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CROP BREEDING, GENETICS & CYTOLOGY

Solvent Retention Capacities of Irrigated Soft White Spring Wheat Flours

^a Univ. of Idaho Research and Exten. Ctr., P.O. Box AA, Aberdeen, ID 83210
^b Univ. of Idaho Research and Exten. Ctr
^c Nabisco Toledo Flour Mill, P.O. Box 2208, Toledo, OH 43603

* Corresponding author (esouza{at}uidaho.edu)

	ABSTRACT

TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 
The solvent retention capacity (SRC) test uses the ability of flour to retain a range of solvents as a means of evaluating multiple aspects of wheat (Triticum aestivum L.) quality: pentosan content, starch damage, gluten strength, and general water retention. To assess the utility of the SRC in cultivar evaluation, 26 soft white spring wheat genotypes were produced in seven irrigated environments, and milling and baking quality parameters for these genotypes were determined. Solvent (water, 500 g kg^-1 sucrose, 50 g kg^-1 sodium carbonate, and 50 g kg^-1 lactic acid) retention capacities of flours effectively differentiated genotypes across environments. Flour protein concentration and sucrose SRC together effectively modeled sugar snap cookie diameter. Flour extraction and sodium carbonate SRC were negatively correlated. Whole grain measurements, including near-infrared hardness, single kernel hardness, and sodium dodecyl sulfate (SDS) sedimentation volume were correlated with SRC values. The SRC test is a promising method for evaluating soft wheat genotypes on the basis of their underlying biochemical flour characteristics, independent of flour protein concentration.

Abbreviations: AACC, American Association of Cereal Chemists • AWRC, alkaline water retention capacity • HMW-Glu, high molecular weight glutenin • L, alveograph extensibility • NIR, near-infrared • P, alveograph tenacity • SDS, sodium dodecyl sulfate • SKCS, single kernel characterization system • SRC, solvent retention capacity • SWS, soft white spring • W, alveograph deformation work

	INTRODUCTION

TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 
SLADE AND LEVINE developed a new American Association of Cereal Chemists (AACC) method (56-11), the solvent retention capacity (SRC) test, to evaluate soft wheat quality (Gaines, 2000; Slade and Levine, 1994). The ability of flour to retain a set of solvents relates flour functionality to specific flour components. The solvent retention capacity is the weight of solvent held by flour after centrifugation, expressed as percent of flour weight (140 g kg^-1 moisture basis). Retention of four solvents (water; 500 g kg^-1 sucrose; 50 g kg^-1 sodium carbonate; and 50 g kg^-1 lactic acid) produces a practical flour quality functionality profile for predicting commercial baking performance. Generally, lactic acid SRC is associated with glutenin characteristics, sodium carbonate SRC is associated with levels of damaged starch, sucrose SRC is associated with pentosan and gliadin characteristics, and water SRC is influenced by all of those flour constituents (Gaines, 2000). In contrast to the alkaline water retention capacity method (AWRC; AACC 56-10), the 50 g kg^-1 sodium carbonate in the SRC method elevates the pH >11, above the pK_a of starch hydroxyl groups. The sodium carbonate SRC therefore diagnostically distinguishes damaged or pregelatinized starches from raw, undamaged, native starch (Gaines, 2000).

Mechanical mixing characteristics, extensibility, and elasticity of dough are determined primarily by gluten. Gluten strength requirements vary with products manufactured from soft wheat. For example, crackers and flatbreads require stronger gluten than cookies. Gluten strength is a function of both protein concentration and protein composition. For example, Abboud et al. (1985) examined 44 flours representing a wide range of quality and observed that, among soft wheat flours, correlation coefficients of protein concentration with sugar snap cookie diameter were not significant. Instead, cookie diameter depended on damaged starch and water absorption. Within a cultivar and production environment, however, flour protein concentration was negatively correlated with sugar snap cookie diameter. Bettge et al. (1989) found that alveograph deformation work (W), a measure of gluten strength, was negatively correlated with sugar snap cookie diameter. Effects of the three high molecular weight (HMW) glutenin alleles (Glu-A1, Glu-B1, Glu-D1) on sugar snap cookie quality of soft white spring (SWS) wheat flours were evaluated by Souza et al. (1994). With the exception of Glu-B1g (13+19), individual alleles were not important determinants for sugar snap cookie quality. However, when alleles were summed in a glutenin rank score (Payne et al., 1987), a joint index of gluten strength, cookie diameter was negatively correlated with gluten rank score (Souza et al., 1994).

