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CROP BREEDING, GENETICS & CYTOLOGY

Sources of Variation in the Solvent Retention Capacity Test of Wheat Flour

Univ. of Idaho Res. and Ext. Ctr., P.O. Box 870, Aberdeen, ID 83210

^* 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 economically important aspects of wheat (Triticum aestivum L.) quality: pentosan content, starch damage, gluten strength, and general water retention. Inbred lines from three soft spring wheat populations (‘Vanna’/‘Penawawa’, ‘Kanto 107’/IDO488, and M2/IDO470) were produced in replicated, irrigated trials in 2000 and 2001 to assess variance components for the SRC tests. Milling yield, flour ash, flour protein, and sugar snap cookie diameter for these genotypes were determined. Flour solvent (water, 500 g kg^-1 sucrose, 50 g kg^-1 sodium carbonate, and 50 g kg^-1 lactic acid) retention capacities were measured, and variances estimated for genotype, genotype x environment interaction, and error terms. The variance of genotypes for SRC values in the three populations ranged from 0.67 to 0.97 of the total variance (genotype, genotype x environment, and error) not attributed to main effects of year and replication. Correlations among genotypes were significant and positive for water, sodium carbonate, and sucrose SRC within all three populations (r = 0.70 to 0.92, P < 0.01). In all three populations, these SRC values were negatively correlated with cookie diameter (r = -0.54 to -0.89, P < 0.01). Correlations between lactic acid SRC, a measure of gluten strength, and the other SRC values were complex, as was the correlation between lactic acid SRC and cookie diameter. This suggests that milling and baking quality could be improved through manipulation of flour components using SRC selection.

Abbreviations: AACC, American Association of Cereal Chemists • SKCS, Single Kernel Characterization System • SRC, solvent retention capacity

	INTRODUCTION

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

 
THE AMERICAN ASSOCIATION of Cereal Chemists (AACC) method 56-11, the SRC test, is used to evaluate soft wheat quality (Slade and Levine, 1994; Gaines, 2000). The SRC is the weight of solvent held by flour after centrifugation, expressed as a fraction of the original flour weight (140 g kg^-1 moisture basis). Retention of four water-based solvents (water; 500 g kg^-1 sucrose; 50 g kg^-1 sodium carbonate; and 50 g kg^-1 lactic acid) relates flour functionality to specific flour components, producing a practical quality functionality profile for predicting commercial baking performance. Sodium carbonate SRC is associated with levels of damaged starch, sucrose SRC is associated with pentosan and gliadin content, lactic acid SRC is associated with glutenin characteristics, and water SRC is influenced by all of these flour constituents (Gaines, 2000).

Previous studies with components of soft wheat quality suggest relatively limited genotype x environment interactions compared with the magnitudes of genetic and environmental effects. Bassett et al. (1989) found that location and genotype effects were at least an order of magnitude greater sources of variation than genotype x location effects in soft wheat milling characteristics and cookie quality. Similarly, milling characteristics of hard and soft wheat in the presence of artificially imposed moisture stress were influenced to a greater degree by genotype and moisture regime than the interaction of the two (Guttieri et al., 2001b). Previous work with the SRC test demonstrated significant differences among genotypes for the retention by flour of all four standard SRC solvents (Guttieri et al., 2001a). In contrast, genotype x environment interactions were insignificant for the four solvents. This is consistent with investigations of the flour components assayed by the SRC test. Hong et al. (1989) found variation among genotypes for pentosan content were much greater than variation associated with genotype x environment interaction. Gluten strength may be determined to a greater degree by year, moisture stress, and genotype than interactions of these components (Guttieri et al., 2000). This is not to dismiss the existence of genotype x environment interaction in the determination of end-use quality of wheat. However, it suggests that differences among genotypes for many quality attributes may be sufficiently large relative to the genotype x environment interaction to result in significant gain from selection for the SRC test.

Our previous study (Guttieri et al., 2001a) evaluated the utility of the SRC in cultivar evaluation. Solvent retention capacity profiles and milling and baking quality parameters were measured for 26 soft white spring wheat genotypes produced in seven irrigated environments. The SRC test effectively differentiated among genotypes across production environments with limited genotype x environment interaction. The objective of this study was to estimate the genotypic variance component for SRC tests relative to genotype x year interaction and error within three soft spring wheat populations unselected for SRC tests. Our goal was to provide an estimate of the test's utility for selection among inbred lines that plant breeders may generate in early stages of among-family selection.

