content (e.g., 10olo lower than alkaline noodle flours) than Udon flour, will noodles from this flour have similar texture compared to noodles from Udon flours? Fractionation and reconstitution technology can also provide information about the balance of starch and protein in Chinese white noodles. Further, better understanding of the balance required for noodle making quality will provide more information to screen cultivars for noodle manufacture. 191
Results in Chapter 8 showed some important differences between Canadian and Chinese wheats. However, examination of more varieties grown over several seasons is required to validate the results and to provide a more comprehensive comparison of wheats from the two countries. ln addition, studies on the effects of blending Canadian wheat with
Chinese wheat during milling on Chinese food quality will provide some useful information to satisfy the needs of Chinese buyers. t92
CHAPTER 10
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N rate MPF SGF IGF MPP SGP SIG2O SIGs SIGO stG20P StcSP SIGOP Katepwa
0 5.29c 1 .34bcd 3.'l 6a 49.86b 12.64b 29.81a 5.06c 5.00c 4.24ab 47.74ab 47.12b 39.95a 40 4.94d 1.24d 2.86b 49.90b 12.47b 28.B9ab 4.53d 4.81d 4.08b 45.76bc 48.54a 41 .16a BO 5.45c 1.29cd 2.88b 52.36b 12.36b 27.64bc 5.01c 4.96cd 4.17ab 48.13a 47.6gab 40.05a 120 6.62b 1.50abc 3.27a 55.59a 12.56a 27.44bcd 5.39b 5.27b 4.38ab 45.25c 44.24c 36.76b 160 6.96a 1.52ab 3.23a 55.64a 12.16a 25.B4cd 5.60b 5.58a 4.31ab 44.80c 44.64c 34.44b 240 7.14a 1.63a 3.25a 56.18a 12.80a 25.55d 5.87a 5.55a 4.45a 46.18abc 43.66c 35.00b Roblin
0 5.33d 1.33c 3.27bc 48.45b 12.09a 29.68ab 6.06c 5.69cd 4.17a 55.09bc 51.73b 37.91a 40 4.70e 1.25c 3.15c 46.53c 12.38a 31 .19a 5.80d 5.51d 4.11a 57.43a 54.50a 40.69a BO 5.56c 1.53bc 3.37bc 50.05ab 13.78a 30.32ab 6.27c 5.80c 3.78a 56.49ab 52.21b 34.0sb 120 6.47b 1.70ab 3.55ab 51.76a 13.60a 28.36ab 6.68b 6.14b 3.94a 53.40cd 49.12c 31.48bc 160 6.87a 1.87a 3.78a 51.62a 14.03a 29A2ab 6.78b 6.44a 3.93a 50.98e 48.42c 29.51c 240 7.00a 1.96a 3.77a 51.43a 14.39a 27.72b 7.10a 6.49a 3.87a 52.17de 47.68c 28.42c BW 90 5.56d 1.26b 2.98cd 52.90a 12.00a 28.38a 5.36d 5.12cd 4.47a 51 .00a 48.76a 42.57ab 40 4.89e 1.22b 2.85d 49.34b 12.32a 28.74a 5.16d 4.97d 4.36a 52.12a 50.15a 44.04a 80 6.00c 1.39b 2.97cd 55.00a 12.71a 27.25ab 5.62c 5.25c 4.43a 51.56a 48.17a 40.60b 120 '1.65a 6.63b 3.22bc 54.30a 13.48a 26.39ab 5.92b 5.53b 4.53a 48.48b 45.29b 37.09c 160 7.14a 1.77a 3.39ab 52.85a 13.07a 25.07b 6.39a 5.92a 4.49a 47 .30b 43.81bc 33.26d 240 7.37a 1.84a 3.56a 52.27a 13.0'la 25.21b 6.53a 6.05a 4.67a 46.31b 42.87c 33.09d AC Reed
0 4.01d 1.02d 1.76ab 54.93ab 13.90b 24.11a 2.73ttc 3.86c 3.59bc 37.33a 52.88a 49.1la 40 3.BBd 1.02d 1.58b 54.58b 14.30b 22.11ab 2.44d 3.62d 3.43c 34.37bc 50.92ab 48.24a BO 4.29c 1.18cd 1.63b 55.65ab 15.32ab 21.17b 2.58cd 3.80c 3.55bc 33.44c 49.35bc 46.10a 120 4.94b 1.33bc 1.7Ùab 58.12a 15.59ab 20.94b 2.80b 4.07b 3.76abc 32.88c 47.82c 44.18a 160 5.09ab 1.49ab 1.84a 57.19ab 16.69a 20.62b 3.19a 4.48a 3.99ab 35.79ab 50.28b 44.78a 240 5.22a 1.50a 1.84a 57.36ab 16.48a 20.22b 3.'l9a 4.36a 4.19a 35.05bc 47.91c 45.99a AC Taber
U 4.33d 0.99cd 2.73bc 49.77b 11.38a 31 .32a 4.63d 4.54d 3.64a 53.22b 52.18c 41.84ab 40 3.90e 0.93d 2.19d 50.58b 12.08a 28.38ab 4.02e 4.28e 3.53a 52.21b 55.58a 45.78a 'l.06cd BO 4.62c 2.43cd 53.66a 12.33a 21.26ab 4.78cd 4.63d 3.65a 55.58a 53.78b 42.33ab 120 5.16b 1.20bc 2.60cd 54.26a 12.58a 27.37b 4.96c 4.91c 3.71a 52.16b 51.63cd 39.00ab 160 5.57a 1.34ab 2.96ab 53.56a 12.88a 21.46ab 5.36b 5.27b 3.83a 51.49b 50.67d 36.78b 240 5.71a 1.44a 3.12a 51 .B6ab 13.09a 28.32ab 5.68a 5.56a 3.85a 51 .59b 50.50d 35.00b tM.u.t, in the same column within a variety and heading follorved by the same letter are not statistically different (P > 0.05) by t multiple range test. t MPF and MPP : the percentage of monomeric protein in flour and protein, respectively; SGF and SGp : the percentage of soluble glutenin in flour and protein, respectively; IGF anú IGP : the percentage of insoluble glutenin i¡r flour and protein, respectively; SIG20 and SIG2OP : swelling index of glutenin with 20 min swelling time in flour' (g/g) and protein (%), respectively; SIG5 and SIG5P - swellìng index of glutenin with 5 min swelling time in flour (g/g) and protein (%), respectively; SIGO and SIG0p: swelling index of glutenin with 0 min swelling time in flour (g/g) and protein (%), respectively. 208 Table 2 Multiple Comparisons' of Protein Fractions t Determined by Sequential Extraction and SIG values for 1995 Spring wheat cultivars
N rate MPF SGF IGF SGP IGP SIG20 SIG5 stG0 SIG2OP SIGsP SIGOP Katepwa
U 1.12c 5.30de 2.99bcd 51.96c 10.93a 29.31a 4.88c 5.01de 3.78a 47.79a 49.12ab 37.01a 40 1.04c 5.22e 2.78d 53.27c 10.61a 2Ù.32ab 4.64d 4.89e 3.72a 47.30a 49.85a 37.91a BO 1.11c 5.65d 2.84cd 54.33bc 10.63a 27 .26ab 4.88c 5.05d 3.54a 46.88a 48.56b 33.99ab 120 1.19bc 6.67c 3.12bc 56.97ab 10.17a 26.67ab 5.07c 5.33c 3.40a 43.29b 45.56c 29.02bc 160 7.32b 1.32ab '10.39a 3.30ab 57.60a 25.98b 5.55b 5.57b 3.58a 43.70b 43.86d 28.1 5bc 240 1.40a 7.73a 3.49a 57.22ab 10.37a 25.81b 5.89a 5.84a 3.41a 43.63b 43.22d 25.22c Roblin
0 5.18e 1.13b 50.74c 3.11de l1.o8a 30.44a 5.S4e 5.52c 4.28ab 54.26a 54.07a 41 .91a 40 5.10e 1.16b 2.97e 49.95c 11.37a 29.12ab 5.30f 5.20d 4.15ab 51.96b 50.98b 40.64a BO 1.36ab 6.02d 3.30cd 51.45bc 1 1.58a 28.16bc 5.89d 5.61c 3.99b 50.34c 47.95cd 34.06b 120 1.37ab 6.69c 3.40bc 54.35a 11.14a 27.64bc 6.26c 6.08b 4.13ab 50.85bc 49.39c 33.58b 160 7.04b 1.50a 3.63ab 52.93ab 11.24a 27.26bc 6.70b 6.26b 4.23ab 50.34c 47.07d 31.80b 240 7.68a 1.60a 3.84a 53.67a 11.19a 26.82c 7.19a 6.59a 4.66a 50.28c 46.08d 32.59b Glenlea tt 0.80c 4.85e 3.18cd 52.15a 8.60a 34.19a 5.45d 5.07d 4.02a 58.55ab 54.52a 43.23a 40 4.62e 0.85c 2.92e 51.91a 9.49a 32.8'la 5.24d 4.82d 3.81a 58.8Ba 54.10a 42.75a BO 0.99bc 5.24d 3.07de 52.93a 1 0.00a 31 .01b 5.73c 5.50c 3.76a 57.88ab 55.51a 37.9Bab 120 5.91c 1.09b 3.32c 52.77a 9.73a 29.60bc 6.3Sb 5.92b 3.93a 56.70b 52.81a 35.04bc 160 I.18ab 6.34b 3.60b 52.36a 9.75a 29.75bc 6.39b 5.7Bbc 3.84a 52.77c 47.77b 31.69c 240 6.97a 1.32a 52.77a 3.82a 9.96a 28.90c 6.80a 6.31a 3.99a 51.48c 47.77b 30.1 9c AG Reed
0 4.09cd 1.16cd 1 .56ab 56.81a 16.11a 21.67a 2.15cd 3.28cd 3.54cd 29.79ab 45.56a 49.17a 40 1.08d 3.85d 1.48b 55.00a 15.43a 21 .14a 2.13d 3.09d 3.36d 30.36a 44.07a 4B.00ab '1 BO 4.38c 1.37bc .63ab 56.1 5a 17.56a 20.90a 2.28c 3.32cd 3.63bcd 29.17abc 42.50ab 46.54abc 120 1.42b 5.04b 1.70ab 58.60a 1 6.51a 19.77a 2.45b 3.46c 3.B5ab 28.49bc 40.23b 44.77bc 160 1.43b 5.06b 1.64ab 58.84a 16.63a 19.07a 2.44b 3.88b 3.77bc 28.37bc 45.12a 43.78c 240 5.60a 1.69a 1.78a 58.95a 17.74a 18.74a 2.66a 4.34a 4.07a 28.00c 45.68a 42.84c AG Taber n 0.95cd 4.59c 2.43b 52.76a 10.92a 27.93a 4.44c 4.67d 3.77a 50.98ab 53.62a 43.33ab 40 0.86d 4.31c 2.06c 54.56a 10.82a 26.08a 3.93d 4.30e 3.69a 49.75b 54.37a 46.65a BO 4.40c 0.99bcd 2.32bc 52.38a 11.79a 27.56a 4.43c 4.58d 3.70a 52.68a 54.46a 43.99ab 120 5.02b 1.15ab 2.58ab 53.92a 12.37a 27.69a 4.721) 4.84c 3.73a 50.75ab 51.99b 40.1'lab 160 5.26b 1.11ab 2.61ab 54.18a 11.39a 26.91a 4.86b 5.02b 3.71a 50.05b 51 .70b 38.20b 240 5.71a 1.29a 2.88a 53.82a 12.12a 27.17a 5.41a 5.42a 4.10a 50.99ab 51 .08b 38-68b 'M.on, in the same column within a variety and heading followed by the same lefter are not statistically different (P > 0.05) by t multiple range test. 2 Abbreviations as defined in Table l. 209 Table 3 Multiple Comparisonsr of Protein Fractions' Determined by Sequential Extraction and SiG values for 1994 winter wheat curtivars
N rate MPF SGF MPP SGP IGP SIG2O SIGs SIGO slc2oP stGsP SIGOP CDC Kestrel n 4.59c 1.01d 2.92b 50.44ab 11.1Ocd 32.09a 5.08b 5.21d 4.49bcd 55.82ab 57.20ab 49.29bc 40 4.14d 0.87e 2.51e 51 .69a 10.88d 31 .3Ba 4.SBd 4.76e 4.34cd 57.19a 59.50a 54.25a BO 4.10d 2.61d 0.98d 49.40b 11.81c 31.39a 4.30e 4.83e 4.29d 51.81c 58.13ab 51 .63ab 120 4.64bc 1.31c 2.69c 49.36b 13.88b 28.56c 4.83c 5.48c 4.61bc 51 .33c 5B.30ab 49.04bc 160 4.85b 1.45b 2.89b 48.50b 14.50b 28.B5bc 5.21b 5.71b 4.70b 52.10c 57.05b 46.95c 240 5.44a 1.77a 3.26a 49.45ab 1 6.09a 29.59b 6.01a 6.23a 5.07a 54.64b 56.59b 46.05c Norstar
0 5.48b 1.01d 2.83c 54.21a 10.00d 28.02c 5.8'lb 5.51c 4.62abc 57.52a 54.55c 45.74b 40 4.44c 2.63d 0.76f 52-24ab 8.94e 30.BBa 4.54d 4.66e 4.34c 53.41b 54.82c 51 .00a BO 4.54c 2.60d 0.85e 52.79ab 9.88d 30.23a 4.67d 5-02d 4.42c 54.30b 58.31a 51 .34a 120 5.33b 1 .1 8c 2.87 c 53.B4ab 11.87c 28.94b 5.44c 5.60bc 4.49bc 54.95b 56.57b 45.35b 160 5.54b 1.40b 3.06b 51.78b 13.04b 2B.60bc 5.83b 5.74b 4.B0ab 54.44b 53.64c 44.86b 240 6.10a 1.614 3.28a 52.09ab 13.76a 28.03c 6.36a 6.35a 4.94a 54.32b 54.23c 42.18b sB6-375 0 4.85c 0.54e 2.10cd 61 .33a 6.77f 26.58b 3.81b 4.05d 3.64d 48.23a 51 .27abc 46.08b 40 4.26d 0.61e 2.08d 56.73b 8.07e 27.73a 3.41c 3.93d 3.75cd 45.47b 52.33a 50.00ab BO 4.26d 0.70d 2.16bc 55.26bc 9.03d 27.99a 3.53c 4.05d 4.02bcd 45.84b 52.53a 52.21a 120 4.69c 0.91c 2.17b 55.83b 10.83c 25.83b 3.4Sc 4.35c 4.05abc 41.01c 51 .73ab 49.21ab tou 5.09b 1.07b 2.15bc 55.88b 11.70b 23.63c 3.83b 4.54b 4.24ab 42.03c 49.84c 46.59b 240 5.28a 1.27a 2.38a 53.BBc 12.96a 24.29c 4.13a 4.93a 4.44a 42.14c 50.26bc 45.31b s86-1 01
0 5.07c 1.00cd 2.72bÇ, 54.46a 10.75d 29.25a 4.6Sd 5.12d 4.41c 49.95b 55.00ab 47.42ab 40 4.40d 0.91d 2.57c 50.57b 10.40d 29.48a 4.28e 4.87e 4.33c 49.20bc 55.92ab 49.71a BO 4.45d 1.05c 2.55c 49.94b 1 1.80c 28.65a 4.31e 5.07de 4.39c 48.37c 56.91a 49.27a 120 5.14c 1.43b 3.02ab 49.85b 13.83b 29.32a 5.1Sc 5.55c 4.76b 49.95b 53.83b 46.17b 160 5.39b 1.67a 2.99ab 49.86b 15.46a 27 .64a S.39b 5.91b 4.88ab 49.91bc 54.72b 45.14b 240 5.73a 1.77a 3.15a 50.26b 15.48a 27 .63a 5.91a 6.344 5-15a 51.84a 55.57ab 45.13b Winalta
0 5.28c 0.95c 3.20c 50.77a 9.09d 30.72d S.g6b 5.29c 4.10a 57.31ab 50.82c 39.42ab 40 4.57e 0.86d 3.01d 50.16a 9.40d 33.08b S.30c 4.9sd 4.08a 58.1 9a 54.40ab 44.78a BO 4.56e 0.94c 3.14c 49.57b 10.22c 34.08a S.1Bc 5.1 3cd 4.14a 56.25abc 55.71a 44.95a 120 5.03d 1.36b 3.36b 47 .41d 12.83b 31 .70cd 5.81b 5.59b 4.16a 54.81c 52.74bc 39.20ab 160 5.50b 1 .41b 3.61a 48.67c 12.43b 31.95c 6.24a 5.95a 4.24a 55.1 Bbc 52.65bc 37.52b 240 5.62a 1.62a 3.72a 47.23d 13.57a 31.22cd 6.20a 6.1 5a 4.37a 52.06d 51.64c 36.72b 'M.un, in the same colunÌr within a variety and heading followed by the same letter are not statistically different (P > 0.05) by t multiple range test. 2 Abbreviations as defured in Table 1. 2t0 t Table 4 Multiple Comparisonsr of Protein Fractions Determined by Sequential 'wheat Extraction and SIG values for 1995 winter cultivars
N rate MPF IGF MPP SGP IGP SIG2O stG5 SIGO SIG2OP SIGSP SIGOP CDG Kestrel 0 4.13c 0.78d 2.50c 5't.56a 9.75d 31.25c 4.44b 4.58c 4.'l6b 55.50b 57.25b 51.94ab 40 3.1 Be 2.22e 0.61e 48.11cd 9.24e 33.56a 3.71d 3.94e 3.63c 56.14ab 59.62a 55.00a BO 3.45d 0.83c 2.40d 46.55d 11.22c 32.36b 3.84d 4.34d 3.90bc 51.89cd 58.65ab 52.64a 120 4.01c 0.99b 2.46cd 4B.84bc 12.07b 29.94d 4.19c 4.77c 4.15b 51.04d 58.17ab 50.55ab 160 4.52b 1.02b 2.66b 50.73a 11.46c 29.Bgd 5-23a 5.28b 4.25ab 58.76a 59.33ab 47.70b 240 4.88a 1.23a 2.79a 50.31ab 12.68a 28.71e 5.27a 5.53a 4.59a 54.28bc 56.96b 47.27b Norstar
0 4.82c 0.75d 2.66c 53.56a 8.28d 29.56bc 4.82c 4.66e 3.84a 53.50b 51 .7Bc 42.61c 40 3.46f 0.57e 2.20d 50.14b 8.19d 31.88a 4.05d 4.07d 3.60a 58.70a 58.91a 52.17a BO 3.78e 2.29d 0.71d 50.40b 9.40c 30.53ab 4.25d 4.45d 3.81a 56.67ab 59.27a 50.73ab 120 4.51d 0.89c 2.63c 51.25b 10.06b 29.B3bc 4.80c 4.87c 3.82a 54.55b 55.28b 43.35bc 160 5.26b 1.06b 2.96b 53.0Ba 10.71a 29.90bc 5.44b 5.41b 3.97a 54.95b 54.65b 40.05c 240 5.67a 3.08a 1.13a 52.99a 10.51ab 28.74c 5.99a 5.82a 4.22a 55.98ab 54.39b 39.39c s86-375 0 4.96b 0.59e 2.07b 60.43a 7.20e 25.19bc 3.78ab 4.08c 3.62b 46.10a 49.76d 44.09d 40 3.55f 0.57e 1.86d 53.71cd 8.56d 2B.1Ba 2.94c 3.64e 3.50b 44.47ab 55.1sab 52.95a BO 3.77e 1 .96c 0.71d 53.1 0d 9.93c 27.54a 3.06c 3.83d 3.65b 43.03ab 53.94b 51.34ab 120 4.1 9d 0.79c 1.99bc 55.07bcd 1 0.39bc 26j8b 3.13c 4.13c 3.75b 41 .1Bb 54.28ab 49.28bc 160 4.68c 0.93b 2.07b 56.39b 11.20ab 24.BBc 3.58b 4.61b 4.12a 43.13ab 55.48a 49.58abc 240 5.1 5a 1.09a 2.29a 55.98bc 11.79a 24.89c 3.97a 4.75a 4.35a 43.1Oab 5'1.58c 47.28cd s86-1 01 0 4.57c 0.87d 2.59c 52.47a 10.00e 29.71e 4.49b 5.00b 4.17bc 51 .61a 57.47b 47.93abc 40 3.5't e 0.75e 2.35d 49.44c 10.49d 33.'l0d 3.72d 4.33d 3.73c 52.39a 60.92a 52.46a BO 3.88d 1.00c 2.56c 47.90d 12.28c 31.54c 3.97c 4.60c 4.01bc 49.01c 56.73bc 49.51ab 120 4.51c 1.24b 2.78b 48.44cd 13.33a 29.89a 4.53b S.14b 4.09bc 48.66c 55.27cd 43.98c 160 4.78b 1.22b 2.751) 50.85b 12.98b 29.20b 4.64b 5.10b 4.35ab 49.36bc 54.26d 46.22bc 240 5.1 6a 1.35a 3.00a 50.54b 13.24a 29.41a 5.24a 5.84a 4.66a 51.37ab 57.25b 45.69bc Winalta 0 5.12c 0.70e 2.85c 55.00a 7.47d 30.59bc 5.38b 4.91c 3.98a 57.85ab 52.74b 42.80ab 40 4.1 6d 0.62f 2.52d 53.33ab 7.95c 32.24a 4.S6c 4.37d 3.79a 58.46a 56.03a 48.53a BO 4.31d 2.59d 0.80d 51.25c 9.52b 30.83b 4.71c 4.96c 3.94a 56.01bc 58.99a 46.85a 120 5.1 9c 1.05c 2.94c 52.42bc 10.61a 29.70cd 5.33b 5.17bc 3.79a 53.84cd 52.22b 3B.28bc 160 5.49b 3.10b 1.13b 52.74bc 10.82a 29.76cd 5.70a 5.44ab 3.98a 54.81cd 52.26b 38.27bc 240 5.94a 1.20a 3.22a 54.00ab 10.86a 29.23d s.BBa 5.66a 3.88a 53.45d 51.41b 35.27c 'M.urrc in the same column within a variety and heading followed by the same letter are not statistically different (P > 0.05) by t multiple range test. 2 Abbreviations as defined in Table 1. 211 Table 5 Multiple Comparisons' of Protein Fractions' Determined by SE-HpLC for 1994 Spring Wheat Cultivars