Good cookie and cracker flours hold water poorly (Faridi et al., 1994). The gross hydrophilic components of a sugar snap cookie formula are flour and sugar. If the flour is less hydrophilic, then more water is available to the sugar to form syrup, decreasing dough viscosity during baking. The resulting dough spreads farther, producing larger diameter cookies (Slade and Levine, 1994). Moreover, flours with excessive water retention require increased baking times in cookie and cracker manufacturing operations, which results in a less tender product and increased energy costs in bakeries.

Damaged starch absorbs much more water than does undamaged starch. Gaines et al. (1988) demonstrated that increasing damaged starch increases sugar snap cookie dough stiffness and decreases cookie diameter. Substituting damaged starch for flour in a sugar snap cookie formulation or for native prime starch in an all-starch cookie model system decreased cookie diameter and increased AWRC (Donelson and Gaines, 1998).

Pentosans are highly hydrophilic structural carbohydrates that absorb 10 times their weight in water (Kulp, 1968; Jelaca and Hlynka, 1971). Kaldy et al. (1991) surveyed 20 soft white spring wheats and 5 soft white winter wheats from a wide range of production environments. Water-soluble and total pentosan contents correlated with smaller cookie diameter and cake volume across production environments.

Manufacturing quality of soft wheat is assessed directly by preparing test products, such as standardized cookies, crackers, sponge cakes, or Udon noodles (Hoseney et al., 1988). The AACC (American Association of Cereal Chemists, 1995) sugar snap cookie test (AACC 10-52) is used widely to select soft wheat cultivars for confectionary products. This test's popularity derives from its reproducibility and efficiency. Although the sugar-snap cookie test can effectively distinguish good flours from bad flours, it provides no information on the specific biomolecular basis of performance.

The SRC test is relatively new with limited literature describing its application. This study evaluated the SRC test as a tool for identifying soft wheat genotypes with improved manufacturing quality.

	MATERIALS AND METHODS

TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 
Genotypes and Field Trials
Twenty-six SWS genotypes were grown in irrigated field trials at Aberdeen, Kimberly, and Tetonia, ID, in 1998 and at Aberdeen, Hazelton, Blackfoot, and Tetonia, ID, in 1999. The Kimberly and Hazelton locations were proximal to each other in southcentral Idaho and are henceforth termed the Magic Valley location. The genotypes included named cultivars and advanced breeding lines (Table 1). All genotypes were well adapted to the Pacific Northwest and irrigated production. The genotypes were included with other breeding lines in 100-entry, three-replication yield trials arranged in 10-by-10 lattice designs. The experimental unit was a 4-m² plot of a single genotype. Trials were planted on the dates shown in Table 2 at a seeding rate of 3 million seeds per hectare. Fertilizers were applied before planting on the basis of soil tests, a target yield of 8000 kg ha^-1, and University of Idaho recommendations. Trials were irrigated with overhead sprinklers to replace the estimated loss of soil moisture due to transpiration of the crop (estimates obtained from the United States Bureau of Reclamation Pacific Northwest Cooperative Agricultural Weather Network). Field management and timing of management practices generally matched local commercial production practices. Grain quality samples were collected from each replication of the 1998 and 1999 Aberdeen trials. Grain quality samples from each other location were prepared from a composite of two or more replications.

High molecular weight glutenin alleles were determined as described by Souza et al. (1994) by comparison to soft white wheat cultivars with previously described Glu-1 profiles. Starch pasting viscosity was determined on a Foss Super 3 Rapid Viscoanalyzer (Newport Scientific Pty. Ltd., Warriewood NSW, Australia). Rotor speed was 960 rpm for 10 s then was held at 160 rpm for the remainder of each analysis. Temperature was held at 60°C for the first two min, then increased to 93.5°C over 6 min, held at 93.5°C for 4 min, decreased to 50°C over 4 min, and held at 50°C for 4 min.

Alveographs were obtained from flour samples from grain produced in 1999 at Aberdeen and in the Magic Valley on a Chopin model MA 87 alveograph (Chopin, Villeneuve-la-Garenne, France) with the RCV4 calculator according to AACC method 54-30A. Circulating water baths maintained correct instrument temperature. Laboratory conditions for operation were 35 to 40% RH with ambient temperature of 72°F. Five dough pieces were prepared from each sample and allowed to rest 20 min at 25°C before insertion into the alveograph. A bubble was blown, and the air pressure profile was recorded with the RCV4 recording calculator. Dough tenacity (P) was the maximum pressure reached in blowing the dough bubble. Dough extensibility (L) was the length of the alveograph curve, measured as the average abscissa at bubble rupture. Deformation work (W) is related to the area under the curve. W at L = 100 is the area under the curve at L = 100 mm.