	MATERIALS AND METHODS

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

 
Genotypes and Field Trials
Inbred lines of three crosses were evaluated. The cross ‘Vanna’/‘Penawawa’, was made in the greenhouse in 1995 and advanced without selection through the F₄ generation. Vanna and Penawawa are high yielding, soft white spring cultivars adapted to the Pacific Northwestern USA, with Vanna having better milling yield and cookie quality than Penawawa. In 1998, 150 heads of the Vanna/Penawawa population were randomly selected from a bulk F₄ population. Seed from individual heads was selected visually for kernel quality characteristics, and 60 heads were planted as F_4:5 families in 1999. Twenty-five families were selected for harvest based on agronomic appearance in the field (primarily short stature and moderate to early maturity). Grain from these families (F_4:6) was used to plant the 2000 trials. For this population and each of the other two populations, the 2001 trials were planted from the seed harvested from the 2000 trial.

The cross Kanto 107/IDO488 was made in the field in 1995. IDO488 is a high yield, early maturing, soft white spring wheat breeding line with the pedigree PI 294994/4*Centennial. Kanto 107 is a fall-seeded soft red wheat from Japan with a facultative vernalization requirement and good adaptation to high yield environments. The F₁ generation was planted in the greenhouse in 1995-1996. Generations were advanced by single-seed descent in the greenhouse through 1998 without vernalization. In 1998, individual F_5:6 rows were planted in the field from each of 22 individual plants grown in the greenhouse. Plots (F_5:7) were planted from seed from these families in 1999. Eighteen F_5:8 families produced sufficient grain to plant the 2000 trial.

The cross M2/IDO470 was made in the greenhouse in 1996. IDO470 is a high yielding, hard white spring wheat breeding line reselected from the cultivar ‘Idaho 377s’. M2 was obtained from the CIMMYT breeding program and is a synthetic wheat derived from the cross of a durum breeding line and an Aegilops squarrosa auct. (= Aegilops tauschii Coss.) selection. M2 is a large-seeded genotype with weak straw and low yield. M2 is also a soft kernelled genotype with relatively weak gluten. The F₁ was grown in the field in 1996. Generations were advanced by single-seed descent in the greenhouse through 1998. Individual F_5:6 rows were planted in the field in 1998 from 63 individual greenhouse plants. Plots of F_5:7 genotypes were planted in the field in 1999 from seed of the 1998 rows. Grain from these plots was tested for hardness using a Perten 4100 Single Kernel Characterization System (SKCS, Perten Instruments, North America Inc., Reno, NV). Twenty-six lines determined to be soft and having a minimum of 120 g of grain were planted in 2000 as F_5:8 genotypes.

Genotypes were grown in irrigated field trials at Aberdeen, ID, in 2000 and 2001. The experimental design was a randomized arrangement of a complete block design with two replications. The experimental unit was a 1.4- by 3.1-m plot of a single genotype. Trials were planted in early April and harvested in early September. The seeding rate was 60 g per plot. Fertilizer was applied before planting on the basis of soil tests, a target yield of 8000 kg ha^-1, and University of Idaho recommendations (Brown et al., 2001). Trials were irrigated with overhead sprinklers to replace the estimated loss of soil moisture due to crop transpiration (estimates obtained from the United States Bureau of Reclamation Pacific Northwest Cooperative Agricultural Weather Network). Grain yield was measured by the automated weighing system on the plot combine. Yield was adjusted to a 12% moisture basis using the moisture determined by the weighing system on the combine. Test weight was measured on cleaned grain samples. Grain quality samples of each genotype were collected from each replication in each year.