N rate GLIP AGP IGP SGF GLIF AGF BW 90 0 11.52a 46.06a 12.41b 30.00a 1.21cd 4.84c 1.30d 3.1 5cd 40 11.25a 44.60a 14.12a 30.02a 1.11d 4.42d 1.4ocd 2.97d BO 11.37a 45.42a 13.20ab 30.01a 1.24c 4.95c 1.44c 3.27c 120 11.19a 44.99a 13.95a 29.BBa 1.36b 5.49b 1.70b 3.65b 160 11.54a 45.93a 13.41ab 29.12a 1.56a 6.20a 1 .B1ab 3.93a 240 .13.68a 11.40a 45.93a 28.98a 1.6'1a 6.48a 1.93a 4.09a Katepwa
U 11.82a 47.75a 12.09c 28.34a 1.25b 5.06b 1.28b 3.0Ocd 40 12.07a 46.75a 13.13b 2B.05ab 1.19b 4.63c 1.30b 2.78d BO 12.24a 47.89a 12.97bc 26.90abc 1.27b 4.98bc 1.35b 2.80d 120 12.14a 48.28a 14.08a 25.49c 1.44a 5.75a 1.68a 3.03bc 160 12.11a 48.52a 13.24ab 26.13bc 1.51a 6.06a 1.65a 3.27b 240 1 1.65a 47.10a 12.43bc 28.B2a 1.48a 5.98a 1.58a 3.66a AC Reed
0 . EÊ^ 16.33b 48.62a 14.72bc 20.33a 1.19c 1.07c 1.48c 40 16.57b 49.42a 16.07a 17.94b 1.18c 3.51c 1.14bc 1.27d
80 17.64a 50.70a 1 5.1 0abc 16.56c 1.36b 3.90b 1 .16b 1.28d 120 16.58b 51.14a 15.67ab 16.60c 1.41ab 4.35a 1.33a 1.41c 160 15.91b 50.04a 15.78a 18.27b 1.42ab 4.45a 1.40a 1.63b 240 15.94b 50.43a 14.68c 18.96b 1.45a 4.59a 1.34a 1.73a Roblin
0 1 0.1 5a 41 .B9a 12.94a 35.03a 1.12c 4.61c 1.42b 3.B5bc 40 1 0.61a 42.73a 11.93ab 34.72a 1.07c 4.32c 1.21c 3.51c BO 10.27a 42.56a 12.25ab 34.92a 1.14c 4.72c 1.36b 3.88b 120 10.21a 43.79a 11.67b 34.34a 1.28b 5.47b 1.46b 4.29a 160 10.45a 44.11a 12.87a 32.57a 1.39a 5.87ab 1.71a 4-33a 240 10.38a 43.98a 12.67ab 32.96a 1.41a 5.98a 1.72a 4.48a AG Taber 0 11.29b 42.46a 12.79a 33.45a 0.98cd 3.69cd 1 .11c 2.91b 40 12.38a 43.43a 13.81a 30.37b 0.95d 3.34d 1.06c 2.34d BO 12.33ab 43.90a '1.06c 13.36a 30.41b 3.78c 'r .1 5c 2.62c 120 12.35ab 43.92a 13.61a 30.1 2b 1.17b 4.17b 1.29b 2.B6bc 160 11.96ab 43.99a 13.48a 30.57b 1.24ab 4.58a 1.40ab 3.1 8a 240 12.18at: 44.60a 13.24a 29.98b 1.34a 4.91a 1.46a 3.30a ' M"uns in the same column within a variety and heading followed by the same letter are not statistically different (P > 0.05) by t multiple range test. : ' AGP and AGF the percentage of albumin-globulin in protein and flour, respectively; GLIp and GLIF : the percentage of gliadin in prãtein and floui, respectivèly; SGP and SGP : the páicentage of soluble glutenin in protein and flour, respectively; IGP and IGF : the percentage of insoluble gtuteiin in protein and floru, respectively. 212 Table 6 Multiple Comparisonsr of Protein Fractions' Determined by SE-HPLC for i995 Spring Wheat Cultivars
N rate SGP AGP SGF AGF IGF Glenlea
0 9.90a 44.01a 8.BBb 37.21a 0.92cd 4.09cd 0.83e 3.46d 40 9.81a 43.62a 11.50a 35.07ab 0.87d 3,BBd 1.02d 3.12e BO 10.04a 43.51a 10.77a 35.6Bab 0.99c 4.31c 1.07 3.53cd 120 10.36a 44.57a 11.05a 34.01b 1.16b 4.99b 1.24dc 3.81c 160 10.27a 43.96a 11.16a 34.61ab 1.24a 5.32ab 1.35b 4.19b 240 9.93a 42.63a 11.49a 35.95ab 1.31a 5.63a 1.52a 4.75a Katepwa
0 12.47a 49.95a 11.31c 26.27ab 1.27c 5.1 0c 'l .15d 2.68c 40 .1.30c 12.49a 49.71a 13.31ab 24.48b 1.22c 4.87c 2.40cd 80 12.04a 49.12a 14.09a 24.75b 1.25c 5.11c 1.47b 2.57cd 120 12.06a 48.90a 12.05c 26.99a 1.41b 5.72b 1.41bc 3.1 6b 160 11.92a 49.65a 13.15b 25.29ab 1 .51ab 6.31a 1.67a 3.21b 240 11.92a 49.43a 13.17ab 25.4\ab 1.61a 6.67a 1.78a 3.44a AC Reed 0 16.54b 49.90a 14.60a 18.96a 1 .19d 3.59e 1.05a 1.37bc EE 41^ 40 17.64ab 11 .01c 1 6.1 9b 1.23d 3.86d 0.77d 1 .13d 80 17.93a 54.90a 11.70cb 15.48bc 1.40c 4.28c 0.91c 1.21d 120 17.3gab 55.45a 12.15b 1 5.01c 1.50b 4.77b 1.05ab 1.29c 160 17.26ab 54.97a 11.54cb 16.23b 1.48b 4.73b 0.99b 1.40b 240 17.51ab 54-B6a 11.31c 16.32b 1.66a 5.21a 1.07a 1.55a Roblin
0 10.64a 44.62a 12.03ab 32.71a 1.09d 4.55c 1.23c 3.34de 40 '10.80a 45.03a 11.76b 32.42a 1.10d 4.59c 1.20c 3.31e 80 10.82a 45.77a 12.20ab 31 .21a 1.27c 5.36b 1.43b 3.65cd 120 10.58a 45.29a 11.B8ab 32.25a 1.30bc 5.57b 1.46b 3.97bc 160 10.58a 46.1 6a 12.80a 30.45a 1.41b 6.14a 1.70a 4.05b 240 10.72a 45.86a 12.54ab 30.BBa 1.53a 6.56a 1.79a 4.42a AC Taber
0 11.92a 44.04a 12.15b 31.89a 1.04d 3.83cd 1.06d 2.77b 40 12.58a 44.48a 14.01a 2B.93bc 0.99d 3.51d 1.11cd 2.29c '13.63a BO 12.59a 45.24a 28.55c 1.06cd 3.B0cd 1.14cd 2.40c 120 12.31a 44.57a 12.98a 30.1 4abc 1.14bc 4.1Sbc 1.21bc 2.80b 160 12.31a 44.66a 13.90ab 29.1 3bc 1 .1 9ab 4.33b 1.35a 2.83b 240 11.77a 44.67a 12.14b 31.43att 1.25a 4.73a 1.29ab 3.33a ' Means in the same column within a variety and heading followed by the same lefter are not statistically different (P > 0.05) by t multiple range test. 2 Abbreviations as def,ined in Table 5. 213 t Table 7 Multiple Comparisonsr ff Protein Fractions Determined by SE-HPLC for 1994 Winter Wheat Cultivars
N rate SGP GLIP AGP IGP SGF GLIF AGF IGF s86-375
0 1 1 .91b 47.84a 11.16b 29.10a 0.94c 3.78c 0.BBbc 2.30ab 40 12.57b 47.23a 10.65b 29.55a 0.94c 3.54c 0.80d 2.22bc '10.69b BO 12.80b 47.63a 28.89a 0.99c 3.67c 0.82cd 2.22bc 120 14.22a 49.74a 11.07b 24.97b 1 .19b 4.18b 0.93b 2.10c 160 14.07a 50.25a 12.56a 23.12b 1.28b 4.57a 1.14a 2.10c 240 13.96a 49.66a 12.01a 24.36b 1.37a 4.87a 1.18a 2.39a s86-1 01
0 12.67ab 47.61a 10.44bc 29.2Ùabc 1.18d 4.43c 0.97d 2.72c 40 12.22b 46.32a 10.07c 31.39a 1.06e 4.03d 0.BBe 2.73bc BO 12.64ab 46.46a 1 1.10b 29-B0ab 1 .1 3de 4.1 3dc 0.99d 2.65c 120 12.89ab 47.49a 10.52bc 29.1 0bc 1.33c 4.89b 1.08c 3.00a 160 13.32a 48.61a 10.87b 27.20cd 1-441) 5.25a 1.17b 2.94ab 240 13.57a 47.2Ba 13.14a 26.02d 1.55a 5.39a 1.50a 2.97a CDC Kestrel 0 12.7jbc 47.58a 10.1Aab 29.59b 1 .16d 4.33c 0.92c 2.69bc 40 12.37c 45-54a 9.93b 32.16a 0.99e 3.64d 0.79d 2.57cd BO 1 3.1 Oabc 46.87a 10.43ab 29.60b 1.09d 3.89d 0.87c 2.46d 120 13.46ab 48.42a 10.78a 27.34c 1.27c 4.55bc 1.01b 2.57cd 160 1 3.50abc 48.20a 10.49ab 27.81bc 1.35b 4.82b 1.05b 2.78b 240 13.17a 47 .71a 10.21ab 28.91bc 1.45a 5.25a 1.12a 3.1 8a Norstar
0 10.67a 44.41a 9.69b 35.23a 1.o8cd 4.49c 0.98cd 3.56ab 40 10.99a 44.30a 10.05ab 34.66ab 0.93e 3.77d 0.85e 2.95de BO 11.39a 44.96a 10.84a 32.B1abc 0.9Bde 3.87d 0.93de 2.82e 120 1'r.58a 45.67a 10.78a 31.97bc 1 .1 5bc 4.52bc 1.07bc 3.1 6cd 160 11.49a 45.83a 10.80aa 31.89c 1.23b 4.90b 1 .1 6ab 3.41bc 240 11.58a 46.52a 10.22ab 31.68c 1.35a 5.44a 1.20a 3.71a Winalta
0 1 0.1 5c 44.47a 10.44a 34.94a 1.06b 4.62c 1.09bc 3.63a 40 10.38bc 43.99a 10.26a 35.37a 0.94c 4.00d 0.93d 3.22c BO 11.00abc 45.23a 11.06a 32.71ab 1.01bc 4.1 6d 1.02cd 3.01c 120 11.79a 46.38a 10.Bga 30.94b 1.25a 4.92bc 1.15b 3.2Bbc 160 11.23ab 46.56a 10.52a 31.68b 1.27a 5.26ab 1.19b 3.5Bab 240 11.24ab 46.1 9a 11.03a 31.54b 1.34a 5.50a 1.31a 3.75a I' Means in the same colurnn within a variety and heading followed by the same letter are not statistically different (P > 0.05) by t multiple range test. 2 Abbreviations as defined in Table 5. 214
Table 8 Multiple Comparisonsr of Protein Fractions' Determined by SE-HPLC for 1995 Winter Wheat Cultivars
N rate SGP GLIP AGP IGP SGF GLIF AGF IGF s86-375 a aa^ 0 12.30b 45.60a 13.70c 28.39a 1 .01c 3.74bc 1.12b 40 13.29a 45.06a 14.36bc 27.30ab 0.88c 2.97e 0.95c 1.80b BO 13.87a 46.06a 13.64c 26.43bc 0.98d 3.27d 0.97c 1.88b 120 13.50a 46.15a 15.45a 24.90cd 1.03c 3.51cd 1.17b 1.89b 160 13.98a 47.93a 15.25ab 22.85e 1.16b 3.98b 1.27a 1.90b 240 13.98a 48.12a 13.86c 24.05de 1.29a 4.43a 1.27a 2.21a s86-101
0 13.61ab 46.45a 1 3.90ab 26.04bc 1.18b 4.04c 1.21b 2.27c 40 14.49a 46.04a 13.28b 26.1 9abc 1.03c 3.27e 0.94d 1.86d BO 13.6Bab 45.57a 13.12b 27.63ab 1.11bc 3.69d 1.06c 2.24c 120 14.30a 46.63a 14.49a 24.57c 1.33a 4.34bc 1.35a 2.29c 160 13.66ab 46.36a 13.62ab 26.37abc 1.28a 4.36ab 1.28ab 2.48b 240 13.14b 45.45a 13.33b 28.08a 1.34a 4.64a 1.36a 2.86a CDG Kestrel 0 12.34c 43.0Bbc 12.63cd 31.96a 0.99c 3.45c I .01d 2.56b 40 12.B7bc 43.04c 12.08d 32.01a 0.85d 2.84e 0.80e 2.11c 80 13.26ab 43.46bc 14.19a 29.09b 0.98c 3.22d 1.05cd 2.15c 120 13-92a 45.B2ab 13.43abc 26.83c 1.14b 3.76b 1.10c 2.20c 160 11.02d 42.65c 13.38bc 32.95a 0.98c 3.80b 1.19b 2.93a 240 1 3.61ab 46.31a 13.7Aab 26.34c 1.32a 4.49a 1.33a 2.56b Norstar
0 1 1.55abc 44.65a 12.68b 31 .12ab 1.04b 4.02b 1.14c 2.80b 40 11.94a 43.80a 13.72ab 30.54ab 0.82c 3.02c 0.95d 2.11d BO 10.62c 39.65a 12.61b 37.12a 0.80c 2.97c 0.95d 2.78b 120 11.71ab 44.82a 14.67a 28.7gbc 1.03b 3.94b 1.29b 2.53c 160 11.87a 45.31a 14.83a 28.00c 1.17a 4.49a 1.47a 2.77bc 240 10.B2bc 43.72a 13.48b 31.99a 1.16a 4.68a 1.44a 3.42a Winalta 0 9.91b 44.33ab 10.70ab 35.05ab 0.92c 4.12c 0.99b 3.26bc
40 1 0.1 4ab 43.02b 1 0.06bc 36.78a 0.79d 3.36d 0.78c 2.87d BO 1 0.31ab 43.15b 11.00a 35.53ab 0.87cd 3.62d 0.92b 2.98cd 120 10.98a 46.61ab 9.55c 32.B7bc 1.09b 4.61b 0.94b 3.25bc 160 10.65ab 45.7Sab 9.32c 34.28abc 1.11b 4.76b 0.97b 3.57a
240 10.97a 47.57a 1 0.1 3abc 31.33c 1.21a 5.23a 1 .11a 3.45ab t M.un, in the same column within a variety and heading followed by the same letter are not statistically different (P > 0.05) by t multiple range test. 2 Abbreviations as defined in Table 5. 275 Table 9 Multiple Comparisons' of HMW/LMW Glutenin Subunit Ratio Determined by RP-HPLC for 1995 Spring and Winter Wheat Cultivars
and N Rate and N Rate Glenlea GDC Kestrel 0 0.59ab 0 0.39ab 40 0.58b 40 0.36b 80 0.60ab 80 0.37b 120 0.63ab 120 0.38b 160 0.66a 160 0.44a 240 0.66a 240 0.40ab Katepwa Norstar 0 0.40c 0 0.47bc 40 0.42c 40 0.42d BO 0.42bc 80 0.42d 120 0.49ab 120 0.44cd 160 0.50a 160 0.48ab 240 0.53a 240 0.52a AC Reed s86-375 0 0.36c 0 0.57a 40 0.39bc 40 0.47b 80 O.42ab 80 0.46b 120 0.43ab 120 0.47b 160 0.44ab 160 0.47b 240 0.45a 240 0.48b Roblin s86-101 0 0.53bc 0 0.41ab 40 0.50c 40 0.35c BO 0.54bc 80 0.37bc 120 0.57abc 120 0.38bc 160 0.60ab 160 0.40abc 240 0.64a 240 0.43a AC Taber Winalta 0 0.47a 0 0.49ab 40 0.45a 40 0.45b 80 0.46a 80 0.43b 120 0.46a 120 0.45b 160 0.48a 160 0.48ab 240 0.47a 240 0.52a 'M"un, in the same column within a variefy and heading folrowed by the same letter are not statistically different (P > 0.05) by t multiple range test. 216
Table 10 Quality Data¡ for 1994 Spring V/heat Cultivars
PSI MDT ETP MTI FAB BW 90 0 59.8 10.5 60 4.47 29.5 + t.¿ 2.5 1.0 7.5 25 55.6 9.81 40 59.2 qà 9.9 4.00 26.9 41.7 2.0 1.5 5.5 25 55.6 11.83 80 57.2 10.9 59 3.27 27.0 50.6 5.5 1.5 11 .5 15 57.0 11.60 120 57.1 12.2 59 2.60 21.9 51.8 6.5 'E 10.5 30 58.2 11.42 160 56.7 13.5 64 2.07 18.0 56.4 6.0 3.0 7.0 25 59.2 10.79 240 57.9 14.1 64 2.20 19.8 56.2 6.5 3.5 8.0 40 59.7 8.90 Katepwa 0 60.6 tro 10.6 3.40 20.6 37.9 2.0 1.0 2.5 50 55.7 9.10 40 58.4 qJ 9.9 3.53 21 .3 36.9 2.0 1.5 1.5 60 56.1 10.93 BO 58.9 10.4 70 2.33 15.2 40.5 2.5 1.5 3.5 40 56.3 11.12 120 59.1 11.9 56 2.60 18.5 43.3 4.5 2.0 6.0 50 57.1 10.24 160 EO 59.1 12.5 1.87 14.7 49.9 4.0 2.0 s.0 45 57.8 9.63 240 58.1 12.7 59 2.07 16.2 49.9 4.0 2.0 5.5 40 57.9 8.36 AC Reed 0 75.2 38 1.80 6.3 20.4 1.5 1.0 1.0 175 47.3 9.27 40 74.5 7.1 JJ 1.33 4.5 18.6 1.0 0.5 1.0 200 47 .5 10.38 BO 74.4 7.7 1.33 5.7 22.7 1.0 1.0 0-5 190 48.7 10.08 120 72.0 8.5 43 0.73 3.3 22.8 1.0 0.5 1 .0 180 49.6 9.49 160 75.5 8.9 51 1.20 6.6 27.0 1.0 0.5 1 .0 185 49.6 9.31 240 73.7 9.1 52 1.13 6.3 27.5 1.5 1.0 1.0 175 49.6 8.66 Roblin 0 65.8 oô 11.0 4.13 28.9 44.4 7.0 1.5 10 55.4 7.12 40 64.9 10.1 76 3.33 22.7 43.8 5.5 1.0 13.0 15 55.8 7.53 80 63.6 11.1 76 2.93 24.1 52.0 6.5 2.0 10.0 35 56.5 7.73 120 62.3 12.5 81 2.87 25.6 54.5 9.0 4.0 t3 59.6 7.46 160 63.4 '13.3 B4 2.67 24.8 57.6 8.5 4.5 7-5 25 59.2 7.34 240 61 .2 oo 13.6 2.53 23.5 59.4 7.5 3.5 13.0 15 59.3 6.73 AC Taber U 64.4 8.7 56 4.67 22.7 30.4 f.3 1.0 2.5 60 51.6 1 0.10 40 63.8 7.7 51 3.87 17.6 28.2 1.5 1.0 2.5 60 51 .6 12.21 80 62.4 8.6 60 3.27 17.5 34.0 2.0 1.0 5.0 40 52.7 11 .77 120 62.1 9.5 63 2.73 16.0 36.9 5.0 1.0 6.5 65 53.0 't 1.59 160 62.2 10.4 55 3.07 19.1 39.4 5.0 1.5 7.0 55 53.3 11.17 240 61.1 11.0 lf) 2.73 18.4 42.3 5.5 3.0 5.5 55 53.5 10.16 'PSI = particle size index (%);Pro: flour protein content (%); : : SDSV SDS sedimentation volume (mL); MDT mixograph mixing development time (min); ETP : mixograph energy to peak (newton.meter (Nm)); TEG: mixograph total energy (Nm); DDT: farinograph dough developmenf dme i-i"¡;enf : farinograph arival time (min); STA : farinograph stability (min); MTI : farinograph mixing tolerance index (BU); FAB : farinograph absorption (%); FSp : flour swelling power (g/g). 217
Table 11 Quality Datat for 1995 Spring Wheat Cultivars
PSt Pro SDSV MDT TEG DDT ART STA MTI FAB FSP Glenlea 0 56.7 9.3 59 8.60 44.3 52.2 2.00 1.0 2.0 65 55.8 9.84 40 56.6 8.9 52 8.80 55.3 63.9 1.50 1.5 1.5 70 55.5 10.65 80 58.7 9.9 65 2.87 18.6 39.9 2.00 1.0 3.0 40 56.1 10.29 120 55.8 11.2 67 4.13 32.8 82.4 8.50 t.3 13.5 20 57.5 10.41 160 55.8 12.1 tl 3.87 30.7 83.8 10.00 2.5 14.0 20 57.9 9.50 240 57.4 7tr, ÀÊ 13.2 3.47 33.7 98.6 9.50 12.0 20 58.2 8.39 Katepwa
U 61.8 q) 10.2 2.60 16.4 39.9 2.00 1.5 3.0 30 57.7 9.58 40 58.4 9.8 51 3.07 18.3 37.0 1.50 1.0 2.0 50 58.4 10.95 BO 58.9 10.4 55 2.73 16.9 3B.B 2.00 1.5 4.5 35 58.7 10.86 120 56.3 11.7 't6.7 54 2.13 46.8 4.00 2.5 5.0 40 61 .2 9.86 160 54.5 12.7 55 1.60 12.6 50.5 4.00 2.5 4.0 55 60.5 9.78 240 56.2 13.5 66 1.47 12.8 53.3 4.00 2.5 4.5 50 62.1 8.68 AC Reed 0 73.7 7.2 31 1.60 5.0 17.1 1.00 1.0 0.5 175 48.5 8.16 40 72.2 7.0 aa 1-80 5.6 17.1 1.00 0.5 1.0 170 48.9 8.65 BO 69.3 7.8 1.27 4.7 19.2 1.00 0.5 1.0 175 49.8 8.21 120 69.2 8.6 40 0.93 4.0 21 .7 1.00 1.0 0.5 155 51 .7 8.26 160 68.5 8.6 1.07 5.0 23.0 1.50 1.0 1 .00 165 51.1 7.93 240 68.9 9.5 55 '1.0 1.07 7.2 32.7 1.50 1 .0 180 51.5 7.25 Roblin 0 66.3 10.2 õt 3.80 26.1 42.7 5.00 1.0 8.5 25 57.9 8.95 40 61.3 10.2 bb 2.80 17.8 42.3 6.00 2.0 9.0 25 58.5 9.55 80 63.2 11.7 77 2.87 23.2 50.3 6.00 2.5 8.5 30 59.4 9.36 120 61.0 12.3 2.00 17.7 55.9 6.00 4.0 7.0 35 6'1.1 9.11 160 59.1 13.3 B1 2.20 21 .6 57.8 7.00 4.0 7.5 30 61 .7 B.5B 240 61.B 14.3 B5 2.07 21 .6 62.4 7.00 4.0 8.5 30 61.8 7.96 AG Taber 0 62.2 8.7 55 4.00 20.6 32.0 2.00 1.0 2.0 45 54.8 10.06 40 60.0 7.9 50 3.60 15.7 28.0 1.50 1.0 3.0 50 54.1 10.80 80 60.0 ó-4 55 5.67 39.2 71.7 2.00 1.0 5.0 40 54.4 10.27 120 58.2 ÈÀ 9.3 3.1 3 18.2 35.2 2.00 1.0 4.5 60 55.4 10.66 160 58.6 qn 9.7 61 2.87 17.4 37.6 4.00 1.5 60 55.8 10.78 240 60.0 10.6 Ió 2.33 16.2 43.5 4.50 2.0 5.5 60 56,1 9.'19 I Abbreviations as defined in Table 10. 2t8