SRC Evaluations
Duplicate solvent retention capacity evaluations were conducted on each flour sample as described in AACC method 56-11 with the following modifications. Water, 50 g kg^-1 lactic acid, and 50 g kg^-1 sodium carbonate were dispensed with automated repeating dispensers (VWR Labmax Bottle-Top Dispenser, Cat. No. 40000-066, VWR Scientific, Salt Lake City, UT). After initial suspension of flour in solvents with manual shaking, samples were placed horizontally on an orbital shaker operating at approximately 100 rpm for 25 min. Suspended flour samples then were centrifuged in a programmable Jouan Model CR422 centrifuge (Jouan, Inc., Winchester, VA) with a M4 swing-out rotor with adapters (Jouan 11174191) for 50-mL conical tubes using the following program parameters: 910 x g, 17 min.; 20°C; radius = 176 mm; acceleration profile = 5; brake profile = 4 mm.

Statistical Analyses
Mean results from the three replications of the Aberdeen trials were used in statistical analyses. Location, genotype, and genotype x location interaction effects were evaluated by a mixed effects analysis of variance (Steele and Torrie, 1980, p. 218–221). The calculation was conducted by PROC MIXED in SAS (vers. 6.12; SAS Institute, Inc., 1997) with location (Aberdeen, Magic Valley, Tetonia) and genotype as fixed effects and year (1998 and 1999) and location (year) as random effects. The Blackfoot trial was produced only in 1999 and was excluded from this analysis to create a balanced dataset for genotype x environment analysis. Genotypic least square means for break, reduction, and total flour yield, flour protein concentration, sugar snap cookie diameter, top grain score, and water, sodium carbonate, sucrose, and lactic acid solvent retention capacity were calculated by the PROC MIXED software.

Pearson's linear correlation coefficients among quality parameters were calculated by genotype means (PROC CORR; SAS Inst., 1997). Multiple regression was conducted with cookie diameter as the dependent variable. Independent variables were selected on the basis of their ability to optimize the R² value of the model. Regression models were selected using the Mallows C(P) statistic, identifying the models for which C(P) approximated (p + 1), where p is the number of independent variables in the model. The optimum regression model was identified as the model having P with the greatest difference in C(P) between the optimum and second optimum subset of independent variables (SELECTION = RSQUARE in PROC REG; SAS Institute, Inc., 1991). The dataset used for this regression analysis was the mean for each genotype across all seven environments (N = 26).

Cluster analysis was conducted on the basis of Euclidean distances using nearest centroid sorting (Anderberg, 1973; PROC FASTCLUS; SAS Inst., 1997) with maximum cluster number arbitrarily set to five. Data for the analysis were mean water, sodium carbonate, sucrose, and lactic acid SRC values across all seven environments, standardized to mean = 0, and standard deviation = 1. Analysis of variance was conducted with cluster as a categorical variable to derive mean non-SRC quality parameters for each cluster and to test levels of significance.

	RESULTS AND DISCUSSION

TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 
Location and Genotype Evaluation
Locations were unimportant sources of variation for milling and baking quality parameters and solvent retention capacities of the 26 genotypes grown at Aberdeen, the Magic Valley, and Tetonia in 1998 and 1999 (Table 3). Because of differences in elevation and spring planting time, these three locations often have significant differences in most agronomic traits. However, all three locations were irrigated to minimize moisture stress during grain-filling period with fertilizer levels adjusted to accommodate differences in residual soil fertility. These intentional modifications of the environment, which match local production practices, may have minimized the effect of location on endosperm development and subsequent flour functionality. Genotypic effects all were highly significant, while location x genotype interactions, with the exception of water SRC, were nonsignificant. Therefore, genotype means across years and locations (Table 4) were used for subsequent correlation analyses.

Association of Quality Parameters
Regression of cookie diameter from each experimental unit on flour protein concentration (N = 182) was highly significant and follows:

(Model 1)

However, this regression model explained less than half of the total variation in cookie diameter. The smallest cookies (8 cm in diameter and smaller) were produced by flours ranging in protein from 106 to 120 g kg^-1. The largest cookies (9 cm in diameter and larger) in the N = 182 dataset were produced by flours ranging in protein from 850 to 980 g kg^-1. The partial R² of sodium carbonate and sucrose SRC, after fitting flour protein, was 25%, indicating that these solvent retention capacities predicted additional variation in cookie diameter.