Milling and Baking and Solvent Retention Capacity Analyses
Grain from each genotype in each replication was measured for hardness, kernel diameter, and kernel weight using SKCS evaluation of a 300-kernel sample. Samples were tempered by standard methods (AACC method 26-10; American Association of Cereal Chemists, 1995) and milled with a Brabender Quadramat Senior Mill (AACC method 26-21A). Near-infrared analysis (Instalab 600, Dickey-John Corp., Auburn, IL; AACC method 39-10) was used to determine flour protein concentration with values calibrated by combustion analysis of total N content (LECO Model NFP-428, LECO Corp., St. Joseph, MO) and corrected to 120 g kg^-1. Sugar snap cookies were prepared and measured (AACC method 10-52). Solvent retention capacity of flour was measured using four water-based solvents according to the AACC method 56-11 (Gaines, 2000) with minor modifications as described previously (Guttieri et al., 2001a). Three of the solvents, water, 50 g kg^-1 lactic acid, and 50 g kg^-1 sodium carbonate were dispensed with automated repeating dispensers (VWR Labmax Bottle-Top Dispensers, Cat. No. 40000-066, VWR Scientific, Salt Lake, UT). The fourth solvent, 500 g kg^-1 sucrose, was dispensed manually with a pipet due to the high viscosity of the solvent. After initial manual suspension of weighed flour sample (5 g on a 140-g-kg^-1 moisture basis) in 25 mL of solvent within 50 mL centrifuge tubes, samples were horizontally agitated for 25 min on an orbital laboratory shaker at 100 rpm. Flour-solvent suspensions were centrifuged in a Jouan Model CR422 centrifuge (Jouan Inc., Winchester, VA) with an M4 swing-out rotor (Jouan part 11174191) using adapters for the conical centrifuge tubes. Centrifugation used the program parameters of 910 x g for 17 min at 20°C, with a radius of 176 mm, and an acceleration profile of 5 mm followed by a brake profile of 4 mm. The supernatant was decanted from the tubes and the pellet drained for 20 min by inverting the tubes. Samples were weighed and the SRC calculated as the sum of the pellet weight less the original flour weight divided by the original flour weight. The SRC is expressed as the grams of solvent retained per kilogram of flour.

Statistical Analyses
Analysis of variance of inbred lines within a family was conducted as described in Fehr (1987)(p. 250–252) and Hallauer and Miranda Filho (1981)(p. 90). The effect of trial year was considered a random environment effect and inbred lines were assumed to be random representatives of a population of soft kernelled inbred lines, adapted to the production environment. Sums of squares due to the random effects models were calculated using PROC GLM in SAS (SAS Institute, 1991). Population sizes for this experiment were similar to those used in a previous study to estimate variances among inbreds for complex traits (Mohammad et al., 1997). Each population was analyzed independently. The among-genotypes variance component (²_g) was calculated assuming that the expected mean square for variance among inbreds equaled the expected mean squares for genotype x year interaction plus the variance for genotypes multiplied by the number of years and the number replications (Hallauer and Miranda Filho, 1981):

where R is the number of replications within a year and Y is the number of years the experiment was repeated. Similarly, the variance for the genotype x environment interaction (²_gxe) was calculated as

and error variance (²_error) as the pooled mean squares for the genotype x environment x replication interaction. The size of the variance component for each trait is dependent on the units of measure for a trait. Therefore, it is necessary to standardize the variance components as a fraction of the total variation or the nonenvironmental variation before the comparison among traits (Bassett et al., 1989; Graybosch et al., 1996). Variance due to genotypes was standardized as a percentage of the total variation by the formula ²_g/. The standard errors for the standardized variance among genotypes numbers were calculated as described in Hallauer and Miranda Filho (1981)(p. 90–91).

	RESULTS AND DISCUSSION

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

 
Overview of Trials
The three populations evaluated represent crosses between parents of increasing divergence for adaptation to the test environment and increasing divergence in quality. Vanna and Penawawa are elite cultivars adapted to southern Idaho irrigated agriculture, and the population derived from the cross of these cultivars had the highest average flour yield and the largest average cookie diameter of the three populations (Table 1) . IDO488 is an elite soft white spring breeding line with similar yields to Vanna and Penawawa. The Kanto 107/IDO488 population involved had higher average grain yield and poorer soft wheat milling and baking quality (as measured by milling yield, break flour yield, and cookie diameter) than the Vanna/Penawawa population and the hardest grain of the three populations (Table 1). The M2/IDO470 population was the most divergent of the three parental pairs, derived from a soft synthetic wheat and a hard white spring wheat. Selection in the M2/IDO470 population before these tests for soft segregants resulted in derived lines having average grain hardness below 50 hardness units and less than the population mean for the soft x soft Kanto 107/IDO488 cross (Table 1). The M2/IDO470 population had the lowest grain yield of the three populations, yet break flour yields and cookie diameter were similar to the Kanto 107/IDO488 population. The M2/IDO470 population had larger kernels, in both diameter and weight, than the other two populations. The Kanto 107/IDO488 and the Vanna/Penawawa populations had similar mean kernel diameter and weights.