Table 12 Quality Datar for 1994 Winter Wheat Cultivars
PSI SDSV MDT TEG DDT ART STA MTI FAB FSP s86-10'l 70.3 0 9.3 bv 2.87 15.3 33.2 3.0 1.50 6.0 25 52.5 8.55 '18.1 40 73.3 8.7 oz 4.20 26.3 t-3 1.00 3.5 40 51.9 9.62 70.6 8.9 80 67 3.20 15.1 29.3 2.0 1.00 3.0 40 51 .6 9.32 tq 120 69.5 10.3 2.93 16.0 34.0 2.5 1.50 6.0 20 53.0 9.55 160 68.4 10.8 BB 2.40 15.6 39.8 4.5 1.50 6.5 40 54.2 9.49 67.9 11.4 240 92 2.53 17.8 42.3 5.0 2.00 7.5 35 55.5 7.BB s86-375 77.8 70 0 43 4.13 15.9 23.8 t.3 1.0 1.0 60 49.5 9.25 77.6 Àa 40 7.5 0.47 0.96 14.0 1.0 0.5 1.5 B5 47.3 10.02 76.5 7.7 47 80 4-80 13.8 17 .7 1.5 1.0 1.0 BO 48.0 9.60 76.2 8.4 49 120 3.07 11.2 22j t-3 1.0 1.5 50 47.9 9.5'1 AO 160 75.4 9.1 3.00 17.1 35.3 2.0 0.5 4.0 80 48.9 9.67 75.9 240 9.8 63 2.20 11.1 29.5 3.0 1.0 4.5 75 49.6 8.53 GDC Kestrel
0 69.6 Ll 74 3.27 17.9 34.3 2.5 1.0 5.5 35 52.4 7.38 71.0 40 8.0 õt 4.20 19.0 27.6 1.5 1.0 2.0 60 52.2 7 72 67.4 B0 8.3 67 2.87 11.6 25.3 1.5 1.0 2.0 65 51 .7 7.63 120 69.4 9.4 BO 2.93 16.0 34.2 2.5 1.0 5.5 30 52.3 7.55 oa 160 67.7 10.0 2.33 10.4 26.4 3.0 1.5 7.0 20 53.0 7.26 240 68.1 11.0 93 2.67 17.9 42.3 5.0 2.0 8.0 30 54.1 7.01 Norstar 67.2 10.1 76 0 4.07 23.8 36.6 3.0 1.5 9.0 15 52.9 B.B4 40 69.0 8.5 63 5.53 19.2 20.9 1.5 'r.0 2.0 70 51.6 10.48 66.5 B0 8.6 õt 5.60 23.6 25.4 1.5 1.0 2.0 60 51.5 10.29 67.5 oo 120 4.60 23.4 31.7 2.5 1.5 7.0 30 52.3 10.07 160 65.2 10.7 62 3.93 22.5 35.7 5.5 1 .5 10.5 20 52.4 9.59 68.0 11.7 92 240 3.07 19.B 41 .5 7.0 3.0 10.5 25 54.1 B.1B Winalta 68.6 0 10.4 71 5.07 34.7 41.6 2.5 1.0 5.5 30 55.2 8.34 67.6 9.1 40 65 4.87 22.9 28.6 2.0 1.0 2.5 50 54.1 9.63 80 66.1 9.2 6B 5.80 32.9 34.1 2.0 1.5 2.0 50 54.6 9.70 66.4 120 10.6 79 4.33 26.3 37.4 3.0 1.0 8.0 20 54.4 9.13 66.0 11.3 160 84 3.33 22.9 43.4 4.0 1.5 8.5 20 55.5 ô-Õz 67.3 240 11.9 87 2.93 21.8 47.2 6.5 2.0 11.5 20 56.7 8.19 I Abbreviations as defined in Table 10. 219
Table l3 QualityData' for 1995 Winter Wheat Cultivars
PSI Pro SDSV MDT EÏP ÏEG DDT ART STA MTI FAB FSP s86-1 01 0 72.2 QA '1.0 62 3.47 15.5 27.9 2.0 5.5 35 52.5 7.61 40 72.1 7.1 43 6.00 13.6 1 3.6 1.0 0.5 1.5 65 50.9 8.25 BO 71.8 8.1 54 3.93 14.3 22.6 1.5 0.5 3.5 45 52.3 7.90 120 68.2 9.3 61 2.93 12.5 27.8 4.5 t.c 8.0 30 53.1 7.75 160 69.0 9.4 64 2.67 14.6 33.3 3.0 1.0 5.0 45 53.1 7.83 240 69.0 10.2 75 2.73 16.7 37.2 5.0 2.0 7.0 30 53.7 7.45 s86-375
0 79.2 8.2 40 4.60 15.5 20.9 1.0 0.5 1.5 60. 50.5 9.79 40 704 Ò1 o.ô 0.40 0.59 9.2 1.0 1.0 1.0 85 48.4 11.95 BO 78.8 7.1 35 5.53 12.3 13.4 1.0 0.5 1.5 75 49.3 12.23 120 78.8 39 '/.t) 4.27 12.4 18.1 1.0 1.0 1.0 70 49.4 12.48 '160 76.3 8.3 50 3.27 11.7 22.7 1.0 0.5 1.0 60 49.7 11.45 240 76.5 9.2 59 2.67 12.4 27.9 3.5 1.0 5.0 B0 50.6 10.09 GDC Kestrel
0 70.6 8.0 59 4.40 18.7 25.9 1.5 0.5 2.5 55 53.2 9.64 40 74.1 O.C) 40 3.13 7 .8 15.4 t.3 1.0 1.5 75 50.9 10.83 80 72.5 7-4 48 4.60 16.3 21 .6 1.5 1.0 2.0 60 52.6 11.15 120 66.4 56 3.87 16.9 27 .1 2.0 1.0 3.0 40 53.3 10.64 160 66.7 8.9 61 3.07 19.0 39.1 2.0 1.0 4.5 30 s3.3 9.83 240 65.8 9.7 3.07 17.5 36.4 4.5 1.5 6.5 20 55.0 8.87 Norstar
0 67.9 9.0 60 5.67 22.3 23.8 1.5 1.0 1.5 50 52.9 9.24 40 71.7 6.9 42 4.27 9.9 14.2 1.5 1.0 1.0 85 51.2 11.06 80 67.9 7.5 54 0.60 1.6 20.9 1.5 1.0 1.0 B0 52.9 10.94 65.4 oo 120 41 4.20 1B.B 28.5 2.0 1.0 3.0 40 53.3 11.23 64.8 oo 160 72 4.40 24.5 34.5 3.0 1.0 8.5 20 53.5 10.61 240 63.0 10.7 BO 3.33 20.1 39.3 6.0 2.0 11.0 10 55.4 9.15 Winalta o2 0 69.4 60 4.Ot 22.3 29.3 2.0 1.0 2.5 40 54.9 9.84 40 71.3 t-o 47 4.20 16.0 23.1 t.J 1.0 1.5 53.9 1'1.38 80 68.9 8.4 55 6.00 28.5 28.5 2.0 1.0 2.0 60 55.6 11.33 oo 120 64.4 62 5.20 28.6 33.6 3.0 1.5 7E 20 56.0 11.29 160 63.7 10.4 65 3.80 23.2 38.6 2.5 1.0 7.0 20 55.9 11.44 240 64.9 11.0 67. 4.00 27.0 41.8 5.5 1.5 11.5 10 57.1 9.45 I Abbreviations as defined in Table 10. 220 Table 14 Multiple Comparison' of Flour Color for Three 1995 Spring, Three Winter Wheat, Three 1994 Spring and Three Winter Wheat Cultivars
Variety N (ks/ha)
AC Reed 0 91.Bga -1 .75c 6.48b 92.02a -1 .B4d 6.44e AC Reed 40 91 .BSab -1 .76c 6.61a 9r.86b -1.85d 6.60c AC Reed 80 91.49d -1.72c 6.48b 91.83c -1 .80c 6.67a AC Reed 120 91 .55c -1 .6'1b 6.49b 91.82c -1 .70b 6.63b AC Reed 160 91.59c -1.58b 6.38c 91.66d -1 .67a 6.44e AC Reed 240 91.84b -1 .47a 6.1 9d 91 .63e -1 .65a 6.57d
Roblin 0 89.78c -1 .4Bc 7.52a 91.14c -1 .50c 7.63a Roblin 40 89.66d -1 .45c 7.42b 91.02d -1.48c 7.53b Roblin B0 90.33b -1.33b 7.38d 91 .70b -1 .35b 7.49d Roblin 120 89.45e -1.31b 7.40c 90.81e 1.33b 7.51c Roblin 160 90.79a -1.26a 7.43b 92.17a -1 .2Ba 7.55b Roblin 240 89.32f -1 .25a 7.43b 90.68f -1 .27a 7.55b
Katepwa 0 91.36a -1.64d 8.50e 90.43b -1.60d 8.40d Katepwa 40 91 .21b -1.65d 8.50e 90.36c -1 .61d 8.55b Katepwa 80 90.06c -1.53c 8.66a 90.36c -1 .56c 8.68a Katepwa 120 89.93d -1-41b 8.53d 90.32d -1.49b 8.46c Katepwa 160 89.47t -1 .2Ba 8.61b 90.13e -1 .41a 8.40d Katepwa 240 89.88e -1-27a 8.57c 90.54a -1.42a 8.37e
sB6-375 0 92.04f -1 .59b 6.1 5a 91.95d -1.60a 6.04f sB6-375 40 92.93a -1.55a 6.07c 92.1 8b -1.74d 6.47c sB6-375 B0 92.72b -1 .71d 5.84e 92.31a -1 .77d 6.25e sB6-375 120 92.64c -1 .69cd 5.96d 92.12c -1.75d 6.63b sB6-375 160 92.48d -1 .66c 6.1 5a 91.70f -1 .69c 6.82a s86-375 240 92.40e -1.59b 6.1 0b 91.88e -1.65b 6.32d
Winalta 0 91.63b -1.59d 7.03d 91 .1 6d -1.51a 7.28f Winalta 40 91.69a -1 .63e 7.00e 91 .37b -1 .66c 7.44e Winalia 80 91.67a -l.60d 7.11c 91 .28c -1.65c 7.46d Winalta 120 91 .42d -1.52c 7.16b 91 .09e -1.66c 8.00a Winalta 160 91 .42d -1.47b 7.19a 92.30a -1.59b 7.68b Winalta 240 91.56c -1.38a 6.99e 90.95f -1.52a 7.62c
Norstar 0 91 .1 5f -1 .61c 7.46e 91.37d -1.52b 7.38f Norstar 40 92.09a -1 .79f 7.52b 91.53b -1 .86e 8.01a Norstar B0 91.79b -1.74e 7.51b 91.59a -1.82d 7.91b Norstar 120 91.65c -1 .67d 7.58a 91 .16e -1 .65c 7.85c Norstar 160 91.56d -1.51b 7.30d 91 .40c -1.63c 7.80d Norstar 240 91.39e -1 .35a 6.97e 91.04f -1.46a 7.59e
Udon 92.46 -2.03 7.30 tM"urrs in the same colun'm within a variety and heading followed by the same letter are not statistically different (P > 0.05) by t multiple range test. 221 Table 15 Multiple Comparisonsr of Noodle Color for 95 Spring Wheat Cultivars
0hr sample 2hr 24h 24 hr and cooked L* a* b* L* a* b* a* b* L" b* Glenlea
0 86.78a 0.33c 1 1.90c 83.1Ba 0.58c 14.60d 77.82ab 1.43d 16.79de 78.47c -0.72d 14.43c 40 87 .17 a 0.1 9d 83.6Ba 12.02c 0.40d 14.48d 78.55a 1.18e 16.58c 78.9'l b -0.87d 1 3.90d BO 85.26b 0.41c 13.22b 82.12b 0.62c 15.67c 77.17b 1.50c 17.54cd 7\.70bc -0.62d 14.69c 120 85.20b 0.60b 13.24b 81.07c 0.79b 16.53bc 75.27c 1.90b 'lB.34bc 79.46a 0.19c 15.70a 160 0.66b 84.51c 13.92ab 80.41d 0.83b 17.06b 74.80c 1 .94b 1B.53ab 78.62bc 0.57b 15.17b 240 1.07a 83.28d 14.57a 78.91e 1.30a 18.02a 72.BBd 2.65a 19.24a 77.91d 1.11a 1S.36b Roblin rì 86.41a -0.06e 13.50bc 82.49b 0.53e 17.60d 78.10b 1.57d 18.96d 80.48b -0.26c 15.97a 40 86.6Ba -0.06e 13.14c 82.84a 0.36f 17.78d 78.73a 1.23e 19.64c 81.26a -0.57d 15.76a 80 84.71b 0-31d 13.B7bc 80.51b O.BBd 1 8.97c 75.87c 1 .91c 20.83ab 80.15b -0.12c 15.65ab 120 84.50b 0.40c 14.26ab 80.33c 1.07c 18.98c 75.62c 2.23b 20.51b 80.09b 0.47b 15.53ab 160 83.56c 0.60b 14.BBa 79.52c 1.31b 't 9.7Bb 75.02d 2.37b 21 .14a 80.21b 0.52b 15.3ibc 240 82.69d 0.96a 1 78.10d 5.01a 1.85a 20.45a 73.53e 3.02a 20.98ab 79.06b 1 -21a 15.06c Katepwa
0 86.97b 0.45e 15.54b 83.93b 0.68e 20.17cb 80.39a 1.50e 22.72c 83.36a -0.60e 19.24a 40 87.50a 0.43e 14.78c 84.57a 0.67e 19.30d 80.45a 1 .73d 21.56d 83.25a -0.48e 18.72b BO 86.55bc 0.70d 15.46bc 83.72b 0.98d 19.74cd 79.04b 2.13c 22.60c 83.00ab -0.14d 18.10c 120 86.13c 0.95c 15.58b B2.9Bc 1.26c 20.63b 77.98c 2.55b 23.46bc 82.31bc 0.45c 18.90b '160 84.50d 1.24b 17.08a 81 .21d 1.60b 22.14a 75.77d 3.27a 23.71ab 82.13c 0.64b 17.4Sd 240 84.43d 1.41a 17.17a 80.97d 1.83a 22.50a 75.91d 3.27a 24.33a 81 .74c 0.89a 17.68d AG Reed 0 0.42e '15.18cb 86.28a 83.54b 0.84e 18.01e 78.01c 2.63e 1 9.31 b 82.79b -0.18d 17 .45a 40 86.67a 0.30f 14.74c 84.28a 0.76f 17.32d 79.33a 2.S6e 1B.46b 84.28a -0.45e 16.52d 80 86.35a 0.54d 14.87c 83.42b 1.04d 17.71cd 78.41b 2.82d 18.66b 83.08b 0.09c 16.4Td 120 84.99b 0.91b 15.94ab 82.03d 1 .46b 18.86b 76.37e 3.54b 20.21a 82.21c 0.40b i7.13b 160 85.48b 0.81c 16.10a 82.58c 1 .21c 19.73a 77 .50d 3.1 6c 20.71a 82.93b 0.14c 16.90c 240 1.17a 84.14c 16.41a B0.BBe 1 .74a 19.95a 75.40f 3.68a 20.91a 81 .22d 0.73a 17.00cb AC Taber
0 86.96b 0.82c 13.63bc 84.17c 1 .14c 17 .4Bc 80.13b I .96c 19.23c 82.18cd 0.05bc 1B.50ab 40 87.91a 0.64e 12.95c 85.37a 0.87e 16.83d 81.69a 1.50e 18.26d 83.12a -0.28c 18.22c BO 0.75d 87.17b 13.50bc 84.53b 1.02d 17.50c 80.27b 1.70d 1 9.61c 82.48b -0.20c 1B.34bc 120 86.90b 0.93b 13.37c 84.00c 1.29b 17.39c 79.35c 2.11b 19.60c B1 .95d 0.35ab 1 B.sSa 160 86.56c 0.97b 14.07ab 83.68d 1.33b 18.50b 79.05d 2.17b 20.68b 82.24bc 0.48a 18.56a 240 85.13d 1 .29a 14.71a 81 .79e 1.72a 19.57a 76.59e z.BSa 21 .55a 81.17e 0.61a 1 8.01d
udon 88.85 -0.68 17.62 86.71 -0.65 21 .72 83.71 _0.40 24.45 85.28 -2.05 23.85 'M.un, in the same column within a valiety and heading followed by the same letter are no, uuur,r.ully different (P > 0.05) by t multiple range resr. 222 Table 16 Multiple Comparisons' of Noodle Color for 95 Winter'Wheat Cultivars
0hr Sample 2hr 24h 24 hr and cooked L* a* b* L* a* b* L" a* b" L* a* b* CDC Kestrel 0 86.40d 0.06c 16.52bc 83.03d 0.36c 21.32c 79.32d 0.98c 22.97c 83.23b -1.18b 20.Tic 40 88.41a -0.37e 15.52d 85.77a -0.07f 18.82e 82.36a 0.45f 20.65e 84.28a -1.63c 20.b0c 80 87.90b -0.23d 16.09c 85.03b 0.04e 20.19d 81.64b 0.56e 21.90d 84.37a -1.61c 20.61c 120 87.09c 0.03c 16.99b 83.85c 0.30d 22.16b 80.13c 0.86d 24.09b 83.43b -1.04b 21.92a 160 86.20d 0.32b 17.95a 82.71e 0.70b 23.20a 78.77e 1.38b 24.65ab 82.48c -0.50a 21.46b 240 85.86e 0.52a 17.84a 82.31f 0.86a 23.40a 78.48e 1.56a 24.98a 83.00bc -0.46a 20.59c Norstar 87.15d 0 0.47c 13.85c 83.81d 0.85c 18.05e 79.80c 1 .61c '19.79b 82.97c -0.16c 18.19a 88.70a 40 0.14e 13.36c 85.79a 0.44e 16.86f 82.14a 1.23e 18.25c 84.57a -0.79e 1T.S7b 80 87.71b 0.23de 14.87b 84.78b 0.51d 18.46d 80.91b 1.33d 19.78b 84.51a -0.76e 17.02d 120 87.44c 0.31d 15.23b 84.37c 0.52d 19.67c 80.57b 1.26de 21.06a 84.65a -0.60d 1T.9Ba '15.81a 160 86.02e 0.81b 82.70e 1.04b 20.66b 78.85d 1.89b 21.42a 83.15b 0.08b 1T.6sb '1.60a 240 85.17f 1.23a 15.84a 81.48f 21.09a 77.63e 2.53a 21.43a 82.40d 0.65a 17.28c s86-375 85.88c 0 0.55c 14.78ab 82.36d 0.74c 17.51c 77.52d 1.92c 1 8.1 9c 80.39d 0.20b 17.11ab 88.01a 40 0.18e 12.79b 85.18a 0.48f 14.84e 81.82a 1 .52d 15.68e 83.68a -0.42d 16.75bc '16.40d 80 87.17b 0.24e 14.29ab 84.27b 0.55e 80.55b 1.65d 17.23d 83.48a -0.34d 16.51c 120 86.53bc 0.44d 14.63ab 83.33c 0.67d 17.68c 79.37c 1.88c 18.75c 82.83b -0.16c 17.43a 85.04d 160 0.70b 14.49ab 81.86e 0.96b 19.58b 77.53d 2.43b 19.83b 81 .86c 0.24b 17.20a 240 83.93e 0.98a 16.64a 80.67f 1.26a 20.48a 75.87e 2.79a 20.81a 80.86d 0.58a 17.08ab s86-101 '1.06b 0 85.54de 0.75b 15.11cd 82.24e 20.17c 77.86d 2.03a 21.62a 82.68e -0.37c 18.65a 't7.09e 40 87.77a 0.46d 13.22e 84.78a 0.68d 81.04a 1.55c 18.27c 84.1Sbc -0.70e 1T.71d 80 86.96b 0.46d 14.87d 83.90b 0.70d 19.11d 79.97b 1.53c 20.38b 84.59a -0.79f 17.21e 120 86.48c 0.62c 15.61bc 83.36c 0.81c 20.39cb 79.85b 1.48c 21.54a 84.23b -0.49d 18.17b 85.74d 160 0.81b 16.16ab 82.69d 1.00b 20.70b 79.15c 1 .72b 21.61a 83.88cd -0.24b 1B.06bc 240 85.27e 1 .04a 16.68a 82.19e 1.31a 21 .44a 79.05c 1.95a 21.55a 83.66d 0.07a 17.88cd Winalta 0 86.24c 0.47d 15.00a 83.12d 0.79c 18.91b 79.23c 1.58cb 20.74b 82.64c -0.17c 18.38a 40 87.92a 0.42d 13.01b 84.96a 0.77c 16.23d 80.88a 'l.56cbd 18.07d 83.44a -0.20cd 17.52c 80 87.67a 0.47d 13.52b 84.46b 0.72d 17.70c 80.46b 1.47d 19.43c 83.40ab -0.26d 17.41c 120 86.79b 0.61c 14.58a 83.62c 0.86b 18.96b 79.45c 1.49cd 20.83b 83.13b -0.05b 17.78b 160 86.12c 0.76b 15.20a 82.92d 0.88b 20.04a 78.71d 1.65b 21.28a 83.38ab -0.04b 16.87d 85.34d 240 1.00a 15.28a 81.96e 1.25a 20.01a 77.57e 2.10a 21.18a 82.42c 0.36a 16.93d
88.85 udon -0.68 17.62 86.71 -0.65 21 .72 83.71 -0.40 24.45 85.28 -2.05 23.85 'M"un, in the same column within a variety and heading followed by the same letter are not statistically different (P > 0.05) by t multiple range test. 223 Table 17 Multiple Comparisonsr of Noodle Color for Three 1994 Spring and Three 1994 Winter Wheat Cultivars