The optimum multiple regression model for cookie diameter of each genotype over all environments (N = 26) included flour protein concentration and sucrose SRC:

(Model 2)

Regression models for cookie diameter on flour protein concentration and sucrose SRC were statistically significant in each of the seven environments (P < 0.05 to P < 0.001; data not shown). These results suggest that large cookie diameter genotypes can be selected based upon flour protein concentration and sucrose SRC. Sucrose SRC generally predicts pentosan content (Gaines, 2000), thus Model 2 describes cookie diameter as a function of protein and pentosan concentrations. Bettge and Morris (2000) similarly observed that sugar snap cookie diameter of flour samples from eleven soft wheat cultivars was modeled effectively by flour protein and total pentosan content.

Solvent retention capacities were independent of flour protein concentration (Table 5), and water SRC was independent of sodium carbonate, sucrose, or lactic acid SRC. Sucrose, sodium carbonate, and lactic acid SRC were positively correlated. Flour extraction was strongly negatively correlated with sodium carbonate SRC (Table 5), and to lesser extent with sucrose SRC. Genotypes that milled with high extraction had less starch damage and lower pentosan content than poor milling genotypes. Consistent with the regression models, sugar snap cookie diameter and top grain score were correlated negatively with flour protein concentration and sodium carbonate, sucrose, and lactic acid SRC.

Lactic acid SRC correlations with alveograph parameters reflect the common causality of gluten proteins on these measures. The results of this study are consistent with the reported relationship between alveograph parameters and Glu-1 alleles. In near-isogenic lines, the Glu-D1d (5+10) allele had a strong positive influence on dough tenacity (P) and dough extensibility relative to the Glu-D1a (2+12) allele (Redaelli et al., 1997). In our study, genotypes with the Glu-D1d allele generally had greater dough tenacity (P = 38 mm) than genotypes with the Glu-D1a allele (P = 32 mm; difference significant at P < 0.02). Genotypes with the Glu-D1d allele generally also had greater W (129 x 10^-4J) than genotypes with the Glu-D1d allele (94 x 10^-4J; difference significant at P < 0.01). However, Glu-D1 genotype was not associated with dough extensibility. Sucrose SRC correlation with tenacity and deformation work may be a consequence of the partial association of sucrose SRC with gliadins. Gliadin alleles also affect dough strength, as measured by alveograph W (Metakovsky et al., 1997). Epistatic effects between Glu and Gli loci on tenacity, extensibility, and deformation work also have been reported (Nieto-Taladriz et al., 1994). The underlying basis of the water correlations with alveograph parameters is not readily explained and requires further investigation.

Correlations with Whole Grain Quality Measures
Although flour requirements for the SRC test are relatively small (80 g for two determinations in each of four solvents), the test nonetheless requires a sufficient quantity of grain for milling (150 g). The grain quantity and labor requirements for milling restrict application of the SRC test to materials entering grain yield testing. Our laboratory uses whole-grain microtests for selection in early generations. Therefore, we evaluated the correlation of NIR and SKCS hardness and SDS sedimentation volume with SRC values.

Correlations among NIR hardness, SDS sedimentation volume, solvent retention capacities, flour protein concentration, and cookie diameter were determined by genotype means over five environments (N = 26). Within this set of genotypes, softer wheats had greater pentosan and damaged starch content based on the negative correlations of NIR and SKCS hardness with sucrose SRC and sodium carbonate SRC (Table 6). These solvent retention capacities, particularly sucrose SRC, were negatively correlated with sugar snap cookie diameter. Therefore, in this data set, NIR hardness was positively correlated with sugar snap cookie diameter. This is unexpected as harder kernels should produce flours with greater starch damage. The mean sucrose SRC of the seven cultivars with the lowest NIR hardness was 959 g kg^-1, and the mean sucrose SRC for the seven cultivars with the highest NIR hardness was 886 g kg^-1. Excluding from the dataset two genotypes with low NIR hardness and high sucrose SRC, Penawawa and IDO524 (a Penawawa derivative), did not influence these conclusions (data not shown). Bettge and Morris (2000) also observed a negative correlation of grain hardness (NIR and SKCS) with membrane-bound pentosan content among 11 soft wheat cultivars. They suggested that this inverse relationship may be due to differences in amyloplast membranes, such as the composition of membrane lipids.