Requirements for gluten strength vary among products manufactured from soft wheat flour. The lactic acid SRC measures flour gluten strength, with higher values indicating greater gluten strength (Gaines, 2000). The Vanna/Penawawa population had the greatest average lactic acid SRC of the populations (Table 1). The M2/IDO470 population had the smallest average lactic acid SRC, yet the greatest range in lactic acid SRC, nearly three times greater than the range in the Vanna/Penawawa population (Table 1). Both the Vanna/Penawawa population and the M2/IDO470 population segregated for alternate alleles at the Glu-1D locus (2001, unpublished data; Guttieri et al., 2001a), which differ in their gluten strength in dough (Payne, 1987).

Variance among Genotypes for Agronomic and Quality Traits
We measured the agronomic and non-SRC quality traits within the three populations and reported the variances to provide a characterization of populations and references of variance components for known traits against which to compare the relatively unstudied SRC tests. Standardized variances among inbreds for agronomic traits ranged from 0.12 ± 0.37 for grain yield in the Vanna/Penawawa population to 0.95 ± 0.27 for plant height in the M2/IDO470 population (Table 2) . Standardized variance among inbreds for agronomic traits was greatest in the population derived from the cross of the unadapted x adapted parent (M2/IDO470) and least in the population derived from the cross of adapted x adapted parents (Vanna/Penawawa). The large standardized variance among inbreds for grain yield and test weight in the M2/IDO470 population was because of segregants with low yield and test weight rather than transgressive segregates with greater yield and test weight (Table 1). Of the four agronomic traits, plant height had the greatest standardized variance among inbreds (0.80 averaged across populations) and grain yield had the lowest standardized variance among inbreds (0.35 averaged across populations, Table 2). Kernel size or weight is an important factor for milling performance, yet is often measured as a yield component in agronomic studies. In these populations, the standardized variances of kernel weight and diameter were similar to those of plant height (Table 2).

Variation among Genotypes for Solvent Retention Capacity Values
The four solvents of the SRC test all had significant variance among genotypes in all three of the populations, with the standardized variance ranging from 0.67 to 0.97 (Table 2). The relative magnitudes of the variances for an SRC solvent were similar in each of the populations despite their different parentage and methods of inbred derivation. In previous studies, these solvent retention traits were correlated with milling yield and cookie diameter (Guttieri et al., 2001a; Gaines, 2000). Yet, in populations with very small standardized variances (1 SD from zero) for milling yield (Vanna/Penawawa) and cookie diameter (Kanto 107/IDO488), SRC values, which indirectly measure individual components of soft wheat quality, had significant variation based on F tests of genotypes and very large standardized variances (Tables 1, 2). This suggests that they might be manipulated through direct selection when selection among lines for milling yield and cookie diameter would likely produce limited improvement.

In all three populations water, sodium carbonate, and sucrose SRC were positively correlated, based on correlations among inbred lines averaged across years and replications (Table 3) . Water SRC was included in the SRC profile as a summary solvent that is affected by all hydrophilic flour components. Therefore, the correlation of water SRC with sodium carbonate and sucrose SRC values is understandable, as those two solvents measure the relative contributions of damaged starch and pentosans to overall water absorption. The correlation of sodium carbonate SRC with sucrose SRC among the inbred lines is consistent with previous results from irrigated experiments in Idaho (Guttieri et al., 2001a). The correlations among lines within three divergent populations suggests that, within these environments, differences in flour-damaged starch and pentosan levels may be controlled by common genetic factors.

Selection for SRC is of little utility unless it reflects inherent, reproducible differences among genotypes for traits of economic value. Previous work has established these tests as quantitative measures of flour functionality (Slade and Levine, 1994; Gaines, 2000). Among the inbred lines of this study, water, sodium carbonate, and sucrose SRC are correlated with the two primary measures of economic value, flour extraction and cookie diameter (Table 3). Therefore, selection for lower SRC values using these solvents should result in gain from selection for the SRC values and, in turn, identify genotypes with greater milling yield and larger cookie diameter.