0hr Samples 2hr 24h 24 hr and cooked L* a* L" a* b* L* a* b* L* a* b* Katepwa 0 87.17a 0.54d 14.89c 83.93b 1.02c 19.52c 78.79b 2.24b 21.64d 81.68c 0.04b 19.56a 40 87.25a 0.51d 15.21bc 84.27a 0-81d 20.02cb 79.19a 1.94d 2230c 82.47b -0.30e 19.34a 80 86.97a 0.64c 15.34bc 83.91b 1.00c 20.54b 78.63b 2.12c 23.32b 82.39b -0.21d 19.47a 120 86.32b 0.76b 16.55a 83.28c 1.07b 21.63a 78.87b 1.99d 23.63b 83.07a -0.07c 18.97b '1.30a 160 86.06b 0.98a 16.14ab 82.70d 22.00a 77.99c 2.36a 24.30a 82.62b 0.20a 19.01b 240 86.21b 0.96a 15.70abc 82.88d 1.29a 21.52a 78.08c 2.37a 24.27a 83.07a 0.17a 18.65c AG Reed '16.65e 0 87.60a 0.07d 13.73d 84.69a 0.47d 80.04a 1.98d 17.48e 82.59ab -0.06bc 18.04c 40 86.93b 0.06d 15.08c 84.28b 0.55c 17.44d 79.33b 2.42c 18.02d 82.66a -0.08cd 17.67d '17.83d 80 87.60a 0.14c 14.01d 84.64a 0.44d 80.19a 2.01d 18.43c 82.95a -0.'18d 18.34b 120 86.12c 0-46a 15.7Sbc 82.92c 0.90aa 19.86c 77.49d 3.01a 20.40b 82.58ab 0.04ab 18.71a 160 85.69cd 0.35b 16.55ab 82.70d 0.77b 20.40b 77.82c 2.72b 20.72b 82.56ab 0.09a 18.30b 240 85.26d 0.46a 16.95a 82.28e 0.78b 21.52a 77.37d 2.76b 21.52a 82.25b 0.12a 18.55a Roblin 0 86.19b 0.77c 15.43cd 83.08b 1.09c 20.30cd 78.51b 2.18d 22.94c 82.23d -0.04e 19.48a 40 87.05a 0.63d 15.00d 84.04a 0.93d 19.68d 79.73a 1.90e 22.47d 83.28a -0.32f 18.98bc 80 85.97bc 1.01b 15.11d 82.46c 1.43b 20.62c 76.81d 3.05a 23.29bc 81.29f 0.55c 19.16b 120 85.58cd 1 .10b 16.49b 82.46c 1.37b 18.12b 78.47b 2.38c 23.60b 82.81b 0.37d 19.13b 160 85.1 6d 1.33a 16.21bc 81.90d 1.66a 21 .68ab 77.60c 2.75b 23.67b 82.47c 0.73b 18.75c 240 84.47e 1.38a 17.46a 81.37e 1.67a 22.69a 76.99d 2.87b 24j4a 81.91e 0.94a 18.86c s86-375 0 87.78ab 0.26c 12.45c 84.46b 0.56c 14.52e 80.23b 1 .1 9d 15.25d 79.75e 0.37b 17.55e 40 88.05a 0.03e 14.19b 85.16a 0.34e 16.85d 81j2a 1.46c 18.00c 83.19a -0.30c 18.28d 80 87.52b 0.05de 14.96b 84.62b 0.28f 18.19c 80.40b 1.22d 19.81b 82.65b -0.47d 19.48bc 120 86.38c 0.16cd 17.18a 83.34c 0.41d 21.09b 79.01c 1.57c 21.49a 82.27c -0.45d 19.89a 160 85.31d 0.62b 17.59a 81.92d 1.01b 22.05a 76.72d 2.65b 22.09a 80.70d 0.43b 19.70ab 240 83.76e 0.86a 18.02a 80.64e 1.20a 22.03a 76.64d 2.46a 21.65a 80.47d 0.59a 19.24c Winalta 0 89.88a 0.79b 14.39c 82.85c 1.26b 19.47c 78.39c 2.00c 22.24d 81.43c 0.31b 20.47b 40 87.99ab 0.38d 13.84c 84.70a 0.74d 18.32d 80.64a 1.29e 20.96f 82.97a -0.30e 19.94d 80 87.49ab 0.49c 14.28c 83.87b 0.89c 19.17c 79.08b 1.63d 21.71e 81.98b -0.14d 19.87d 120 85.50b 0.8'lb 17.67a 82.24d 1.22b 22.74a 77.46d 2.11bc 24.70a 81.79b 0.1 1c 21 .41a 160 85.55b 0.98a 16.60b 82.07d 1.39a 21.99b 77.07e 2.31a 23.88c 81.39c 0.54a 20.32bc 240 84.80b 1.03a 17.65a 81.44e 1.43a 22.58a 77.02e 2.17b 24.31b 81.38c 0.51a 20.22c Norstar 0 85.77c 0.73c 16.86c 82.29c 1.03c 22.40cd 79.11b 1.76b 23.35bc 83.00b -0.35c 19.84b 40 87.21a 0.14e 16.61c 84.28a 0.28e 21.48e 80.86a 0.95d 23.43bc 83.68a -1.20e 20.82a 80 86.52b 0.41d 16.75c 83.32b 0.64d 21.89de 79.'l0b 1.58c 23.28bc 83.04b -0.74d 19.86b 120 85.00d 0.98b 17.37bc 81.80e 1.26b 22.45c 77.35d 2.41a 23.08c 81.86d 0.38a 19.92b 160 85.15d 0.75c 17.98ab 81.97d 1.02c 23.19b 78.53c 1.76b 23.66ab 83.15b -0.23b 19.71b 240 84.05e 1.28a 18.86a 80.74f 1.60a 24.25a 77.19d 2.49a 24.06a 82.22c 0.36a 19.57b
88.85 udon -0.68 17.62 86.71 -0.65 21 .72 83.71 -0.40 24.45 85.28 23.85 tM.un, in the same column within a variety and heading followed by the same letter are not statistically different (P > 0.05) by t multiple range test. 224 Table 18 Multiple Comparisonsr of Noodle Making Processing Quality and Cooking Quality2 for 1994 Spring Wheat Cultivars
N rate DSL THICK AREA DIST BW 90 0 54.6bc 1.40bc 105.59bc 555.40d 23.88c 405d 2.02b 40 57.7a 1.34c 92.01d 462.18d 22.70c 370e 1.93c 80 55.9b 1.38bc 101.25cd 636.75d 27.75c 490b 2.03b 120 53.4cd 1.43b 104.28c 1047.88c 40.81b 440c 2.00b 160 52.0d 1.44b 1 15.55b 1268.29b 45.31b 455c 2.02b 240 50.5e 1.52a 145.BBa 1759.96a 50.80a 54Oa 2.07a Katepwa
0 58.3b 1.26b 83.B3bc 515.76cd 26.60bc 4B0c 1.81b 40 61.1a 1.22b 80.22c 425.03d 23.58c 460c 1.81b 80 58.8b 1.26b 86.91abc 418.21d 21.B6bc 500c 1.88a 120 58.7b 1.29ab 83.83bc 616.42bc 31.45ab 570ab 1.83b 160 57.2bc 1.29ab 94.11ab 707.59ab 31.80ab 555b 1.88a 240 55.9c 1.37a 96.19a 751.03a 32.51a 61 0a 1.87a AC Reed 0 67.5bc 1.14ab 47.54c 39.80c 5.53c 270c 1.76abc 40 70.9a 1.12ab 43.00c 36.28c 5.22c 250b 1.74cd 80 69.5ab 1.11b 47.18c 38.52c 5.71c 280a 1.70d 120 68.0b 1 .1 3ab 58.07b 60.63b 7.03b 300a 1.7\bc 160 67.3bc 1.13ab 72.20a 109.86a 9.06a 300a 1.79a 240 65.7c 1.18a 76.71a 1 18.01a 9.26a 31 0a 1.79ab Roblin 0 55.5bc 1.35abc 83.99d 985.1 5c 46.83cd 555c 1.88b 40 60.2a 1.26c 71.16e 769.52d 43.40d 425e 1.87b 80 56.4b 1.29bc 85.56cd 1216.77b 55.'14bc 525d 1.82c 120 '1388.98b 54.3c 1.36abc 92.18c 62.O6ab 61 0b 1.95a 160 52.0d 1.40ab 112.23a 1741.97a 65.27ab 61 5b 1.94a 240 51.5d 1.43a 102.67b 1627.27a 67.62a 670a 1.95a AC Taber U 58.7c 1.34b 89.1 6d 205.96d 12.64c 410c 1.91b 40 62.0a 1.27d 73.85e 132.74d 10.38c 320d '1.83c 80 60.3b 1.30cd 87.63cd 229.76d 13.74c 390c 1.54b 120 59.3bc 1.31bc 84.06c 409.33c 22.27b 450b 1.94b 160 56.2d 1.39a 105.53a 573.25b 24.59b 500a 1.99a 240 54.7d 1.41a 90.89b 921.46a 41.78a 470b 1.99a ' Means in the same column within a variety and heading followed by the same letter are not statistically different (P > 0.05) by t multiple range resr. : 'DSL dough sheet length (cm); THICK : thick¡ess of fresh noodle (mm); Rmax : the maximum extension resistance (g); AREA : the area under extension curve (g.s); DIST : extension distance (mm); OCT: optimum cooking time (s); CNT: cooked noodle thickness (mm). 225
r ab re I e Murtip I e and co okin g iîilï:"î;, |;i:îHffiix å:ïîî:'aritv
N rate DSL THICK DIST CNT Glenlea 0 55.0a 1.41b 83.35d 422.01cd 22.97d 72obc 2.07b 40 55.3a 1.33c 86.73cd 317.83d 18.02e 610d 2j1b 80 53.0ab 1.40b 93.91bcd 598.50c 27.58c 750ab 2j1b 120 50.8bc 1.43b 96.09bc 966.42b 40.17b 770ab 2.17b 160 48.8c 1.54a 103.04b 1002.34b 39.36b 690c 2.16b 240 45.1d 1.58a 133.06a 1767.33a 55.86a 800a 2.27a Katepwa 0 56.0ab 1.38ab I 16.09b 423.08c 1 8.1 Bc 735c 2.11ab 40 57.0a 1.26c 116.57b 281.26d 13.42d 660d 2.11a 80 57.1a 1.32bc 122.57b 422.20c 17.38c 730c 2.06b 120 55.7ab 1.38ab 131 .65a 646.17b 22.93b 900b 2.09ab 160 54.6bc 1.41a 131.51a 727.95b 25.10b 980a 2.12a 240 52.9c 1.44a 138.14a 1079.16a 33.65a 950a 2.10ab AC Reed 0 69.4a 1.06c 48.55de 45.07c 5.92c 330d 1.88b 40 70.4a 1.0Bbc 45.05e 38.43c 5.22c 315d 1.84b 80 69.0ab 1.14ab 52.90d 49.31c 5.75c 365c 2.07a 120 67.0bc 1.11abc 60.6'1c 73.91b 7.55b 360c 1.83b 160 66.4cd 1 .1 3abc 66.35b 84.85b 7.91b 460a 1.88b 240 64.2d 1.17a 82.54a 175.64a 12.06a 430b 1.80b Roblin 0 55.8a 1.34de 107.56c 432.94e 1 9.1 9e 670cd 2.08c 40 56.2a 1.32e 107.87c 543.67e 22.75e 660d 1.84e 80 53.9b 1.38cd 109.30c 1207.58d 44.00d 705c 2.04d '114.59bc 120 52.5bc 1.42bc 1591.14e 55.22c B00b 2.17a '160 51.4c 1.45b 120.84b 1828.40b 62.05b 810b 2.04d 240 49.7d 1.52a 146.45a 2411.93a 69.56a 860a 2.13b AG Taber 0 59.6b 1.33ab 89.02cd 203.17c 12.61c 600b 2.15a 40 61.5a 1.28b 75.13e 102.77d 8.37d 570bc 2.10b 80 61.3ab 1.29b 84.62d 195.48c 12.63c 550c 1.97d 120 57.8c 1.30b 96.88b 217.20c 12.48c 665a 2.03c 160 56.6c 1 .31b 93.69bc 289.21b 15.65b 680a 2.11ab 240 54.5d 1.38a 105.14a 631.80a 26.88a 675a 2.05c I' Means in the same column within a variety and heading followed by the same letter are not statistically diffe¡ent (P > 0.05) by r mulriple range rest. 2 Abbreviations as defined in Table 18. 226 Table 20 Multiple Comparisonsr of Noodle Making Processing Quality and Cooking 'Winter Quality2 for 1994 Wheat Cultivars
N rate DSL THICK AREA ocr CNT CDG Kestrel 0 58.8b 1.32bcd 76.64b 537.06c 30.54c 350c 1.93d 40 60.7a 1.28d 69.72b 300.25d 20.02d 340c 1.92d 80 58.3b 1.29cd 77.44b 254.66d 16.51d 345c 1.99b 120 56.3c 1.3Babc 78.52b 641.20c 34.62c 380b 1.97c 160 55.4c 1.39ab 76.22b 963.95b 51.17b 395b 2.06a 240 53.7d 1.45a BB.63a 1527.20a 68.59a 470a 2.05a Norstar 0 55.9b 1.40b 86.75b 758.93bc 36.13c 370cd 1.85d 40 58.3a 1.33b 82.61b 271.33d 16.53d 350d 1.89c 80 58.5a 1.32b 90.08b 312.62d 17.09d 380c 1.97ab 120 55.9b 1.40b 86.54b 643.46c 31.77c 450a 1.95b .160 53.5c 1.39b 83.71b 934.28b 44.12b 420b 1.95b 240 51.2d 1.51a 106.02a 1488.15a 55.90a 435ab 2.01a s86-375 0 62.0bc 1.23cd 72.80c 99.83d 8.54d 270e 1.99a 40 64.5a 1.20d 67.81c 84.23d 7.95d 290d 1.82c 80 62.7ab 1.21d 72.33c 115.22cd 9.49cd 310c '1 .90b 120 62.4bc 1.29ab 79.25b 151.54c 11.19c 270e 1.87bc 160 61.7bc 1.26bc 84.37ab 228.28b 14.21b 450a 1.86bc 240 60.5c 1.30a 89.92a 375.78a 20.05a 430b 1.82c s86-101 0 60.0a 1.32ab 85.62b 513.70bc 26.92bc 280e 1.91c 40 60.6a 1.33ab 77.14c 397.70c 23.96c 380b 1.83d 80 60.5a 1.30b 79.17bc 381.43c 22.37c 360cd 1.85d 120 55.9bc 1.37ab 83.45bc 689.96b 35.24bn 370bc 1.96b 160 57.0b 1.36ab 82.24bc 1050.57a 50.34a 350d 1.94bc 240 54.3c 1.42a 93.92a 1136.45a 47.93a 445a 2.01a Winalta 0 54.6bc 1.40a 101 .03c 655.62c 28.54bc 550b 2.11a 40 57.8a 1.33a 80.39d 442.59d 24.74c 430d 1.90d 80 56.6ab 1.39a 97.07c 359.93d 17.95d 465c 1.99c 120 57.3a 1.36a 95.05c 754.91bc 33.60ab 540b 2.08ab 160 54.0c 1.41a 109.24b 844.84b 32.72b 605a 2.06b 240 53.0c 1.41a 124.03a 1216.00a 40.02a 570b 2.00c l' Means in the same column within a variety and heading followed by the same lefter are not statistically different (P > 0.05) by t multiple range test. 2 Abbreviations as defined in Table 18. 227
2 Table 21 Multiple Comparisonsr of Noodle Processing Quality and Cooking Quality for 1995 Winter Wheat Cultivars
N rate THICK DIST ocT CDC Kestrel 0 63.2a 1.20b 74.45cd 289.47b 'lB.60b 660a 1.80c 40 64.5a 1 .19b 57.93e 85.'t6c 8.58c 410d 1.86c 80 61.3b 1.25ab 67.20d 1 13.35c 9.71c 495c 1.80c 120 60.5b 1.26ab 78.71bc 154.04c 11.05c 570b 1.93b 160 59.8b 1.27ab 82.24ab 300.67b 17.80b 590b 1.94ab 240 57.5c 1.32a 89.69a 538.65a 26.34a 570b 2.01a Norstar n 61.5b 1.21cd 94.68bc 546.80c 25.96b 420b 1.88c 40 63.6a 1.16d 87.00c 240.40d 14.82c 340c 1.90c 80 62.Sab 1.25bc 94.68bc 273.59d 15.34c 340c 1.96b 120 59.8c 1.29b 100.63b 583.06bc 26.24b 450a 1.97b 160 58.6c 1.27bc 102.58b 730.98b 31.23a 435ab 'r .91c 240 56.0d 1.37a 1 18.99a 951.92a 35.08a 450a 2.00a s86-375 0 65.5a 1 .16ab 75.37b 117.14b 9.59b 330c 1.82bc 40 66.2a 1.14b 44.44d 36.08c 5.35d 345c 1.88a 80 65.8a 1 .15b 51 .26c 45.11c 5.92cd 380b 1.83b 120 64.5ab 1.16ab 56.63c 55.87c 6.55c 390ab 1.83b 160 63.5b 1.18ab 80.42b 126.38b 10.'t0b 395ab 1.80c 240 61.3c 1.22a 95.22a 196.59a 12.23a 410a 1.89a s86-1 01 0 63.3b 1.19b 68.36bc 377.67b 24.28b 450bc 1.76d 40 65.3a 1.16b 63.57c 93.65d 8.85d 430c 1.74d 80 64.1ab 1.19b 71.74abc 210.97cd 14.67c 390d 1.82c 120 60.9c 1.26ab 78.24ab 330.74bc 19.55bc 440c 1.86b 160 60.6c 1.25ab 73.84ab 381.32b 23.07b 470b 1.83c 240 57.7d 1.33a 79.27a 589.97a 31.14a 520a 1.97a Winalta 0 58.6b 1.28b 62.41d 607.45b 39.30b 450d 1.90c 40 61.6a 1 .23c 66.01cd 105.10d 9.22d 400e 1.90c 80 58.1b 1.27b 77.69ab 157.52d 11.27d 430d 1.94abc 120 56.5c 1.29ab 71.73bc 346.60c 22.19c 530b 1.96a 160 55.5d 1 .31ab 71.94bc 700.33b 38.99b 4B0c 1.91bc 240 53.7e '1.33a 81.25a 975.70a 47.95a 605a 1.95ab I' Means in the same column within a variety and heading followed by the same ìetter are not statistically different (P > 0.05) by t multiple range test. 2 Abbreviations as defined in Table 18. 228 Table 22 Multiple Comparisonsr of Noodle Cooking properlies 2
N rate N rate CL 1995 Spring Wheat 1995 Winter Wheat Katepwa Norstar 0 7.21cd 2.82a 0 6.44cd 2.56bcd 40 B.19ab 2.81a 40 8.06a 2.69a 80 8.47a 2.80a 80 7.47ab 2.66ab 120 7.43bc 2.79a 120 6.BBbc 2.61abc '160 6.99cd 2.78a 160 6.1 5de 2.50cd 240 6.57d 2.72a 240 5.67e 2.46d Roblin s86-375 0 7.63a 2.74a 0 6.74b 2.54ab 40 7.50ab 2.87a 40 8.30a 2.60ab 80 7.3Oab 2.82a BO 8.00a 2.67a 120 7.00bc 2.89a 120 6.53b 2.30c 160 6.5Bcd 2.73a 160 6.22b 2.48b 240 6.31d 2.70a 240 6.09b 2.46b AC Reed Winalta 0 7.31ab 2.46c 0 5.45d 2.53b 40 7.42a 2.55ab 40 7.68a 2.67a 80 7.42a 2.58a BO 6.79b 2.65a 120 6.81bc 2.50bc 120 6.56b 2.48b 160 6.22cd 2.38d 160 6.03c 2.56b 240 6.15d 2.47c 240 5.92c 2.50b 1994 Spring Wheat 1994 Winter Wheat Katepwa Norstar 0 6.15ab 2.63b 0 5.89c 2.58b 40 8.41a 2.73a 40 6.77a 2.68a BO 5.48ab 2.71a 80 6.68a 2.64ab 120 5.70b 2.66b 120 6.30b 2.63ab 160 6.40ab 2.65b 160 5.73cd 2.58b 240 6.39ab 2.63b 240 5.60d 2.50c Roblin s86-375 0 6.18a 2.66ab 0 7.36bc 2.55ab 40 6.43a 2.68a 40 8.1 6a 2.59ab BO 6.13a 2.67ab BO 7.44b 2.62a 120 5.57b 2.64bc 120 6.B6cd 2.52b 160 5.45b 2.60d 160 6.68de 2.43c 240 5.97ab 2.61cd 240 6.27e 2.42c AG Reed Winalta 0 7.30a 2.57cd 0 5.71b 2.52bc 40 7.06b 2.65a 40 6.15a 2.59a 80 6.60c 2.62ab 80 6.31a 2.55ab 120 6.52c 2.62ab 120 5.75b 2.53b 160 6.04d 2.60bc 160 5.53b 2.49cd 240 6.08d 2.53d 240 5.58b 2.45d ' Meaus in the same column within a variety and heading followed by the same letter are not statistically different (P > 0.05) by t multiple range test. ' CL: cooking loss (%); WT : water uptake (g/g). 229 2 Table 23 Multiple Comparisonsr of Cooked Noodle Texture in Constant Cooki¡g Time Test for 1994 Spring Wheat Cultivars
N rate RECOV CS SFM HARD ADHE SPRING COHES GUM CHEWN RESI BW 90 rì 57.96c 25.72c 94.35c 1956.24c -79.63abc 1.00a 0.75a 1463.55d 1458.78b 0.42a 40 54.13d 22.22d 93.09c 1733.S6e -85.31abc 0.97a 0.75a 1303.03f 1259.86b 0.42a BO 59.30b 25.59c 103.30bc j986.46c -137.7\bc 0.97a 0.75a 1493.66c 1446.10b 0.41a 120 57.08c 25.08c 107.22ab 1843.47d -76.83ab 0.99a 0.75a 1387.45e 1369.54b 0.42a 160 60.1 6b 29.78b 116.25a 2171.46b -140.75c 0.97a 0.76a 1644.13b 1599.30b 0.41a 240 62.04a 31.85a 114.94a 2298.79a -60.68a 1.16a 0.75a 1727.36a 1997.B8a 0.41a Katepwa