Clustering Genotypes by SRC Profiles
The objective of the SRC test is to develop multivariate profiles based on macromolecular flour characteristics. Therefore, we applied cluster analyses to assess the ability of the SRC profiles to classify genotypes meaningfully, arbitrarily setting the cluster number to five (Table 7). Analysis of variance of non-SRC characters indicated that the clusters differed for all measured parameters except flour protein concentration and break flour extraction.

Flours of cultivars in Cluster 1 (Alpowa and Penawawa) had the highest tenacity (43 mm) of any cluster (P > 0.01) and relatively low extensibility (118 mm), providing the highest P/L ratio (0.38) of the five clusters (P < 0.05). Flours of cultivars in Cluster 1 also had the highest mean alveograph W value (P < 0.01). Flours of genotypes in Cluster 4 represented the opposite extreme. These flours had the lowest tenacity (25 mm) of any cluster (P < 0.01) and relatively high extensibility (130 mm), providing a low P/L ratio (0.19). Genotypes in Cluster 4 had the lowest W (59 x 10^-4J) of any cluster (P < 0.01). Glu-1 alleles inadequately define the alveograph characteristics of these clusters. For example, although both members of Cluster 1 are Glu-B1c, Glu-D1d, two other Glu-B1c, Glu-D1d genotypes (IDO524 and IDO543) are found in Cluster 3. Similarly, although the weak gluten genotypes in Cluster 4 are Glu-B1f, Glu-D1a (IDO553 is heterogeneous for Glu-B1), ‘Centennial’, another Glu-B1f, Glu-D1d genotype, produced much greater W values and was found in Cluster 3.

	CONCLUSIONS

TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 
The results of this study demonstrate that even among improved, adapted soft wheat genotypes, differences in quality characteristics can be detected by solvent retention capacity. These differences relate to end-use functionality as measured by flour extraction, sugar-snap cookie bake tests, and alveograph tests. The SRC test may be of particular value in selection because it reflects the underlying macromolecular basis of quality independent of flour protein concentration. In particular, the SRC test addresses the relative contributions to water absorption of gluten (estimated by the lactic acid SRC, the SDS-sedimentation test, and Alveograph W) and the carbohydrate polymers, damaged starch, and pentosans.

SRC classification of wheat genotypes may rescue genotypes with manufacturing utility from being discarded during the evaluation stages of a breeding program. For example, the use of the sugar snap cookie alone for selection, might have resulted in Cluster 3 genotypes being discarded in favor of Cluster 4 and 5 genotypes. Yet, the combination of gluten strength low damage starch levels of the Cluster 3 genotypes, in the context of their superior milling quality, may have significant manufacturing advantages.

Our results pose additional research questions because there are inconsistencies with historical understanding of wheat quality. The water solvent, a proxy for the standard AWRC test (Gaines, 2000), was not correlated with the solvents (sodium carbonate and sucrose) that measure known determinants of end-use quality, damaged starch, and pentosans. However, water SRC was correlated with alveograph tenacity, a useful predictor of soft wheat quality. Generally, lower AWRC and smaller tenacity values are predictive of better pastry quality (Faridi et al., 1994). Yet, superior confectionary quality genotypes (Cluster 5 genotypes) do not have the lowest water SRC values. Therefore, it is unclear what emphasis should be placed on the water SRC, and by analogy the AWRC, in breeding programs following initial selection against segregants that are either hard kerneled or have very high AWRC. The relationships among water SRC, the other solvents, and cookie baking performance may be stronger in unselected populations, particularly populations with hard wheat parentage. Yet, even following initial selection for quality, the SRC analysis separates genotypes into different quality groups upon which subsequent breeding decisions can be based.

	ACKNOWLEDGMENTS

 
The authors gratefully acknowledge the expert technical advice of Harry Levine and Louise Slade. This research was supported generously by Nabisco, Inc., the Idaho Wheat Commission, and the Idaho Agricultural Experiment Station.

	NOTES

TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 
Univ. of Idaho Agric. Exp. Stn. Paper no. 00726.

Received for publication August 24, 2000.