The SRC values of flour from a grain sample also may depend on the level of flour extraction. Greater levels of flour extraction often result in greater levels of damaged starch and bran fraction within the flour, which would elevate SRC values, particularly for sodium carbonate and sucrose. The AACC protocol for milling using the Brabender Quadrumat Senior uses a fixed gap between the rolls. Therefore, the protocol provides a measure of the ease of flour extraction in a uniform gap roll setting, rather than measuring the total potential flour extraction as might be achieved from a pilot scale or commercial mill. A mill type x genotype interaction might affect the ability of SRC evaluation from a Brabender Quadrumat Senior-milled flour to predict the SRC values that would be obtained on a commercially milled flour. However, a mill x genotype interaction for SRC evaluations likely would be of similar magnitude to that for the sugar-snap cookie test, the alkaline water retention test (AACC 56-10), or other commonly used cereal chemistry evaluations.

The correlation between cookie diameter and lactic acid SRC was not uniform among the three populations. Greater cookie diameter was correlated with lower lactic acid SRC in the Vanna/Penawawa population. This suggests a negative correlation of gluten strength with cookie diameter. Previous studies of soft white spring germplasm have demonstrated similar results (Souza et al., 1994; Guttieri et al., 2001a). However, the strong positive correlation of lactic acid SRC with sodium carbonate, sucrose, and water SRC values in the Vanna/Penawawa population suggests the possibility that the correlation between gluten strength and cookie diameter in the Vanna/Penawawa population may not be direct, but indirect through common factors that simultaneously affect all four solvents. Lactic acid SRC was uncorrelated with cookie diameter or flour yield in the Kanto 107/IDO488 and M2/IDO470 populations (Table 3). This suggests that gluten strength may be manipulated independently of cookie diameter and flour yield in these populations. Because gluten strength is useful for some soft wheat products, such as crackers and some machined cookies (Slade and Levine, 1994; Gaines, 2000), populations similar to Kanto 107/IDO488 or M2/IDO470 may be useful for improvement of gluten strength without degrading other quality factors.

	CONCLUSIONS

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

 
Variances among genotypes were significant for water, sodium carbonate, sucrose, and lactic acid SRC values in all three populations. The average standardized variance due to genotype across all three populations for water (0.73), sodium carbonate (0.87), sucrose (0.89), and lactic acid (0.88) were similar in magnitude to standardized variances for plant height and grain hardness (Table 2). Plant height is among the traits most easily influenced through selection when variation exists within a population, because of its high heritability. The large genotypic variation relative to genotype x year variation for the four solvent retention capacities is not surprising given that: (i) genetic differences among soft wheats have been previously identified; (ii) limited genotype x environment interaction appears to be associated with SRC values in the test environments (see also Guttieri et al., 2001a); and (iii) neither the parents nor these populations have been selected using the SRC test, reducing the opportunity for fixation of quantitative variation for SRC values.

The similarity in variance components among inbred lines for three populations with parents differing for adaptation and functionality suggests that selection should improve both elite populations, such as Vanna/Penawawa population, and unadapted populations, such as the M2/IDO470 population. Interestingly, lines with the lowest sucrose SRC (desirable for soft wheats) were found in the unadapted cross of M2/IDO470 (Table 1), as were lines with the lowest and highest lactic acid SRC, traits desirable in club cultivars and soft wheats used for crackers, respectively. This suggests that additional desirable variation may be found in secondary pools of germplasm, such as the synthetic wheats or hard classes, which may then be incorporated into elite soft wheat populations through selection with the SRC test. Gain from selection with SRC solvents should produce correlated responses in the flour components assayed by the solvents (damaged starch, pentosan, and gluten) and improvements in the foods sensitive to changes in these components such as cookies, crackers, cakes, and many snack foods.

	ACKNOWLEDGMENTS

 
This research was supported by Kraft-Nabisco and by the University of Idaho Agricultural Experiment Station. We wish to acknowledge the technical assistance of Cecile Becker, Jack Clayton, and Katherine O'Brien.

	NOTES

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

 
Univ. of Idaho Agric. Exp. Stn. Paper no. 02717.

Received for publication November 22, 2002.

	REFERENCES

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

 

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This Article Abstract Full Text (PDF) Free Alert me when this article is cited Alert me if a correction is posted Services Related articles in Crop Science Similar articles in this journal Similar articles in ISI Web of Science Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via ISI Web of Science (6) Citing Articles via Google Scholar Google Scholar Articles by Guttieri, M. J. Articles by Souza, E. Search for Related Content PubMed Articles by Guttieri, M. J. Articles by Souza, E. Agricola Articles by Guttieri, M. J. Articles by Souza, E. Related Collections Crop Genetics Wheat Biometrics