0 54.29bc 20.39c 101 .69abc 1613.23c -42.26a 0.99a 0.74ab 1196.45d 1189.47d 0.41a 40 52.68c 18.52d 93.75c 1599.31c -39.23a 1.00a 0.74b 1177.36d 1173.52d 0.41a BO 55.28b 19.86c 92.04c 1660.94c -49.96a 0.98ab 0.74b 1221.20d 1200.84d 0.40a 120 54.64b 20.12c 97.25bc fiBg.23b -64.52a 0.97b 0.72b 1288.78c 1251 .87c 0.39a '160 57.67a 21 .47b 108.62ab ß40.67ab -41.87a 1.00a 0.74b 1353.91b 1349.03b 0.41a 240 57.74a 22.91a 1 1 3.19a 1869.02a -69.06a 0.99ab 0.76a 1419.26a 1399.71a 0.41a AG Reed 0 44.87bc 13.84c B0.77cd 1343.90d -52.11a 0.99a 0.71a 959.54d 952.85d 0.38a 40 39.24d 13.05c 76.43d 1369.33d -74.83a 0.95b 0.69b 949.32d 901 .77e 0.35b 80 43.26c 15.04b 85.66c 13S9.93d -60.74a 0.97ab 0.70ab 957.56d 930.88de 0.36ab 120 44.29c 15.43b 94.48b 1523.21c -62.32a 0.97ab 0.71ab 1080.04c '1050.03c 0.37ab 160 46.68ab 18.21a 104.36a 1SB9.4Bb -61 .12a 0.97ab 0.72a 1142.11b 1111.26b 0.38a 240 48.38a 18.07a 101 .28a 1692.09a -71.54a 0.98ab 0.71ab 1197.77a 1173.21a 0.37a Roblin 0 53.96a 20.77b 108.13ab fl0917c -92.BBb 0.9Bab 0.72ab 1237.34b 1207.69b 0.39ab 40 50.1 3b 't 8.B9c 94.26d 1SBB.17d -49.60a 0.97ab 0.72b 1144.92c 1114.28c 0.39a OU 51.67b 20.68b 103.82bc ß34.67d -1 15.46c 0.96b 0.73ab 1196.95bc 1152.61bc 0.41a 120 54.70a 21.03b 100.33cd 1865.56a -89.50b 0.98a 0.71b 1330.50a 1305.93a 0.37b 160 55.53a 24.48a 105.'l6bc 1765.98b -51.29a 0.99a 0.75a 1327.50a 1311.24a 0.41a 240 54.73a 24.87a 1 15.33a 1819.1Sa -60.94a 0.98a 0.73ab 1330.68a 1307.30a 0.39ab AG Taber 0 52.37cd 22.30c 81.92d 1714.24d -154.90ab 0.94b 0.73a 1259.30d 1181.52c 0.40a 40 45.95e '18.40e 85.02cd 1407.42e -101.53a 0.96ab 0.75a 1054.25e 1009.77d 0.39a 80 50.91d 21 .80d 90.99bc 1766.61c -201 .13b 0.94b 0.73a 1290.98c 1211.37c 0.39a 120 53.29bc 23.07b Oá QOrh 1 758.81c -105.21a 0.95ab 0.75a 1324.40b 1 263.50b 0.40a 160 56.47a 26.35a 96.38ab 2051 .62a -131.96a 0.97a 0.74a 1518.90a 1467.16a 0.39a 240 54.94ab 26.17a 100.71a 2009.68b -119.54a 0.97aa 0.75a 1498.10a 1454.23a 0.40a ' Means in the same column within a variety and heading followed by the same letter are not statistically different (P > 0.05) by t multiple range test. t RBCOV = compression recovery (%); CS : cutting stress (g/mm); : : SFM surface firmness (g/s); HARD TPA hardness (g); ADHE : TPA adhesiveness (g.s); SpRI : TpA springiness; coHE = TpA cohesiveness; GUM: TPA gumminess (g); CHEW: TpA chewiness (g); RESI: TpÀ resilience. 230 2 Table 24 Multiple Comparisonsr of Cooked Noodle Texture in Constant Cooking Time Test for 1995 Spring Wheat Cultivars
N rate RECOV CS SFM HARD SPRING COHES GUM CHEWN RESI Glenlea
0 51.66de 22.68e 90.95b 1798.17d -176.32b 0.94b 0.72a '1291 .39d 1214.59c 0.38a 40 21.19f 92.16b 50.94e 1733.45d -110.42ab 0.97ab 0.71a 1238.06e 1200.07c 0.36a BO 53.52cd 24.89d 91 .29b 1891.23c -172.83b 0.97ab 0.72a 1364.45c 1318.02b 0.36a 120 55.l8bc 27.53c 100.49ab 2106.04b -112.86ab 1.00a 0.71a 1498.30b 1492.65a 0.36a 160 56.21ab 31.32t) 110.24a 2111.62b -126.74ab 0.98a 0.72a 1524.79t) 1498.07a 0.37a 240 57.60a 34.57a 112.41a 2223.93a -'100.55a 0.98a 0.71a 1585.49a 1558.33a 0.36a Katepwa
0 57.37bc 25.70c 100.49a 2255.44c -92.48a 1.02a 0.73ab 1644.17c 1674.45c 0.39ab 40 56.01c 25.52c 94.50a 2274.53c -163.78a 0.97b 0.71b 1625.12c 1574.44d 0.37b 80 25.7Oc 56.09c 104.94a 2270.26c -149.07a 0.97b 0.72ab 1641.22c 1585.90d 0.38ab 120 57.53bc 24.93d 110.35a 2402.94b -80.44a 0.99ab 0.72ab 1733.89b 1711.16bc 0.38ab 160 26.94b 58.77b 101 .64a 2575.94a -79.93a 0.97ab 0.73ab '1852.00a 1836.00a 0.38ab 240 61.79a 29.84a 115.92a 2470.94b -118.40a 0.98ab 0.73a 181 5.69a 17B2.BBab 0.39a AG Reed
U 40.93cd 12.22d 79.20c 1446.36c -50.89a 0.98a 0.69a 997.43cd 975.30cd 0.33ab 40 40.05d 12.71d 79.89c 1433.06c -68.37a 0.97a 0.68a 971 .1 6d 941.30d 0.33b 80 43.26bc 14.99c 59.49d 1580.81 b -38.83a 0.93a 0.67 a 1062.39bc 994.63bcd 0.34ab 120 44.93ab 15.98bc 92.41bc 1629.74b -65.50a 0.98a 0.68a 1115.17b 1088.23ab 0.34ab 160 45.36ab 16.39ab 97.73ab 1782.24a -77.06a 0.97a 0.67a 1 195.56a 1 154.30a 0.35a 240 47.14a 17.52a 107.67a 1643.78b -121.35b 0.95a 0.69a 1 135.01ab 1082.24abc 0.34ab Roblin 0 55.49b 23.29e 103.57a 2131 .jÙc -219.52c 0.95d 0.73a 1560.78b 1483.67b 0.38bc 40 51.91c 23.70e 72.93b 1383.37e -120.27ab 0.95cd 0.74a 1022.14d 974.48c 0.41a 80 57.75ab 26.02d 103.34a 2000.37d -121 .29ab 0.98abc 0.74a 1480.27c 1451.50b 0.40a 120 55.38b 26.81c 107.79a 2299.55a -'154.08bc 0.96bcd 0.71b 1626.47a 1555.23a 0.37d 160 30.58b 1 16.63a 58.39a 2050.61d -64.97a 0.98ab 0.73a 1499.35c 1472.09b 0.39bc 240 60.29a 31.80a 108.90a 2214.43b -84.73a 0.99a 0.73a 1 613.99a 1604.01a 0.38cd AC Taber n 20.22d 51.38b 94.B6bc 2012.50a -88.32a 0.99a 0.70b 1405.44b 1394.71ab 0.37a 40 50.4'l 20.38cd b 82.07d 1716.01c -77.84a 0.97a 0.73a 1250.36c 1215.75c 0.39a 80 20.87c 45.77c 81 .27d 1749.79c -138.44a 0.95a 0.72ab 1260.81c 1199.17c 0.37a 120 18.45e 1 00.B5ab 54.96a 1916.23b -151 .B3a 0.97a 0.73ab 1391 .84b 1343.40b 0.38a 160 52.19b 24.11b 92.34c 2014.06a -140.95a 0.96a 0.72ab 1452.89ab 1 398.B7ab 0.38a 240 55.76a 27.54a 102.78a 2060.06a -131 .30a 0.98a 0.73a 1502.00a 1466.27a 0.39a I ' Means in the same column within a variety and heading followed by the same letter are not statistically different (P > 0.05) by t multiple range test. 2 Abbreviations as defined in Table 23. 231 Table 25 Multiple comparisonsr of cooked Noodle Texture2 in Constant Cooking Time Test for 1994 Winter Wheat Cultivars
N rate RECOV CS SFM ADHE SPRING COHES GUM CHEWN RESI CDC Kestrel 0 50.93c 100.90ab 22.01d 1917.21d -1 32.9Bab 0.96a 0.72a 1389.26d 1326.68c 0.38b 40 49.54d 21 .25e 85.BBc 1807.08e -1 23.30ab 0.97a 0.73a 1311.45e 1272.44c 0.39ab 80 48.47d 21 .72ed 92.79bc 1973.80c -187.96b 0.95a 0.72a 1424.01c 1354.79c 0.39ab 120 55.44b 24.35c 99.34ab 2025.02b -89.51a 0.97a 0.74a 1492.14b 1453.51b 0.40a 160 54.32b 25.80b 107.07a 2037.99b -95.80ab 0.98a 0.74a 1498.96b 1467.91b 0.40ab 240 58.02a 27.65a 97.51ab 2209.57a -119.77ab 0.98a 0.74a 1627.37a 1597.52a 0.40a Norstar 0 53.55c 24.25c 84.94c 1736.49d -84.02a 0.98ab 0.74a 1278.39d 1258.07d 0.40a 40 51.95d 21.46e 93.74bc 1679.49d -133.97a 0.95c 0.73a 1224.18d 1158.27e 0.39a 80 52.72cd 94.00bc 22.80d 1864.65c -74.73a 1.01a 0.73a 1354.39c 1364.59c 0.40a 120 55.18b 26.05b 103.50b 1921 .22bc -'140.99a 0.98abc 0.74a 1420.44b 1386.07bc 0.40a 160 56.78a 26.67b 102.77b 1950.63b -64.39a 1.00ab 0.74a 1438.35b 1431 .41b 0.40a 240 58.00a 118.22a 29.47a 2114.33a -1 35.1 0a 0.97bc 0.73a 1551 .69a 1502.28a 0.40a s86-375 0 45.57b 17.01c 76.51c 1 653.36cd -70.27a 0.97a 0.69b 1132.88d 1102.33bc 0.36bc 40 46.05b 16.91c 80.51 bc 161 9.85d -125.21a 0.94a 0.69b 11 16.30d 1054.02c 0.35c 80 49.61a 17.26c 81.55bc 1774.85ab -66.82a 1.00a 0.70ab 1250.21bc 1251 .30a 0.38ab 120 50.47a 19.19b 90.24b 1715.07bc -98.01a 0.97a 0.71a 1225.56c 1 186.92ab 0.38a 160 49.70a 18.85b 105.23a 1827.42a -120.53a 0.95a 0.70ab 1272.59ab 1213.66a 0.37abc 240 50.60a 21 .18a 107.26a 1 835.34a -85.86a 0.98a 0.70ab 1292.39a 1260.16a 0.37ab s86-1 01 0 51.83c 22.30d 92.18c 1749.78c -145.67ab 0.95cd 0.73ab 1268.33c 1202.59c 0.39a 40 48.47d 20.31f 92.39c 1708.76c -124.21ab 0.94d 0.72b 1222.25d 1152.12d 0.38a 80 50.49c 21 .11e 101 .00b 1724.57c -150.01b 0.94d 0.73ab 1256.41c 1180.43cd 0.39a 120 55.87b 23j2c 109.65a 201 8.03b -91 .00a 0.98ab 0.73a 1477.57b 1451 .02b 0.40a 160 55.91b 24.17b 99.1Bbc 1 969.07b -87.17a 1.00a 0.74a 1453.03b 1449.46b 0.40a 240 59.25a 27 .51a I 10.33a 2163.90a -134.34ab 0.97bc 0.74a 1594.59a 1545.09a 0.40a Winalta 0 58.20ab 25.67d 104.24a 2289.46a -97.06a 0.99a 0.72c 1649.27a 1634.38a 0.38c 40 40.55b 21.87t 99.89a 1 836.04e -89.64a 0.97a 0.75b I 373.50d 1337.67c 0.41ab 80 57.27ab 23.94e 101.47a 2047.48d -141 .99ab 0.97a 0.74b 1510.27c 1459.03b 0.40bc 120 60.73ab 26.61c 108.62a 2187.62c -108.95ab 0.98a 0.74b 1625.34ab 1 589.92a 0.41ab 160 60.13ab 102.24a 27.54b 2201.86b -177.91b 0.96a 0.74b 1637.61a 1571 .98a 0.41ab 240 64.07a 31.19a 108.95a 21 15.89b -1 1 9.57ab 0.98a 0.76a t 1608.62b 1570.19a 0.42a M"on, in the same colurnn within a variefy and heading followed by the same letter are not statistically different (P > 0.05) by t multiple range tesr. 2 Abbreviations as defined in Table 23. 232 Table 26 Multiple Comparisonsr of Cooked Noodle Texture2 in Constant Cooking Time Test for 1995 Winter Wheat Cultivars
N rate RECOV CS HARD ADHE SPRING COHES GUM CHEWN RESI CDC Kestrel
0 44.40cd 20.47c 77.43c 1466.90c -85.93ab 0.97a 0.73a 1067.0'fc 1033.75c 0.38b 40 42.09d 17.83d 74.09c 1270.11d -72.95ab 0.97a 0.74a 933.80d 904.83d 0.39ab BO 47.54bc 21 .20c B2.57bc 1493.96c -58.77ab 0.97a 0.74a 1104.81c 1069.28c 0.40a 120 50.34ab 22.37b 92.11ab 1791 .12b -1 16.45b 0.96a 0.72a 1294.03b 1245.50b 0.38b 160 53.90a 23.46b 97.16a 1773.37b -98.49ab 0.97a 0.73a 1296.38b 1253.28b 0.39ab 240 54.83a 25.75a 101.37a 1 993.04a -47.05a 0.99a 0.73a 1452.21a 1440.50a 0.39ab Norstar
0 53.21c 22.32c 89.46b 1749.39d -1 09.83ab 0.97a 0.75a 1307.56d 1263.05cd 0.41a 40 49.14d 13.47e 95.28ab 1 676.30e -139.45ab 0.96a 0.73b 1218.68e 1171.75d 0.39a BO 53.36c 19.68d 88.98b 1 654.1 8e -104.72ab 0.97a 0.74ab 1228.20e 1185.47d 0.40a 120 55.71b 22.41bc 94.95ab 2003.71b -149.82b 0.96a 0.75a 1492.01b 1441 .49b 0.40a 'lOB.Bga 160 56.91ab 22.86b 1 849.93c -102.22ab 0.96a 0.75a 1375.78c 1324.31c 0.40a 240 58.39a 24.93a 108.87a 2094.52a -84.57a 1.02a 0.74ab 1545.04a 1567.85a 0.40a s86-375
0 40.54e 15.34d 81.B3d 1461 .29c -77.59a 0.96a 0.69ab 1009.17c 969.18c 0.35a 40 42.11de 15.44cd 93.49c 1682.49b -138.49a 0.94a 0.68ab 1142.36b 1072.45b 0.35a 80 42.69cd 15.84c 89.62c 1676.54b -87.17a 0.95a 0.68ab 1147.92b 1090.52b 0.36a 120 44.22bc 16.34b 99.62b 1685.46b -96.46a 0.95a 0.68b 1144.11b 1091 .B4b 0.35a 160 45.49b 16.48b 100.11ab 1641 .13b -117.01a 0.95a 0.70a 1154.32b 1095.02b 0.37a 240 50.07a 20.70a 104.64a 1 958.82a -115.76a 0.96a 0.69ab 1359.98a 1300.38a 0.36a s86-101
0 45.53c 1 8.1 Be 91 .57b 1573.29d -64.87a 0.98a 0.72b 1137.84d 1111.42cd 0.38b 40 43.70c 17.39t 83.67c 1281 .SBe -85.07a 0.95b 0.72b 919.08e B73.4Be 0.38ab BO 45.1 3c 19.48d 84.10c 1 538.'l 1 d -117.56a 0.95b 0.73ab 1125.70d 1067.02d 0.39ab 120 49.07b 21 .53b 93.03b 1667.79c -108.51a 0.96ab 0.72b 1207.98c 1 '159.61c 0.3Bab 160 49.44b 20.85c 98.64ab 1 763.85b -'104.68a 0.97ab 0.75a 1319.25b 1279.39b 0.38ab 240 53.49a 22.92a 105.25a 1916.05a -101 .40a 0.96ab 0.74ab 1408.07a 1358.91a 0.40a Winalta tt 44.81c 18.27d 92.97a 1650.68c -51.67a 0.97a 0.71a 1173.07c 1131 .48c 0.36bc 40 40.89d 15.48f 75.72d 1472.73e -58.74ab 0.97a 0.69b 1009.42e 981 .46e 0.35c 80 44.98c 17.22e 81 .75cd 1556.79d -88.27b 0.96a 0.69b 1073.58d 1029.95d 0.35c 120 45.75c 18.78c 87.05abc 1712.01b -74.02ab 0.98a 0.71a 1211.38b 1 191 .31 b 0.38ab 160 47.26b 19.35b B6.08bc 1 693.39b -54.28ab 0.98a 0.72a 1208.60b 1183.44b 0.38ab 240 51.15a 22.01a 90.57ab 1774.65a -45.75a 0.98a 0.72a 1280.92a 1251.05a t 0.38a M"un, in the same column within a variety and heading followed by the same letter are not statistically different (P > 0.05) by t multiple range test. 2 Abbreviations as dehned in Table 23. 233 Table 27 }i4.ultiple Comparisons' of Cooked Noodle Texture2 in Optimum Cooking Time Test for 1994 Spring Wheat Cultivars
N rate RECOV cs HARD ADHE SPRING COHES CHEWN RESI BW 90 tt 62.60d 31 1 19.33a .29c 3098.42a -194.23b 0.92a 0.69a 2145.63a 1971.96a 0.34c 40 53.07e 27 .57d 90.1 8b 1854.98c -80.39a 0.84b 0.67a 1248.66c 1044.95a 0.37b 80 65.27c 27.18d 120.23a 3068.28a -148.85ab 0.95a 0.69a 2120.22a 2008.39a 0.35bc 120 65.68bc 27.23d 96.69b 3036.33a -166.38b 0.94a 0.70a 2138.89a 2001.81a 0.36b '160 67.39ab 132.53a 32.08b 2994.07a -156.85ab 0.94a 0.70a 2086.90ab 1958.99a 0.36b 240 68.99a 101 .B7b 37.28a 2753.27b -157.40ab 0.94a 0.70a 1940.25b 1828.00a 0.40a Katepwa 0 62.43bc 120.28a 26.14d I 885-23d -159.41c 0.96ab 0.76a 1423.36e 1370.71c 0.44a 40 62.13bc 23.65e 99.42bc 2203.25c -'104.93abc 0.92b 0.74b 1621.94d 1495.70c 0.4'1bc 80 63.01b 27 103.34b .60c 2478.73b -111.91bc 0.99ab 0.71c 1750.20c 1738.76bc 0.38d 120 65.49a 27.23c 129.29a 2869.89a -98.1 1ab 1.00ab 0.72c 2054.96a 2045.63ab 0.39cd 160 65.38a 120.42a 28.73b 2588.37b -90.59ab 1.16a 0.74b 1907.80b 2212.18a 0.41b
240 60.91c 90.39c 1 30.88a 839.35d -49.63a 0.98ab 0.74b 1355.46e 1324.28c 0-44a AC Reed
0 52.91c 15.33c 138.94a 1592.81b -106.22abc 0.98ab 0.65a 1044.08a 997.36b 0.32ab 40 50.73d 14.49d 124.08a 2395.84ab -'l34.B9cd 0.95ab 0.65a 1547.24a 1471.11ab 0.31b 80 52.40c 120.72a 13.90d 1 860.49ab -77.04a 0.92b 0.66a 1224.95a 1125.62ab 0.34a 120 57.42a 16.32b 132.91a 2629.91ab -120.37bcd 0.94ab 0.64a 1682.08a 1584.97ab 0.30b 160 54.1 9b 16.76b 121.10a 2650.63a -102.41ab 0.97ab 0.65a 1727.68a 1676.61a 0.32ab 240 56-71a 31 1 'l9.98a 2488.49ab -58a -142.79d 0.99a 0.65a 1616.80a 1602.94ab 0.31b Roblin
0 63.07a 27.51c 127.38a -1 '13.03a 2245.67a 1 .06a 0.73ab 1630.30ab 1719.37abc 0.38c 40 59.66cd 25.02d 127.89a 2039.60c -156.56a 0.98a 0.73ab 1483.70d 1451.59cd 0.38bc erì 58.35d 31 125.68a .14b 1928.28d -108.44a 0.99a 0.72b 1390.29e 1373.24d 0.40ab 120 61.23bc 119.17a 30.66b 2158.71b -97.33a 1.15a 0.73ab 1581.80bc 1815.35a 0.39bc 160 62.50ab 34.74a 117.62a 2283.22a -133.55a 1.07a 0.73ab 1667.81a 1785.67ab 0.39bc 240 62.68ab 102.29a 34.61a 2059.39c -91.41a 0.98a 0.75a 1535.91cd '1507.3gbcd 0.41a AG Taber
0 52.73b 21 .87d 102.82bc 1621.54d -62.51a 0.95b 0.69b 1 1 18.87d 1059.90d 0.38b 40 52.B3b 121.48a 20.50e 181 0.91c -97.70b 0.99a 0.71ab 1294.31c 1283.47c 0.39ab BO 49.25c 22.86c 100.44bc 1 533.90d -60.98a 0.99ab 0.69b l 053.95d 1 040.1 8d 0.41a 120 60.05a 25.36b 115.45ab 1962.46b -84.82ab 0.99ab 0.74a 1453.22b 1431.58bc 0.40ab 160 59.74a 31.82a 110.45a 2040.04b -68.12a 0.9Bab 0.74a 1501 .57b 1471.91b 0.40ab 240 60.27a 32.34a 86.26c 2330.41a -1 00.50b 0.97ab 0.72ab 1 686.37a 1633.24a 0.38b Means in the same column within a variety and heading followed by the same letter are not statistically different (P > 0.05) by t multiple range test. 2 Abbreviations as defined in Table 23. 234 Table 28 Multiple Comparisonsr of Cooked Noodle Texture2 in Optimum Cooking Time Test for 1995 Spring Wheat Cultivars
N rate RECOV CS HARD ADHE SPRING COHES GUM CHEWN RESI Glenlea
0 5'l .73cd 20.24d 69.88d 1B14.80e -1 82.86ab 0.97ab 0.71a 1295.12d 1250.45d 0.37a 40 49.91d 1 8.99e 102.65a 1829.78e -218.12b 0.95b 0.71a 1292.53d 1230.57d 0.36a
BO 51 .91c 21 .64c 83.79c 1 928.1 0d -1 94.00ab 0.95b 0.72a 1380.87c 1317.97c 0.37a 120 54.60b 23.76b 93.99b 2035.89b -215.62b 0.96ab 0.72a 1462.31b 1409.60b 0.37a 160 55.70ab 25.61a 97.95ab 1 983.1 0c -162.48ab 0.98ab 0.71a 1411.23c 1377.07b 0.37a 240 57.66a 24.86a 93.10b 2166.57a -141 .B4a 0.99a 0.71a 1539.19a 1517.B6a 0.36a Katepwa