	REFERENCES

TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 

• Abboud, A.M., R.C. Hoseney, and G.L. Rubenthaler. 1985. Factors affecting cookie flour quality. Cereal Chem. 62:130–133.
• American Association of Cereal Chemists. 1995. Approved methods of the AACC, 9th ed. AACC, St. Paul, MN.
• Anderberg, M.R. 1973. Cluster analysis for applications. Academic Press, Inc., New York.
• Bettge, A.D., and C.F. Morris. 2000. Relationships among grain hardness, pentosan fractions, and end-use quality of wheat. Cereal Chem. 77:241–247.
• Bettge, A., G.L. Rubenthaler, and Y. Pomeranz. 1989. Alveograph algorithms to predict functional properties of wheat in bread and cookie baking. Cereal Chem. 66:81–86.
• Dick, J.W. and J.S. Quick. 1983. A modified screening test for rapid estimation of gluten strength in early generation durum wheat breeding lines. Cereal Chem. 60:315–318.
• Donelson, J.R., and C.S. Gaines. 1998. Starch-water relationships in the sugar-snap cookie dough system. Cereal Chem. 75:660–664.
• Faridi, H., C. Gaines, and P. Finney. 1994. Soft wheat quality in production of cookies and crackers. p. 154–168. In W. Bushuk and V.F. Rasper (ed.) Wheat: Production, properties, and quality. Chapman & Hall, Glasgow, Scotland.
• Gaines, C.S. 2000. Report of the AACC committee on soft wheat flour. Method 56-11, Solvent Retention Capacity Profile. Cereal Foods World 45:303–306.[ISI]
• Gaines C.S., J.R. Donelson, and P.L. Finney. 1988. Effects of damaged starch, chlorine gas, flour particle size, and dough holding time and temperature on cookie dough handling properties and cookie size. Cereal Chem. 65:384–389.
• Hoseney, R.C., P. Wade, and J.W. Finley. 1988. Soft wheat products. p. 407–456. In Y. Pomeranz (ed.) Wheat: Chemistry and technology. Vol. 1, 3rd ed. Am. Assoc. Cereal Chem., St. Paul, MN.
• Jelaca, S.L., and I. Hlynka. 1971. Water-binding capacity of wheat flour crude pentosans and their relation to mixing characteristics of dough. Cereal Chem. 48:211.
• Kaldy, M.S., G.I. Rubenthaler, G.R. Kereliuk, M.A. Berhow, C.E. Vandercook. 1991. Relationships of selected flour constituents to baking quality in soft white wheat. Cereal Chem. 68:508–512.
• Kulp, K. 1968. Pentosans of wheat endosperm. Cereal Sci. Today 13:414–417.
• Metakovsky, E.V., P. Annicchiarico, G. Boggini, and N.E. Pogna. 1997. Relationship between gliadin alleles and dough strength in Italian bread wheat cultivars. J. Cereal Sci. 25:229–236.
• Nieto-Taladriz, M.T., M.R. Perretant, and M. Rousset. 1994. Effect of gliadins and HMW and LMW subunits of glutenin on dough properties in the F6 recombinant inbred lines from a bread wheat cross. Theor. Appl. Genet. 88:81–88.
• Payne, P.I., M.A. Nightingale, A.F. Krattiger, and L.M. Holt. 1987. The relationship between HMW-glutenin subunit composition and the bread-making quality of British-grown wheat varieties. J. Sci. Food Agric. 40:51–65.
• Radaelli, R., N.E. Pogna, and P.K.W. Ng. 1997. Effects of prolamins encoded by chromosomes 1B and 1D on the rheological properties of dough in near-isogenic lines of bread wheat. Cereal Chem. 74:102–107.
• SAS Institute, Inc. 1991. Variable selection. p. 107–118. In R.J. Freund and R.C. Littell (ed.) SAS system for regression, Second Edition. SAS Institute, Inc., Cary, NC.
• SAS Institute, Inc. 1997. The SAS system for Windows, Release 6.12. SAS Institute, Inc., Cary, NC.
• Slade, L., and H. Levine. 1994. Structure-function relationships of cookie and cracker ingredients. p. 23–141. In H. Farid (ed.) The science of cookie and cracker production. Chapman & Hall/AVI, New York.
• Souza, E., M. Kruk, and D.W. Sunderman. 1994. Association of sugar snap cookie quality with high molecular weight glutenin alleles in soft white spring wheats. Cereal Chem. 71:601–605.
• Steele, R.G.D., and J.G. Torrie. 1980. Principles and procedures of statistics: A biometrical approach. 2nd ed. McGraw-Hill, Inc., New York.

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