0 60.09a 23.10b 100.25a 2223.32b -148.82ab 1.00a 0.74ab 1639.53b 1634.35ab 0.40a gab 40 55.02c 20.06e 95.1 2010.41d -1 09.99ab 0.98a 0.73ab 1464.45d 1434.20b 0.39a BO 56.51bc 22.66bc 86.1 6b 2222.28b -143.27ab 1.15a 0.73ab 1630.08b 1869.67a 0.39a 120 55.79bc 22.21cd 92.49ab 2103.44c -156.30ab 0.97a 0.73b 1528.27c 1481 .82b 0.39a 160 57.04b 22.16d 91.17ab 2458.65a -80.70a 0.99a 0.70c 1718.21a 1709.05ab 0.36a 240 59.15a 26.28a 94.05ab 2284.32b -'169.38b 0.96a 0.74a 1697.61a 1632.92ab 0.40a AG Reed
n 53.06b 14.82d 1 28.03abc 1981 .50a -1 09.56ab 1.23a 0.70a 1378.51a 1693.58a 0.36a 40 50.17d 13.01e 131 .40ab I 837.85b -86.34a 1.13a 0.70a 1277.21b 1451.42a 0.35a BO 50.54cd 15.09d 1 03.1 4c 1791 .57b -118.17b 0.97a 0.70a 1260.18b 1215.91a 0.37a 120 53.03b 16.12c 129.79ab 1 968.90a -1 06.57ab 1.22a 0.70a 1372.45a 1670.17a 0.35a 160 52.49bc 17.05b 107.53bc 1 955.1 6a -99.1 2ab 1.15a 0.71a 1382.13a 1593.04a 0.37a 240 55.70a 18.31a 145.58a 201 8.30a -92.99ab 1.05a 0.70a 1404.34a 1470.16a 0.35a Roblin
0 56.74bc 22.10e 98.31ab 2164.31b -229.96ab 0.95a 0.73b 1568.75c 1498.08ab 0.38a 40 56.05bc 20.80f BB.21bc 1887.48d -171.43a 0.96a 0.73ab 1382.33e 1328.11d 0.39a BO 57.17ab 22.73d 108.07a 2028.14c -208.40ab 0.94a 0.74ab 1491.90d 1404.16cd 0.40a
120 55.20c 24.13c 97.93ab 2063.57c -215.83ab 0.95a 0.73ab 1 505.71d 1424.04bc 0.39a 160 58.79a 26.84a 102.60a 2268.76a -280.24b 0.94a 0.73ab 1657.70a 1563.85a 0.39a 240 57.55ab 25.24b 85.1 5c 2178.78b -183.92a 0.97a 0.7 4a 161 6.00b 1572.35a 0.39a AG Taber
0 53.48cd 20.64c 89.23a 1951 .54c -123.55ab 0.99a 0.71cd 1392.87bc 1378.59b 0.37c 40 50.43e 18.86d 85.97a 1791 .8Be -118.32ab 0.98a 0.72cd 1288.50d 1262.73c 0.37bc BO 52.62d 19.41d 87.41a 1 889.1 6d -110.47ab 0.98a 0.73bc 1368.83c '1334.76bc 0.39bc 120 55.03bc 20.26c 71.31b 2015.20b -145.25b 0.97a 0.71d 1429.50b 1393.41 b 0.37c 160 56.65b 22.13b 91.67a 1485.91f -47.77a 0.98a 0.76a 1130.06e 1112.61d 0.43a 240 58.82a 25.26a 93.28a 2153.41a -1 53.14b 0.99a 0.73b 1579.79a 1560.05a 0.39b t M.un, in the same column within a variety and heading followed by the same letter are not statistically different (P > 0.05) by t multiple range test. 2 Abbreviations as defined in Table 23. 235 Tabte 29 Multiple Comparisonsr of Cooked Noodle Texture t in Optimum Cooking 'Winter Time Test for 1994 Wheat Cultivars
N rate RECOV GS SFM HARD ADHE SPRING COHES GUM CHEWN RESI CDC Kestrel
0 59.55a 27.07d 82.08c 2281.08b -109.29ab 0.90b 0.69b 1583.52b 1424.64bc 0.35b 40 56.65a 30.54c 148.01a 2611.26a -165.00c 0.97a 0.70b 1819.54a 1772.15a 0.36b 80 57.72a 23.18e 118.82b 2652.24a -147.40bc 0.95ab 0.68b 1801.28a 1709.19a 0.34b 120 58.40a 32.53b 120.89b 2185.71c -121 .56abc 0.96ab 0.72a 1583.41b 1520.02b 0.36b 160 59.17a 32.88ab 104.88b 2065.09d -101.B5ab 0.97a 0.73a 1507.69c 1466.34bc 0.39a 240 56.69a 33.54a BB.31c 1 900.73e -90.30a 0.99a 0.72a 1374.91d 1358.98c 0.40a Norstar
0 61.49a 28.51c 119.82b 1 539.1 0b -160.46bc 0.99a 0.73a 11'13.38b 1099.98b 0.35a 40 60.34ab 22.93d 98.1Ocd 2758.38a -149.01bc 0.94c 0.67c 1845.58a 1735.34ab 0.32b 80 57.96bc 22.92d 84.86de 2327.93ab -1 03.1 1a 0.98a 0.70b 1620.53ab 1586.45ab 0.35a 120 60.00ab 28.55c 80.77e 2696.56a -124.39abc 0.98ab 0.69b 1865.86a 1820.54a 0.34ab 160 57.31c 32.51b 107.49bc 2546.13a -164.91c 0.97ab 0.68bc 1735.42ab 1684.78ab 0.34a 240 60.73a 36.42a 136.10a 2541 .16a -122.49ab 0.95bc 0.68bc 1731.40ab 1645.98ab 0.34ab s86-375
0 52.67b 17.03b 108.01ab 2429.64a -117.40a 0.96a 0.65c 1570.95a 1511.27a 0.31a 40 56.57a 18.12b 93.87b 2661 .27a -1 39.35a 0.97a 0.66bc 1748.28a 1695.03a 0.32a 80 58.05a 20.22ab 110.53ab 2579.82a -1 09.80a 0.90a 0.66bc 1688.99a 1523.76a 0.32a 120 56.02ab 20.65ab 128.81a 141 3.95b -121 .54a 0.82a 0.67b 932.48b 905.40b 0.24a 1 60 59.51a 24.31a 103.35ab 2814.86a -124.50a 0.97a 0.68b 1900.25a 1851.22a 0.34a 240 57.53a 21 .50ab 102.51ab 2377.92a -159.24a 0.98a 0.70a 1658.12a 1626.65a 0.37a s86-1 01
0 58.37cd 20.05d 121 .24bc 2961 .04b -173.62b 0.98a 0.65c 1927.95c 1891.49b 0.29c 40 56.01d 22.91c 104.40c 2348.87c -122.17a 0.98a 0.70a 1647.04d 161 9.36c 0.35a 80 58.01d 23.68c 131.85ab 2891.55b -1 86.30b 0.96a 0.67b '1933.91c 1865.95b 0.32b 120 61.36bc 27 .11b 1 40.05ab 2961 .29b -164.82ab Q.97a 0.68b 2000.91b 1939.27b 0.32b 160 64.52a 29.80a 150.03a 3'r 94.39a -197.'l9b 0.97a 0.67b 2151.02a 2088.51a 0.32b 240 61.54ab 29.87a 122.68bc 3283.31a -202.09b 0.98a 0.67b 2203.29a 2163.15a 0.32b Winalta
0 59.47d 23.65e 87.53d 1647.88e -69.92a 0.97b 0.74a 1221.27e 't 183.59e 0.42a 40 57.65d 23.44e 107.64c 2145.33d -170.21bc 0.96b 0.71b 1520.96d 1459.02d 0.37b 80 63.21c 24.41d 105.61c 3089.30a -1 7B.55bc 0.97b 0.67c 2057.88ab 1988.94b 0.31c 120 64.3't bc 26.12c 130.95b 2535.23c -102.76ab 0.99ab 0.69bc 1743.23c 1725.83c 0.36b 65.84ab 152.30a 160 27.69b 2833.20b -209.95c 0.96b 0.70b 1994.17b 1 91 5.56bc 0.36b ''l39.84ab 240 67.42a 28.89a 2993.70a -119.20ab 1.04a 0.71b 2125.15a 2208.79a 0.36b t M.urn in the same column within a variety and heading followed by the same letter are not statistically different (P > 0.05) by t multiple range test. 2 Abbreviations as defined in Table 23. 236 2 Table 30 Multiple Comparisonsr of Cooked Noodle Texture in Optimum Cooking Time Test for 1995 Winter Wheat Cultivars
N rate RECOV cs HARD ADHE SPRING COHES GUM CHEWN RESI CDC Kestrel 51.B2bc 0 1B.02cd 106.72ab 1918.75b -120.97ab 0.95a 0.72b 1373.89b 1309.89ab 0.37bc
48.40d 1 40 16.07e 16.59a 1 793.30cd -1 09.1 1 ab 0.98a 0.70bc 1255.48c 1229.23b 0.36c 50.14cd B0 17.81d 96.56b 1824.45c -102.27ab 0.98a 0.70c 1268.38c 1239.76b 0.37c 120 52.78ab 18.38c 100.67b 1795.40cd -171.58b 0.94a 0.72ab 1292.2Oc 1211.35b 0.39ab 160 53.10ab 20.87b 105.'t6b 1733.84d -107.49ab 0.97a 0.74a 1279.46c 1236.88b 0.40a 240 53.96a 21 .74a 104.33b 2136.19a -84.13a 0.96a 0-69c 1466.95a 1405.58a 0.36c Norstar 53.63b 0 17.66d 132.73a 2148.13a -101 .95a 0.99ab 0.63c 1362.29b 1348.14bc 0.30d
45.99c 'l 1 40 17 .17d 1 .51bcd 1701 .90c -141 .B9a 0.97b 0.70a 1190.49d 1158.46d 0.36a B0 52.48b 22.90b 125.26ab 1 982.6Bb -137.40a 0.99ab 0.68b 1338.7Bb 1323.37bc 0.33bcd 53.64b 120 20.54c 93.15d 1 945.01b -109.25a 0.97b 0.65c 1270.35c 1230.51cd 0.32cd 56.19a 160 22.83b 100.83cd 2107.47a -164.26a 0.9Bab 0.70a 1472.91a 1445.40ab 0.35ab 240 55.83a 24.00a 118.61abc 2106.84a -112.41a 1.07a 0.69ab 1461.55a 1565.84a 0.35abc s86-375
0 49.81b 15.81c 136.54b 2022.69b -213.83b 0.95a 0.67ab 1358.10b 1287.35bc 0.33b ''t5.10d 40 44.95c 120.58c 1726.74d -125.91a 0.97a 0.67b 1148.22d 1108.09d 0.33b 80 49.77b 15.38d 135.14b 1691.52d -61 .73a 0.98a 0.68ab 1149.25d 1121 .03d 0.37a 51.46b 120 17.12b 132.09bc 1920.28c -130.35a 0.96a 0.68ab 1300.03c 1250.34c 0.35ab 55.90a 160 17.38b 158.90a 2232.41a -140.55ab 0.98a 0.67b 1487.64a 1458.02a 0.33b 240 57.70a 19.69a 139.49b 1961.57c -113.48a 0.98a 0.69a 1355.60b 1329.65b 0.36ab s86-1 01
0 54.91b 21 .29d 132.23ab 1922.41cd -93.29a 0.98a 0.74a 1412.58b 1389.97b 0.40a 40 54.87b 20.11e 107.88c 1880.44d -75.63a 0.98a 0.71c 1327.53c 1299.84c 0.38a 55.31ab B0 21 .71cd 134.67a 1982.77b -124.20a 0.97a 0.72bc 1418.87b 1379.09bc 0.38a 55.B4ab 120 22.35b 138.86a 1 952.61 bc -76.78a 0.99a 0.72b 1406.23b 1390.62b 0.38a 160 57 .16a 2210bc 118.34bc 2064.07a -101 .34a 0.99a 0.73ab 1497.42a 1480.24a 0.39a 240 56.B2ab 27.64a 117.85bc 2068.54a -93.55a 0.99a 0.72ab 1494.56a 1476.43a 0.38a Winalta 58.06b 't.00a 0 22.62e 131.56a 2335.94ab -88.1 8a 0.71a 1665.21a 1660.12a 0.37ab 56.99b 40 22.39e 116.45b 2253.84bc -1 53.31a 1.20a 0.71a 1592.23b 1912.95a 0.35b 80 55.58c 95.44c 23.27d 2223.02c -126.83a 1.02a 0.71a 1573.89b 1 608.1 7a 0-36ab 120 58.28b 102.11c 26.67b 2192.00c -'108.04a 0.99a 0.72a 1575.54b 1 557.89a 0.38a 160 60.51a 1 19.65ab 25.74c 2218.92c -106.40a 0.99a 0.72a 1591.80b 1 582.90a 0.38ab 240 60.64a 27.41a 125.27ab 2347.51a -108.12a 1.10a 0.71a 1677.67a t 1844.89a 0.37ab M.un, in the same column within a variety and heading followed by the same letter are not statistically different (P > 0.05) by t multiple range tesr. 2 Abbreviafions as defined in Table 23. 237 Table 31 Protein Parametersr for Chinese and Canadian Wheat varieties
Variety MPF SGF IGF MPP src20 stcS stco SIGSP SIGOP Canadian
90w950 10.88 7 .05 0.72 2.63 64.80 6.62 24.17 4.23 4.36 4.06 3B.BB 40.07 37.32 Alpha'13 10.22 6.28 '1 .19 2.15 61 .45 11.64 21 .04 3.57 3.85 3.89 34.93 37.67 38.06 Biggar 10.42 5.52 1.56 2.65 52.98 14.97 25.43 5.01 5.32 4.89 48.08 51 .06 46.93 AC Domain 14.27 7.94 1.64 3.70 55.64 11.49 25.93 6.06 6.02 5.24 42.47 42.19 36.72 '1.80 Fielder 10.17 5.55 1.76 54.57 17.70 17.31 3.26 3.73 4.23 32.06 36.68 41.59 Genesis 10.37 5.81 1.50 2.55 56.03 14.46 24.62 4.81 5.11 4.69 46.38 49.28 45.23 Glenlea 12.53 6.23 1.24 3.95 49.72 9.90 31 .52 6.38 5.97 5.25 50.92 47.65 41.90 HY 367 11.03 6.07 1.50 2.83 55.03 13.60 25.66 4.87 5.10 5.07 44.15 46.24 45.97 AC KARMA 11.11 6.44 1.s5 2.78 57.97 13.95 25.02 4.76 5.02 4.62 42.84 45.18 41.58 AC VISTA 11.39 6.21 1.21 3j2 54.52 10.62 27.39 5.37 5.40 4.77 47.15 47.41 41 .88 HY 616 10.87 6.49 1.08 2.87 59.71 9.94 26.40 4.78 5.03 4.52 43.97 46.27 41.58 HY 627 10.82 5.31 1.38 2.77 49.08 12.75 25.60 4.93 5.21 4.99 45.56 48.15 46.12 Neepawa 13.36 7 .85 1.57 3.36 58.76 11.75 25.15 5.64 5.64 4.77 42.22 42.22 35.70 Ghinese
Jimai 36 12.28 6.81 1.45 2.75 55.44 11.80 22.40 3.93 4.06 3.17 31.96 33.06 25.81 Linfeng 7203 8.30 4.45 0.99 1.99 53.64 11.87 24.04 3.63 3.66 3.02 43.73 44.04 36.33 Nongda 010 9.28 5.34 1.24 1.80 57.52 13.33 19.40 3.46 3.51 2.87 37.28 37.77 30.93 Wen 2540 12.38 4.BB 2.15 4.00 39.43 17.33 32.31 5.37 5.33 4.50 43.34 43.01 36.35 Wenmai 4 11.77 5.85 2.03 3.16 49.74 17.28 26.85 4.96 4.96 4.28 42.14 42.10 36.32 Xian I 11.38 5.08 2.71 3.07 44.58 23.84 27.01 4.28 4.36 3.19 37.61 38.31 28.03 Yumai 10 11.58 s.99 1.41 2.92 51.76 12.16 25.21 4.08 4.02 3.20 35.19 34.67 27.63 Yumai 18 9.10 3.89 1.81 2.37 42.75 19.90 26.03 3.66 4.25 3.66 40.22 46.65 40.16 Yumai 2 11.22 5.77 2.04 2.76 51.45 18.18 24.65 4.56 4.75 4.04 40.64 42.34 35.96 Zhong214 11.97 6.69 1.62 2.97 55.91 13.50 24.82 3.97 4.19 3.38 33.'17 34.96 28.24 Zhongyu 4 11.37 6.63 1.17 2.33 58.29 10.26 20.52 3.96 4.16 3.46 34.78 36.59 30.39 Zhoumai 10 9.71 5.12 1.74 2.40 52.72 17.96 24.72 4.04 4.40 3.48 41 .56 45.26 35.84 Commercial '10.43 Lamianfen 5.24 1.47 2.90 50.24 14.09 27.80 4.06 4.17 3.94 38.88 39.93 37.73 Udon 8.85 4.39 1.19 2.75 49.58 13.47 31 .13 4.30 4.31 3.45 48.59 48.70 38.93 I Abbreviations as defined in Table 1, 238 Table 32 Quality Parametersl for chinese and canadian wheat varieties
Farinograph RVA Variety GI DDT ART STA I\ITI BKD Canadian 90w950 3.9 2.1 6.1 59 6l .5 77 8.0ó 137 68 Alpha l3 3.1 t.6 5,9 84 57.8 s8 8.90 220 93 Biggar 5.5 2.3 t7.7 31 58.7 84 838 230 90 AC Domain 5.6 3.5 t6 s l5 64.4 79 7.81 240 98 Fielder 2.1 1.2 3.2 142 57.3 60 ll.l4 J ¿I 192 Genesis 5.3 2.7 t2.6 32 6t.l l3 9.28 217 r38 Glenlea l s.6 2.5 17.5 0 58.3 99 8.00 199 94 HY 367 6.0 3.5 1 3.ó 4t 60.7 76 206 8ì AC KARMA 5.3 2.7 t 3.5 47 58.9 72 ö.to 246 108 AC VISTA 6.3 3.0 r 5.8 47 63.0 84 7.81 2r8 88 FIY 6I6 5.4 2.5 16. I 40 60.8 82 7.74 250 96 I-tY 627 5.7 3.1 t6.9 30 60.6 ÒJ 9.22 237 I t3 Neeparva 5.9 3.6 10. I 29 61.8 50 6.98 186 69 Chinese
Jimai 36 4.1 2.9 2.3 75 62.0 +J 6.66 269 109 Linfeng 7203 1Á 1.8 1.3 40 57.8 80 t.t7 242 95 Nongda 010 3.6 1.4 3.7 85 60.7 46 6.94 262 102 Wen 2540 9.5 7.0 5.7 20 59.7 15 8.34 279 112 Wenmai 4 8.9 l.l 10.8 30 58.0 69 8.02 ¿Ò¿ I l6 Xian 8 3.6 2.8 t.7 85 63. I 48 6.76 269 n2 Yumai l0 4.9 t.8 3.9 80 59.5 75 6.48 273 l0l Yumai l8 1.7 1.2 1.2 40 s9.2 oð 6.91 287 111 Yumai 2 6.3 1.0 6.6 55 56.0 47 10.02 275 117 Zhong2l4 t.7 1.2 ¿.ö 70 57.5 5l 8.64 239 96 Zhongyu 10 4 4.0 2.2 75 6l .1 5'l 9.93 262 il0 Zhoumai l0 3.5 2.4 2.4 95 59.0 52 8.45 239 89 Commcrcial
Lamianfen 1.8 1.2 4.t 40 63. I 48 8.20 237 ôô Udon 2.1 18.5 0 51 .2 10.49 280 123 t pv: RVA peak viscosify (RVU); BKD : RVA breakdown (RVU); other abbreviations as defined in Table 10. 239
Table 33 Variationsr of Noodle Making Quality parameters2
Variety DSL THICK Rmax AREA Canadian 90w950 60.00 f-h l.3l b-g 9t.21 f-i 363.60 g-i 19.5s g-i Alpha l6 62.20 de 1.25 c-j 84.29 h-t 195.55 l-n 12.70 k-n Biggar 55.40 i-k I .35 l¡-c 80. l 9 j-r 1015.30 c 51 17¡ AC Domain 54.30 k 1.40 ab 85.96 g-k 1293.09 b 60.22b Fieider 63.80 bc Ll9 g-k 66.52 nr 246.2s j-l 18.69 g-j Genesis 55.80 i-k 1.34 b-f 91.02 f-í 4s1.04 fg 23.38 g GIen lea 41 .901 1.52 a I 10.60 ab ll'19.88 a 70.72 a tIY 367 s5.60 i-k 1.41 ab 9t.22 r-j 971 .59 c 44.36 d AC KARMA 56.90 i 1.37 bc 86.8a g-j l03l .91 c 49.46 cd AC VISTA 54.60 k 1.37 b-d 97.64 d-f 665.59 e 29.36 f HY 616 56.s0 ij 1.35 b-e 83.40 i-l 754.56 de 38.18 e HY 621 56.40 ü l.3 t b-h 85.07 g-l 806.01 d 38.76 e Nee parva 56.70 i t.29 b-i 79.36 j-t 1247.00 b 6l .08 b Chincse
.limai 36 63.05 cd 1.20 g-k I 10.78 ab 340.46 h-j 16.34 h-k Linfeng 7203 64.50 a-c l. r5 jk 61.55 m 69.34 o 7.34 o Nongda 010 65.50 a r.l0 k 76.71]' 99.71 no 8.27 t'to Wen 2540 58.50 h l 3ó b-d 114.70 a 477.44 f 20.14 gh Wenmai 4 59.70 gh 1.22 e-k 105.09 b-d 314.30 i-k 15.64 h-k Xian 8 1 )1ø-lt 61 .50 ef 98.10 d-f 421.42 f-lt 20.30 gh Yumai l0 65.30 ab l.l2 jk 17.47 kt 209.83 k-m 14.30 i-l Yumai l8 65.20 ab 1.20 gk 83.91 h-t 126.84 m-o 9.40 l-o Yumai 2 62.20 de l.r7 i-k 93.85 e-g 265.3s i-t 15.15 h-k ZhongZl4 64.50 a-c l.l8 h-k 97.s8 d-f 2s0.09 j-l r4.l9j-l Zhongyu 4 65.00 ab l.r8 h-k l0l .02 c-e 215.15 k-m 12.24 k-o
Zhounlai 1 0 64.50 a-c l.r7 i-k 109-4'7 a-c 24s.67 j-l 12.7 5 k¡t Comnrercial
Lanrian 59.75 gh |.22 f-k 113.27 ab 160.121-o 8.78 m-o Udon 60.50 fg t.24 d-j 92.79 e-h 238.20 j-l I 3.83 j-m ' M"u.r, in the same column and heading followed by the same letter are not statistically different (p > 0.05) by t multiple range test. 2 Abbreviations as defined in Table 18. 240
Table 34Yanationsl of Noodle Cooking euality parameters2
Canadian
90w950 7.59 fg 2.75 c-g 450 g 1.95 gh Alpha 13 7.28hi 2.19 c-f 350 mn 1.91 h Biggar 7.70 ef 2.73 d-h 430 hi 2.08 bc AC Domain 7.03 ij 2.69 f-I 710 a 2.04 de Fielder 7.52 f-h 2.94 a 390 i-k 1.85 i Genesis 7.58 fg 2.12 d-h 550 d 1.99 fg Glenlea 6.e6 j 2.s9 jk 630 b 2.16 a HY 367 7.01 j 2.6s h-k 470 f 2.11 b AC KARMA 6.65 k 2.75 c-g 440 gh 2.05 cd AC VISTA 7.42 i-k 2.61i-k 590 c 2.11b HY 616 7.01 ij 2.74 d-h 420 i 2.00 ef HY 627 7.45 f-h 2.'/7 c-f 400 j 2.05 cd Neepawa 6.62k 2.70 e-I 450 g 1.98 fg Chinese Jimai 36 7.48 f-h 2.73 d-h 355 mn 1.7s j Linfeng7203 9.01 b 2.85 a-c 340 no t.7s j Nongda 010 8.78 b 2.77 c-f 250 p 1.72 jk Wen 2540 6.68 k 2.s6k 600 c 1.72 jk Wenmai 4 7.s4 f-h 2.55 k 535 de 1.12 jk Xian 8 7.51 f-h 2.12 d-h 330 o 1.7s j Yumai 10 8.36 c 2.66 g-j 365]m 1.71 k Yumai 18 10.02 a 2;71e-h 360 m 1.76 j Yumai 2 7.53 f-h 2.17 c-f 380 kt 1.71 k Zhong2l4 7.53 f-h 2.81 b-d 520 e 1.74 jk Zhongyu 4 8.38 c 2.85 a-c 420 i 1.74 jk Zhoumai 10 8.01 d 2.80 c-e 360 m 1.71 k Commercial Lamian 7.88 de 2.11 c-f 410 f Udon 8.04 d 2.91ab 390 ik ' Means in the same column and heading followed by the same letter are not statistically different (p > 0.05) by t multiple range test. 2 Abbreviations as defined in Tables lB and 22. 241
Table 35 Variationsl of Cooked Noodle Texture Parameters2 in Constant Cooking Time
Variety SFM HARD ADHE SPRI COHE CHEW RESI Canadian 90w950 as.l9g-j 2t .04ef 197.45bc 193s.62j-l -l 70.89t-h 0.94bc 0.7ja-e I 358.0sdg 1282.29f-h 0.36a-e Alpha l6 aa.90g-j I 8.95k 196.79bc 1859.83mn -203.72gh 0.94bc 0.'/2a-c 1332.78f9 1248.19g-i 0.36a-e Biggar 43.62ij 23.92d 2014.51g-i 207.70b -l 47.60e-lr 0.97b 0.7 I a-d 1427.30ce 1371.56c-f 0.35b-f AC Domain 47.4tfg 25.91b 236.89a 2252.49t) -93.04a-e 0.99b 0.73a 1633.02a 1614.09a 0.37a-d Fielder 42.e0j 19.t3ij t4l.22gh 1328.21q -9 ì .56a-e 0.95b 0.72a-c 956.661 910.871 0.37a-c Genesis 45.96g-i 20.67fg 171.39de 1738.33o - I 36.89c-g 0.89c 0.69c-f l 208.s2ü 1084.01k 0.37a-e Glenlea 46.50f1r 28.56a 200-73bc 2100.1 I c-f -169.531--h 0.97b 0.7|a-c I 496.98bc 1449.3Obc 0.36a-e l:IY 367 45,89g-ì 25.82b 201.66bc 2ll3.5lc-e -216.6óh 0.95b 0.72a-c I 525.29b 1449.22bc 0.37a-d AC KARMA 47.1 I f-h 24.12d 2059.35d-h 201.\]bc -l 69.3óf-h 0.95b 0.72a-c I 485.89bc 1407.2lcd 0.38a AC VISTA 44.471'r-1 25.28c I 99.541¡c 2035.44f-h -14ì.61d-g 0.98b 0.70a-e 1432.22cd 1399.27c-e 0.35b-f FIY 616 46.59f-h 24.30d 196.97bc 1992.56h-j - I 73.20f'-h 0.96b 0.72a-c 1424.96c-e I 364.8 1 c-f 0.36a-e HY 621 46.32f-i 24.93c 171.17de 1828.63n -148.1 le-h 0.96b 0.72ab 11)4O)îo t27l.l3f-i 0.38ab Neeparva 45.1 1 g-j 24.91c 231.95a 2128.161:c -121.34b-f 0.97b 0.70a-e 1491.l5bc 1453.26bc 0.35c-g Chinese
Jimai 36 52.83e l7.29m 160.1 lef 2081.73c-g -41 .52a 0.96b 0.63ù l3l 9.9sfg 1268.73f-i 0.32h-j Linfeng 7203 48.971 14.96o 127.35hi 1877.521-t¡ -28.ó9a 0.98b 0.64ij l 194.l0i-k 1 165.70i-k 0.32g-j Nongda 010 52.93e 13.7 6p 143.22f-h I 949.51 i-k -23.óla 0.95b 0.60k l l 73.28jk l r l5.t7jk 0.3tj Wen 2540 I 9.83hi 62.58a 184.29cd 2218.17b -77.65a-e 0.98b 0.67f-lt 1482.61bc 1450.04bc 0.35c-f Wenmai 4 57.09b 21.53e 149.7 6fg 2146.31c -49.94att 0.98b 0.66gh 1422.44c-e 1400.07c-e 0.34d-h Xian 8 56.s2b t9.gthi l3 L35hi 1922.90je'n -85.48a-e 0.99b 0.68d-g 1309.91 fg 1292.48e-h 0.36a-e Yumai l0 47 .49fg I 4.51 o 130.30hi 1745.88o -85.09a-e 0.96b 0.65hi 1127.02k I 083. I 7k 0.33fJ Yumaì l8 56.4lbc 16.27n 2337.20a 159.92ef -65.47 a-c 0.98b 0.64ij I 485. I 4bc t447.43bc 0.32ij Yumai 2 56.84b 20.24g1t 137.00g-i 1991.40h-k -70.95a-c 0.98b 0.70b-f I 384.07d-f I 35 1.80c-g 0.36a-e Zhong2l4 53.53de I 5.97n I 36.1 9g-i 2060.20d)l -40.83a 0.96b 0.62jk I 268.20hi l2 r 5.04h-j 0.3 I j Zllongyu 4 53.69c-e 18.081 149.7619 2047.43e-h -70.79a-d 0.97b 0.66gh I 350. I 2e-g 1316.03d-h 0.34e-i Zhoumai l0 56.26bd 17.831 122.94i l92l .24k-n't -40.68a 0.98b 0.68e-g 1298.47gh 1274-l2f-i 0.38ab Conrmercial
Lamian s6.56b l9.51ij 90. l sj 2070.82
Table 36 Variationsl of Cooked Noodle Texture Parameters2 in Optimum Cooking Time
Varietv RECOV CS SFM HARD A.DHE SPRI COHE GUM CHEW RESI Canadian
90w950 I 8.58ì I ss.88g-j 20. I 9c-f 2 I 30.3Og-i -132.52b-g 0.97bc 0.70gh I 493.88f-i 1450.94e-j 0.37e-j Alpha l3 17.90m 56.99e-h 127.35b-e 2203.14e-g -123.63b-f l.l8a 0.70f-h I 550.74ef l83l.44ab 0.36g-k Biggar 53.491-o 2l .95fg l0l .48f-i I 930. I 6m -169.79e-h 0.97bc 0.12c-l 1382.871-o r333.81 i-t 0.37d-i AC Domain 24.83c I 09.20e-ì 2231 60.95b .97d-f -l 08.00a-e l.00bc 0.73b-d I 623.98cd I 6l 7.81c-f 0.38b-e
Fielder 53.03m-o 20.61h I 1 1882.89mn 9.00c-f -91.34a-d l.00bc 0.73b 1378.80m-o I 377.59h-k 0.39b Genesis 52.49n-p 20.94h 94.33hì 2043.37 jk -133.56b-g 0.99bc 0.70hi 1422.57 j-n't 1404.24g-j 0.34k-¡r Glenlea 55.29h-l 25.62b 98.04hi 2095.16ij -209.82h 0.96bc 0.7 I e-g I 491 .40g-j 1430.52t-j 0.36h-l t-tY 367 23.89d 56.73e-h I 14.50c-h 20t4.27k1 -l 13.90b-f 0.99bc 0.73bc t464.97h-j 1454.95e-j 0.37c-h AC KARMA 58.73c-e 23.77de 1 I 8.65c-g 2116.77f-l't -l l4.3lb-f l.02bc 0.72b-e 1572.85de 1597.54c-g 0.38b-f AC VISTA 23.34e I 54.64i-m 04.38t-i 2 r 03.45h-j -l 80.85|-h 0.98bc 0.71e-g 1491 .42f-h 1461.7)e-j 0.35i-l HY óI6 g 55.59h-k 21 .87 I I 8.45c-g 2292.92cd -181.20f-h l.09ab 0.71d-g 1635.60c 1775.22a-c 0.36g-k HY 627 20.12i s4.33j-n 133.24a-d 2029.9sjk -l 78.161'-h 0.97bc 0.7 le-g 1442.3lll-k 140s.53f-j 0.36f-j Neeparva 25.06c 132.12a-d 60.34bc 2354.92c -136.49b-g 0.99bc 0.7Ìd-g 1618.63c 1662.95b-e 0.35j-nr Chincse
.limai 36 s5.93g-j 19.24k l48.l5a 227 1 .53de -83.66a-d 0.97bc 0.68jk 't542.76e-g 1503.19e-i 0.33nr-o
Linfeng 7203 53.20m-o 15.60o 1 14.5 I clr l 9l 3.35m -86.38a-d 0.96bc 0.651 1242.34p I I 88.02k1 0.32no Nongda 0 I 0 13.18p 127.68b-e 49.651 1950.981m -84.32a-d 0.94c 0.62m 1202.91p 1134.461 0.28p Wen 2540 28.00a 64.94a 114.27e-l't 2187.80a -l 30.39b-g l.00bc 0.72c-l 1994.45a 1984.24a 0.39bc Wenmai 4 25.2lbc I 61.28b 25.90b-e ì 898.67nrn -73.43a-c 0.98bc 0.15a 1424.95j-m 1393.04g-k 0.42a Xian 8 59.40b-d 22.22fg 129-40a-e 2 I 86.93fg -90. I 9a-d 0.99bc 0.71d-g 1560.78e 1547 -57d-h 0.37c-g Yumai I0 51.93o-q 16.48n 1 34.58a-c 2061 .99i-k -l 78.70f-h 0.97bc 0.69ij t422.31j-m 1376.81h-k 0.35j-m Yunlai l8 50.2lqr 16.17n 125.46b-e 2069.94i-k -142.93c-lt 0.94c 0.651 1339.75o 1264.42j-l 0.29p Yumai 2 22.37f 56.74e-h 120.21c-f 2666.94b -l91.2lgh 0.97bc 0.68jk l8l 5.621) 17s9.45b-d 0.320 Zhotlg2|4 58.23d-f 19.73ij 126.44b-e 2295.76cd -42.80a 0.97bc 0.68j k 't 564.01e 1521.59e-i 0.35j-m
Zhongyu 4 57.88d-g I 9.6ljk 143.43ab I 2302.8 cd -100.92a-e 0.98bc 0.68jk 1559.20e I 53 1.52e-i 0.34I-n Zhounrai l0 53.83k-o l8.17lm 148.15a 2129.07g-i -1 20.05b-f 1.03bc 0.68k 1438.39i-l 1485.37e-i 0.33no Comnrercial Lamian t9.59jk 56.54f-i 91.72i 20 l 8.gokt -66-87 al¡ 0.96bc 0.70hi 1406.25k-n I 350.65h-k 0.38b-e Udon 50.53p-r 19.82ij 98.51hi I 838.04o -152.62d-h 0.98bc 0.7 4b l 35l .l 6no t319.17i-l 0.38b-d ' M.urrc in the same colurnn and heading followed by the same letter are not statistically different (p > 0.05) by t multiple range test. 2 Abbreviations as defined in Table 23. 243
Appendix II
swelling Index of Glutenin Test for Prediction of Durum wheat eualityt
ABSTRACT
The swelling index of glutenin (SiG) was assessed for its suitability as a screening procedure for
durum wheat gluten strength. Five sets of samples possessing a wide range of gluten strengtli were collected with their gluten strength parameters, and characterized for SiG. Statistical analysis revealed that the SIG and sodium dodecyl sulphate (SDS) sedimentation tests were both able to account satisfactorily for variations in gluten strength in all sets of samples. However, for most samples, the SIG test was more reliable than the SDS sedimentation test for predicting gluten strength. Furthermore, the SIG test can differentiate samples which possessed glutenin swelling properties that could not be accounted for by inter-cultivar variation of SDS sedimentation volumes. The percentage of insoluble glutenin in protein is usually a better predictor for gluten strength than the percentage of insoluble glutenin in flour. Similarly, when the SIG values were divided by the protein content of the samples, the resulting proportions were better predictors of gluten strength than the absolute values. Analysis of protein fractions revealed that the insoluble glutenin was the protein fraction most responsible for the SIG value and gh"rten strength. Extensograph extensibility of dough was significantly related to the soluble glutenin content, while the alveograph G index was related to monomeric protein content. The results suggest that screening based on the SIG test would be valuable for comparing durum wheat lines and cultivars for gluten strength and pasta-making quality.
' Published in cereal chemisrry e0o2) ig(2)t7g-2ozby c. wang and M. I. p. Kovacs 244
INTRODUCTION
Durum wheat is an important crop because it is used for the production of high quality pasta. The
semolina from durum wheat is suitable for pasta due to the biochemical characteristics of its
gluten proteins, which form a network in dough and give pasta its viscoelastic properties. in
duntm wheat semolina, the stronger the gluten, the better the quality (firmness) of the cooked
spaghetti (Matsuo and Irvine 1970). A high glutenin but low gliadin content was found to be
associated with firmness of the spaghetti (Walsh and Gilles lg71), and superior cooking quality
(Wasik and Bushuk 1975). The residue protein (insoluble in acetic acid solution) was responsible
for variations in gluten strength and cooking quality of spaghetti (Wasik 1978, Dexter and
Matsuo 1980, Matsuo et al. 1982, Sgrulletta and De Stefanis 1989), while gtiadin and glutenin
soluble in acetic acid solution were negatively related to gluten strength (Dexter and Matsuo
1 e80).
ln duntm wheat breeding programs, only small volumes of grain are avallable for testing in early
generations. The SDS sedimentation test has been a popular predictor of end-use quality in such
materials because it has a small sample size requirement and can be conducted in a relatively
short time. The sediment in the SDS and lactic acid solution theoretically results from the
swelling of the glutenin strands (Eckert et al. 1993). High SDS sedimentation volumes (SDSV) have been associated with stronger gluten and superior pasta-making quality (Dexter et at. 1980).
Cultivars with different protein quality, as expressed by their gl¡ten characteristics, should be differentiated by the SDS sedimentation test. 245
Recently, the swelling index of glutenin test (SIG), originally developed in our lab for
monitoring common wheat quality, has been found more reliable for predicting insoluble
glutenin content and dough strength parameters than sedimentation tests (Wang and Kovacs
2002a,b)' Our study found that the SIG test promotes strong swelling of glutenin and yields
better results because it is based totally on insoluble glutenin content, all soluble protein having
been completely removed. On the other hand, the sedimentation test develops after only mild
swelling caused by the presence of residual soluble glutenin.
To date, most studies on the swelling properties of glutenin from durum wheat have been based
on the SDS sedimentation test. Our goal was to adapt the SIG test so that it could be used to
measure durum wheat qualities in the early generations of breeding progïams. Specif,rcally, our
objectives were: to examine the effects of test conditions and protein composition on the glutenin
swelling capacity, in order to compare the SIG test with the SDS sedimentation test as predictors
of durum gluten strength and pasta-making quality and; to determine the variation in the SIG and
SDS sedimentation tests due to protein composition.
MATERIALS AND METHODS
Samples and Quality Data
Method evaluation samples, harvested in 1990, included 72 vaneties with a wide range of quality, and these quality parameters and their values have been published (Kovac s et al. l9g7b). 246
In addition, three years of durum Co-op samples (95 DURC, g6DllRC and g8DtIRC) were
provided by Dr. B. Marchylo (the Grain Research Laboratory, Canadian Grain Commission,
Wiruripeg, MB, Canada), and used in this study for comparison of the SIG test with the SDS
sedimentation test and gluten strength. Their quality parameters (mixograph dough developing
time (MDT), alveograph W index, gluten index (GI), SDS sedimentation volume (SDSV) and
protein content), were obtained from reports of the Prairie Registration Recommending
Committee for Grain (PRRCG) with the permission of Dr. B. Marchylo. Quality parameters were
described in detail in PRRCG reports (Marchylo et al. 1996, Igg7, Iggg). A ser of samples
contain 105 breeding lines was provided by Dr. J. M. Clarke (Agriculture and Agri-Food
Canada, Swift Current, SK, Canada), along with their GI and SDS sedimentation values.
Samples were collected as seed or semolina and 10 grams of sample (seed or semolina) were
ground in a Udy grinder (Udy Cyclone Grinder, U. D. Co.p., Boulder, CO) with a 1.O-mm screen
for the SIG test.
SIG Test and Protein Fractionation
The swelling index of glutenin in SDS solvent (SIG-SDS) and the swelling index of glutenin in
SDS with lactic acid (SIG) were performed according to the procedure of Wang and Kovacs
(2002a). Wholemeal or ground semolina (a0 mg) was hydrated with 0.6 mL distilled water for
20 min in a 1.5 mL plastic microcentrifuge tube. After the hydration, 0.6 mL SDS-lactic acid stock solution or 1.5 % SDS was added. After 20 min sweiling time in the standard SIG test or different swelling time in the swelling curve test with intermittent vortexing, the suspended samples were centrifuged at 300 x g for 5 min (Micromax model, International Equipment 247
Company, Needham Height, MA). The residues were weighed after removing supem afant, and
the SIG calculated as the weight of the swollen precipitate divided by the original sample weight.
Protein fractions (monomeric protein, soluble glutenin and insoluble glutenin) were determined by turbidity measurement after a sequential extraction procedure (Wang and Kovacs 2002a). All measurements are averages of two determinations.
Statistical Analysis
Statistical analyses were performed using the data analysis tools of Microsoft Excel gT.
RESULTS AND DISCUSSION
Influence of Test Conditions on SIG Test
Results from three cultivars, DT 369 (strong, alveograph W index : 162), Arcola (medium, W:
102), and Cando (weak, W:76) of the method evaluation samples, showed that test conditions influenced the SIG value in a manner similar to that found for common wheat (Wang and
Kovacs 2002a). High mixing intensity (with vortexing), long swelling time or high temperature were required to completely dissolve soluble glutenin and achieve a maximum swelling value for insoluble glutenin. The curve of SIG vs swelling time was divided into three stages namely: swelling, swollen and breakdown (Fig. 1), as was done for common wheat (Wang and Kovacs
2002a).In addition, an increase of hydration time from 0 to 40 min induced an increase of the 248
SIG value (results not shown) which was consistent with the effect of hydration time on the
swelling capacity of glutenin for common wheat (wang and Kovacs2oo2a).
10 20 30 Swell¡ng Time, min
Fig' 1' Effect of swelling time on swelling index of glutenin test for three cultivars. Eror bars indicate standard deviations (n:2).DT 367 (r); Arcora (v); cando (o).
The SIG test was affected by the ratio of solvent to flour, where an increase in the ratio corresponded to an increase in the SIG value (Fig. 2). Unlike common wheat (Wang and Kovacs
2002a), the high swelling capacity in a low solvent ratio was not found in durum wheat, which is difficult to explain. When the ratio was changed from 30 to 40 (mLlg), the SIG value remained relatively constant, allowing the use of convenient sample weights (35-a5mg).
Three types of samples were used in this study: semolina, ground semolina and whole meal. The
SIG value from ground semolina was higher than that fiom semolina probably because the large particle size (semolina parlicle size range: 150 - 500 p) affected the rate of solvent penetration.
Furlhetmore, SIG values from semolina were higher than those from whole meal, because whole 249
4
30 40 Ral¡o of solvent to sample, mU9
Fig' 2. Effect of solvent: flour ratio on swelling index of glutenin test for three cultivars. Error bars indicate standard deviations (n:2).DT 367 (r); Arcola (v); cando (o).
meal contains bran which does not swell effectively in SDS solvent. SIG values from the 98
DIIRC semolina showed strong correlations with SIG values from its whole meal and ground : semolina (r 0.94, r : 0.96, respectively, P < 0.001). This indicated that three types of sample, semolina, ground semolina and wholemeal could be applied in the SIG test. Testing whole meal is more desirable than testing flour or semolina due to ease of preparation.
Two different solvents (aqueous SDS and a mixture of SDS with lactic acid) were tested for their ability to cause swelling of glutenin. The SDS containing lactic acid solvent was identical to the solvent used in the SDS sedimentation test (AACC 2000), while the 1.5 % SDS solvent was the same as that used in a gel protein test (Graveland et at.1979). Glutenin was able to swell well in both solvents, but the swelling capacity of glutenin was higher in the aqueous SDS solvent than in SDS with lactic acid. As was found for the SIG test for common wheat, the swollen glutenin 250
in aqueous SDS solution was too watery to be separated from the supematant, so centrifugation
at 1000 x g instead of 300 x g was used.
The reproducibility of the optimized procedures for SIG and SIG-SDS was determined using
three cultivars (with high, medium and low SIG values) which weïe measured in 24 replicaTes
(Table I). The coefficients of variation (CV o/o) ranged from 1.80 to 2.26 in the SIG and from
1.82 to 2.20 in the SIG-SDS, indicating that the SIG tests in both SDS and SDS-lactic acid
solutions had good reproducibility.
Table I Reproducibility of SIG Tests on Three Diverse Durum Wheat Varieties fi-om Method Evaluation Sarnplesl
SIG SIG-SDS
DT 369 Arcola Cando DT 369 Arcola Cando
Mean 4.18 3.42 2.54 4.60 3.68 2.67 SD2 0.090 0.077 0.046 0.08s 0.067 0.0s9 RSD(%)3 2.ts 2.26 1.80 1.86 1.82 2.20 ,SIG:swe11ingindexofglutenintestinSDS-1acticacidsolution;StG@ glutenin test in SDS solution. The results represent 24 replicates. 2 a, l 1 r Standard deviation. 3 Relative standard deviation.
Relationships befween Small Scale Tests and Gluten Strength
SIG values were significantly correlated with SDS sedimentation volumes (SDSV) in all sets of samples (Table II), and this was in agreement with the result from common wheat (Wang and
Kovacs 2002a). In addition, SIG and SDS sedimentation tests were compared with gluten 251
strength parameters for the five sets of samples. The SDSV was strongly and positively related to
gluten strength in all sets of samples, suppofting previous reports (Dexter et al. 1980, Kovacs
1985, Autran et ctl. 1986, D'Egidio et a\.1990, Cubadda et a\.1992, Kovacs et al. 1997b), which
found that SDSV gave a good prediction of gluten strength. The results showed that the SIG test,
similar to the SDS sedimentation test, strongly correlated to mixograph dough development time
(MDT), gluten index (GI) and alveograph W index. However, the SIG test appears to be superior
to the SDS sedimentation test in relation to MDT. SIG values were better than or equal to SDSV
for prediction of GI and alveograph W in 95 and 98 DURC and method evaluation samples, but
not in 96 DURC samples. The results showed that the SIG test was better than the SDS
sedimentation test for predicting gluten strength for most sets of samples.
Comparison of SIG with SDS Sedimentation Tests
The swelling curves of SIG value vs. swelling time for the i2 method evaluation samples were
similar as swelling time increased (results not shown). As indicated in Fig. 1, the SIG values of
the weak cultivar Cando changed little with an increase in swelling time. The swelling ability of
the strong cultivar (DT 369) increased sharply from 0 to 5 min and then remained relatively
constant. Because the swelling curves among the 12 method evaluation samples were similar, the
correlation coefficient of SIG with SDSV was highly significant (r:0.97, P < 0.001). These results suggested that the glutenin swelling properlies in the method evaluation samples were similar in spite of cultivar diversity in gluten strength and pasta-making quality. Therefore, the glutenin content can be used to account for the variation in quality differences. This is not consistent with the results obtained from common wheat, in which the swelling curves of 252
glutenin from weak and strong varieties were obviously different (Wang and Kovac s 2002a). To
futher investigate the swelling properties of glutenin in durum wheat, 20 samples from the
breeder's lines were selected according to a comparison of the SIG value and SDSV (Fig. 3).
Because SDSV had much higher values compared to SIG values, and it was not possible to
compare SIG values to SDSV directly. In order to compare them, one tenth of SDSV was used.
The most samples had higher SIG value than one tenth of SDSV. In Fig. 3, the SIG values for 16
samples were larger than the one tenth of SDSV, while four samples showed the opposite
relationship. The former samples had swelling curves with a long swollen stage, and showed a
small decrease of SIG value in the breakdown stage. Those with higher values of one tenth of
SDSV showed swelling curves with a sharp drop after short swelling and swollen stages, or gave
a sharp decrease in the SIG value after initial swelling (Fig. a). Because SDSV obtained ffom
gentle mixing corresponded to a short swelling time in the SIG test (Wang and Kovacs2002a),
the sharp decrease of the SIG value after initial swelling is probably the reason that the SIG
value was lower than one tenth of SDSV. Three types of swelling curves reflected different
glutenin swelling properties in these samples. The correlation coefficient of SIG with SDSV in
the selected samples was low, compared with that in the breeding lines (Tabl es 5-2, and 5-3). In
addition, the SiG value gave a higher correlation coefficient with GI than did SDSV (Table Itr).
As swelling time increased, the correlations of SIG with GI increased little, and the highest value
occurred at 5 to 10 min swelling time. A stronger correlation of SDSV with SIG with 0 min
swelling time, compared to that with SIG with 5 min swelling time, occurred in common wheat ( r-value from 0.90*** at 0 min to 0.78s*:F at 5 min)(Wang and Kovacs 2002a),but did not occur in this set of durum samples, even though the coefficient between SDSV and SIG at 0 min was the highest. ¿J3
Table II Correlation Coefficients Between SIG, Sedimentation Tests and Gluten Strength parametersl,2
95 DIIRC 96 DURC 98 DURC MES Breeding Lines ( n:25) ( :25) n ( n: 20) (n: 12) (n: 105) SIG SDSV SIG SDSV SIG SDSV SIG SDSV SIG SDSV MDT 0.51** 0.39* 0.75{:** 0.63** 0.70x** 0.51* 0.76r,* 0.71*+ 0.91>F+ìÈ w 0.6gr,x:* 0.95:k*r< 0.g7*,F,r, 0.g1x** 0.g1,r.,r,r, 0.g0:j<:*x 0.g5r.*{i GI 0.85**t, 0.63** o.Jg*,k;i 0.ggx;r:f. 0.g6,k*:>¡< 0.g5,ß:ß;r. 0.g1**+ 0.gg*:*,¡< 0.g1*+:f 0.70**r, sIG 1.00 0.71,k*r< 1.00 0.g5*** 1.00 0.g5+*>r 1.00 0.g7*r.* 1.00 0.g5*>k,r. MES:methodeva1uationsarnp1es;SIG:S*"llntation y3t13e1tvilT: mixograph dough developing tine; GI: gluten index; W: alveograph W index. 2* **,x* **{< , significant at 5o/o, lo/o and 0.Io/o level, respectively. 254
5
U) (nIJ =4 rl U) Ja
0 1 2 3 4 5 6 I 8 9 1011121314|s1617181920 Varieties
Fig. 3. Diagram of comparing SIG value to i/10 SDS sedimentation volume for 20 samples. SIG (Ä); and 1/10 SDSV (r).
10 20 Swelling Time (min)
Fig. 4. Three kinds of representative swelling curves. A variety with SIG > llrc SDSV e); varieties with SIG < ll10 SDSV (V) and (O). 255
Table III Correlation Coeff,rcients of SIG with Different Swelling Time with SDS Sedimentation, the Percentage of lnsoluble Glutenin Content, the Percentage of Soluble Glutenin Content and Gluten lndex for the 20 Selected Samples from Breeding Linesl,2
Swelling Time (min)3 SDSV IG/F
0 0.71*** 0.gg*+* 0.41 0.86+*.'k
5 0.69>F** 0.93 'r** 0.37 0.89*>k*
10 0.66+* 0.93>1.** 0.39 0.gg**>F
20 0.67** 0.94*** 0.36 0.87***
30 0.69>F*,É 0.95*>k>r 0.37 0.85>1.>t *
40 0.69{
SDSV 1.00 0.58** 0.20 0.55* 'SDSV:SDSsedimentationvo1ume;IG/F:thepercentageofin : the percentage of soluble glutenin; GI: gluten index. *, **, *** ant 5o/o, 1 signific at lyo and 0. Io/o level,respectively. ' Vortexing for 5 sec at beginning and end of swelling period, and vortexing for 5 sec at intervals of 10 min.
To explain the difference between the SIG and SDS sedimentation tests, protein fractions were determined for the method evaluation samples and selected samples. Similar to the correlation between SIG and SDS sedimentation tests, SDSV results frorn method eval¡ation samples were strongly related to the percentage of insoluble glutenin content in flour (IG/FX r : 0.95, p <
0.001). This indicated that SDSV results can be explained by insoluble glutenin content, because varieties possessed the same glutenin swelling properties, and glutenin quality had a similar effect on glutenin swelling capacity. On the other hand, the SDS sedimentation test showed a weaker association with IG/F in the selected samples (Table Itr). As discussed above, the selected samples contained varieties having different glutenin swelling properties which influenced SDSV. As the swelling time increased, the correlation coefficients of SIG with iG/F increased (Table III), suggesting that the SIG test, which facilitates strong swelling, yields results 256
based on the contribution of insoluble glutenin. Unlike the results from common wheat (Wang
and Kovacs 2002a), the percentage of soluble glutenin in flour (SG/F) had no significant
correlation with SIG at different swelling times, but the highest coefficient occurred at 0 min swelling time.
In breeding line samples, most SIG values were larger than the one tenth of SDSV, however, with 72 samples the reverse was true. These 12 unusual samples were compared with 12 common samples which were randomly chosen from breeding line samples after removing unusual samples (Table tV). The SIG value was strongly related to SDSV in these 24 samples, but the SIG value gave a higher corelation coefficient with GI than did SDSV. In the common samples, the SIG had a very strong correlation with SDSV, similar to the result from the method evaluation samples. Here, both SIG and SDSV were good predictors for gluten strength(Gl), indicating the common samples had similar glutenin swelling properties. ln the unusual samples, a weak correlation between SIG and SDSV results was observed, while the correlation coefficient of GI with SIG was significant, thal of GI with SDSV was not. Because the unusual samples exhibited unchalacteristic glutenin swelling characteristics, it was presumed that glutenin swelling properties had a larger effect on SDSV results, making SDSV a poor predictor of gluten strength as measured by GI. The stronger swelling conditions permitted in the SIG test
(due to elimination of the effect of soluble glutenin), may explain why SIG was still significantly related to GI in the unusual samples. Coefnicients of IG/F with SDSV and SIG strongly support the idea that insoluble glutenin is the main contributor of GI and the difference between SDSV and SIG is due to SiG being strongly related to IG/F (Table IV). ln addition, the other two 257
Table IV Relationships among SDS Sedimentation, SIG and_Gluten Index for the Common and Unusual Sarnples fiom Breedi'g Lrnes"'
Total Attributes Samples Samples (SIG > 1/10 SDSV) Samples (SIG < 1/10 SDSV)) n:24 n=12 n=12 SDSV SIG MP/F SG/F IG/F SDSV SIG MP/F SG/F IG/F SDSV SIG MP/F SG/F IG/F SDSV 1.00 0.75*** -0.37 0.16 0.64** i.00 0.92'F** -0.57* 0.32 0.g7*ì<* i.00 0.64* -0.3g -0.11 0.45 sIG 0-75*** 1.00 -0.44* 0.42* 0.94:1"<* 0.92+** 1.00 -0.51 0.2g 0.96r<*ì< 0.64*. 1.00 -0.36 0.61* 0.91{:*>r: GI 0.58** 0.78x** -0.45* 0.31 0.74+** 0.87*>Ft' 0.80+* -0.63x 0.12 0.69* 0.2g 0.7g** -0.10 0.70* 0.87*** SIG : swelling index of glutenin;lutenintestirrSDS-lacticacidsolution;SDSV:SDSie tes thete percentage of monomeric protein in flour; SGÆ : the percentage sohrble t¿ of glutenin in flour; IG/F : the pe-rce¡tage of i'soluble gluterunlulenlnin lnin ïlour.flour.Tlotìr. 2 *, **, *** signifi cant at 5o/o, Iyo and, O.Io/o level, lespectively. 2s8
protein fractions appear to have different effects on the swelling properties and gluten strength in
the two sets of samples. The percentage of monomeric protein in flour (MP/F) was negatively
cor¡elated to SDSV and GI in the common samples, while the percentage of soluble glutenin
(SG/F) had no significant relation to SDSV and GL However, there were significant correlations
of SGÆ with SIG and GI in the unusual samples, while the MP/F had no significant corelation
with them. This indicates that soluble glutenin influences the swelling properties in the unusual
samples, inducing the differences of glutenin swelling properties.
Our results have shown that SDSV was a satisfactory predictor for gluten strength, when samples
have similar glutenin swelling properties. With samples that have different glutenin swelling
properties, the SIG is better.
Relationships of Protein Fractions and Small Scale Tests with Durum Quality
To investigate the contribution of protein fractions to gluten properties and pasta-making quality,
protein fractions from method evaluation samples were analyzed, andthe correlation coefficients
between protein fractions and quality parameters were calculated (Table V). Monomeric protein
content, expressed both as the percentage of monomeric protein content in flour (Mp/F) and in protein (MP/P), was negatively correlated with gluten strength parameters ( mixograph dough
developing time (MDT), extensograph maximum resistan"r (Rro* ) and GI, pasta disc viscoelasticity (PDV), and cooked gluten viscoelasticity (CGVS)). It is not possible to tell whether monomeric protein directly weakens gluten strength or if the result is due only to the 2s9
highly negative correlation with insoluble glutenin content (r: -0.96, P < 0.001). The alveograph
G value, which is used as a parameter lor dough extensibility, positively correlated with
monomeric protein content (MP/F). The soluble glutenin had no significant correlation with
gluten strength but did correlate with mixograph total energy (TEG). This is not in agreement
with a previous report (Dexter and Matsuo 1980), where, using a modified Osborne procedure, a
Table V Correlation Coefficients among Protein Fractions, Small-scale Tests and Dough euality Parameters for the l2 Method Evaluation Samplesl,2
Protein MP/F sG/F IG/F Mp/p sc/p tc/p SDSV sDsv/p stc stc/p TEG 0.41 -0.34 0.69* 0.63* -0.50 0.56 0.47 0.60 0.48 0.581 0.41 _0.43 _0.79x* _0.35 MDl- 0.69* -0.76** _0.16 0.gl ** 0.71*+ 0..:6** 0.76*+ 0.g3*e+
w -0.35 -0.88**t -0.09 0.93*** _0.gg*+* 0.09 o.g2** 0.g5+** 0.g7*** 0.g0+** 0.g4**+
c -0.13 0.63* -0.33 -0.77*+ 0.74** -0.30 -0.71*+ -0.71** -0.72+* -0.774+ _0.6g* _0.25 _0.99*x* Rmax 0.06 0.94*+* _0.g3x** 0.20 O.ggx** 0.g6*** 0.g7*x* 0.g7*** 0.g7x**
EXT 0.20 -0.30 0.67+ 0.40 -0.39 0.63* 0.32 0.45 0.39 0.38 0.29 _0.42 _0.99*** _0.0g _0,gg*++ GI 0.g4*** 0.14 0.g5+*x 0.gg*++ 0.g3*** 0.glx** 0.g7*++ _0.32 _0.92x*x _0.03 CGVS 0.g3*+* _0.g5*+* 0.14 0.gg*** 0.g5*** O.g7**+ 0.g6*** 0.gg**+
PDV 0.02 -0.77** 0.33 0.94**r -0.g7*x+ 0.36 0.g0+*+ 0.gl *** 0.g6*** 0.g0+** 0.g2x* ,Protein:proteincontentofsemolina;MPÆ:the : MP/P the percentage of monomeric protein in protein; SG/F : the percentage of soluble glutenin in flour; SGÆ : the percentage of soluble glutenin in protein; IGTF : the percentage of insoluble glutenin in flour; IG/P : the percentage of insoluble glutenin in total flour protein; SDSV : SDS sedimentation volume; SDSV/P : SDS sedimentation volume divided by protein content; SIG : swelling index of glutenin; : : SIG/P SIG value divided by protein conten! MDT mixograph dough development time; TEG: mixograph total energyl G: alveograph G index; W : alveograph W index; R',u* : extensograph maximum resistance; EXT : eitensograph extensibility; GI : gluten index; CGVS : cooked gluten viscoelasticity; pAV : pasta disc viscoelasticity. 2 *,**,8*8 signific ant at syo,Io/o and 0.1 %olevel,respectively. 260
negative correlation was found between soluble glutenin (extracted with acetic acid solution after
sequential extractions of salt and ethanol solutions) and gluten strength. The contrasting results
may be due to the different protein fractionation procedures and different sets of samples. In
addition, the extensograph extensibility was only significantly corelated with soluble glutenin
content (SG/F), while monomeric protein and insoluble glutenin did not appear to contribute to
parameter. this Monomeric protein and soluble glutenin are both believed to contribute to dough viscosity (Orth and Bushuk 1972, Huebner and Wall 1g76). The results shown here suggest that
the roles of the two fractions are not identical.
It has been demonstrated that insoluble glutenin present in bread wheat flour is largely responsible for bread wheat mixing properties and baking quality (Pomeranz 1965, Orth and
Bushuk 1972, Orth and O'Brien 7976, Gupta et al. 1993, Preston et al. 1992. Bean et al. I99g,
Sapirstein and Fu 1998). Results from the current study also revealed that insoluble gl,tenin was the fraction mostly responsible for determining durum wheat functional properties (Table V).
Insoluble glutenin was the main contributor to durum gluten strength, because strong positive
coefficients were found between insoluble glutenin content and strength parameters (MDT, GI,
W and maximum resistance in the extensograph), which supported previous research (Wasik 1978' Dexter and Matsuo 1980, Matsuo e/ al. 1982, Sgrulletta and De Stefanis 19g9). The absolute content of insoluble glutenin (IG/F) was best correlated with TEG and pDV, while the relative content of insoluble glutenin (IG/P) correlated best with most strength parameters. This indicated that the IGÆ is the best predictor of durum gluten strength. Similariy, the SIG value and SDSV could be divided by their protein content into SIG/P and SDSV/p parameters.
Compared with SIG and SDSV, SIG/P and SDSV/P would be the best parameters for evaluating 26r
durum gluten strength. The same conclusion was obtained from the analysis of other sets of
samples (Data not shown).
CONCLUSIONS
Gluten strength is a fundarnental quality of durum wheat, and it is particularly important when the grain is to be used in pasta-making. The results of this study indicate that the SiG test is a reliable method for evaluating gluten strength either in whole meal or in semolina. The SIG test satisfies nearly all the requirements of breeders. It is rapid, simple, and only a small sample (35-
45 mg) of whole meal or semolina is needed for analysis.
References Cited
As in Chapter 10