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CROP BREEDING, GENETICS & CYTOLOGY

Association of Total Sugar Content with Other Seed Traits of Diverse Soybean Cultivars

Dep. of Agronomy, Iowa State Univ., Ames, IA 50011 USA

wfehr{at}iastate.edu

	ABSTRACT

TOP NOTES ABSTRACT INTRODUCTION Materials and methods Results and discussion REFERENCES

 
Total sugar content of soybean [Glycine max (L.) Merr.] seeds is considered to be important for the quality of some soyfoods. The objective of this study was to determine the association of total sugar with other seed traits of soybean to facilitate the development of cultivars with appropriate seed composition. Total sugar, protein, oil, fiber, and seed size of 23 conventional and food-grade cultivars were evaluated in replicated tests at eight Iowa locations in 1998. Total sugar content was determined by acid hydrolysis, and protein, oil, and fiber were measured by near-infrared reflectance (NIR). There were significant differences among the cultivars for all traits. Total sugar was most highly correlated (-0.81) with the sum of protein + oil (P + O). Seed size had a major influence on the association of total sugar with protein, oil, and fiber individually. Analysis of P + O by NIR was not influenced by seed size and should be a useful method of predicting differences among genotypes for total sugar content.

Abbreviations: CORR, correlation procedure • GLM, general linear model • NIR, near-infrared reflectance • P + O, protein + oil • P + O + F, protein + oil + fiber

	INTRODUCTION

TOP NOTES ABSTRACT INTRODUCTION Materials and methods Results and discussion REFERENCES

 
THE TOTAL SUGAR CONTENT OF SOYBEAN SEED has an influence on the quality characteristics of some soyfood products. Geater et al. (2000) reported that the hardness of natto, a fermented product consumed primarily in Japan, was negatively correlated with the total sugar of small-seeded genotypes developed for the natto industry.

Total sugar is not routinely measured in a soybean breeding program because the current methods of analysis are too time consuming for assessment of a large number of genotypes. In contrast, protein content can be evaluated for a large number of genotypes by near-infrared reflectance (NIR) and is an important selection criterion in development of food-grade cultivars. It would be useful to know how selection for protein and other seed traits in a breeding program influences total sugar and to determine if it would be possible to use other traits as an indirect estimate of total sugar. Krober and Cartter (1962) reported that increases in protein were associated with decreases in total soluble sugar. The sum of protein + oil + fiber (P + O + F) minus 100 is used as an indirect estimate of total sugar content by the Grain Quality Laboratory at Iowa State University (C.R. Hurburgh Jr., 1999, personal communication). Geater et al. (2000) reported that protein, the sum of protein + oil (P + O), and P + O + F had phenotypic correlation coefficients of -0.90 or greater with total sugar for 16 small-seeded genotypes. Their results indicated selection for any of the three traits would decrease total sugar, and that any of the traits would be useful for predicting the differences among genotypes for total sugar.

The study of Geater et al. (2000) was limited to small-seeded genotypes that were suitable for the production of natto. The objective of our study was to determine the relationship of total sugar with other seed traits for food-grade cultivars that differed markedly in protein and seed size to meet the requirements for a variety of soyfood products.

	Materials and methods

TOP NOTES ABSTRACT INTRODUCTION Materials and methods Results and discussion REFERENCES

 
Twenty-three cultivars developed by Iowa State University were selected for the experiment. IA2021 was a general-use cultivar; IA2012, IA2019, and IA2040 were large-seeded cultivars; HP204, Vinton 81, IA2017, IA2020, IA2034, IA2041, IA2042, and IA3011 were large-seeded, high-protein cultivars; IA2023, IA2024, and IA2035 were small-seeded cultivars; IA2011 was a large-seeded, high-protein cultivar that lacked the lipoxygenase-2 enzyme; and IA2025, IA2027, IA2028, IA2029, IA2030, IA2032, and IA2033 were large-seeded, high-protein cultivars that lacked all three lipoxygenase enzymes. The cultivars were grown in three replications of a randomized complete-block design in 1998 at Ames, Fairfield, Grand Junction, Greene, Griswold, Kanawha, Sioux Rapids, and Winterset, IA. Soil types at the eight locations were a Nicollet loam (fine-loamy, mixed, superactive, mesic Aquic Hapludoll) at Ames and Grand Junction, a Haig silty clay loam (fine, smectitic, mesic Vertic Argiaquoll) at Fairfield, a Kenyon silty clay loam (fine-loamy, mixed, mesic Typic Hapludoll) at Greene, a Marshall silty clay loam (fine-silty, mixed, superactive, mesic Typic Hapludoll) at Griswold, a Webster silty clay loam (fine-loamy, mixed, superactive, mesic Typic Endoaquoll) at Kanawha, a Primghar silty clay loam (fine-silty, mixed, superactive, mesic Aquic Hapludoll) at Sioux Rapids, and a Winterset silty clay loam (fine, smectitic, mesic Typic Argiaquoll) at Winterset. The entries were planted in four-row plots 3.1 m long with a row spacing of 69 cm and a seeding rate of 30 seeds m^-1. The center two rows were harvested with a self-propelled combine. The samples for each replication were analyzed from the lowest to the highest plot number assigned for the randomized complete-block design in the field tests.

The seed from each plot was analyzed for total sugar content by the method described by Geater et al. (2001). For each plot, 10 g of seed was ground with a Cyclotec 1093 Sample Mill (Tecator Inc., Herndon, VA). A 150-mg sample of the powder was placed in a 16- by 125-mm screw cap tube. To each tube, 10 mL of distilled water and 1 mL of 25% (w/w) HCl were added. The tubes were capped with rubber-lined phenolic screw caps, vortexed at a speed setting of 4 for 5 s using a Fisher Scientific (Pittsburgh, PA) Vortex Genie 2 Mixer, and placed in an autoclave at 121°C for 20 min. The tubes were removed and cooled to room temperature in a 1°C water bath for 5 min. To each tube, 275 µL of 40% (w/v) NaOH was added. The tubes were capped, and the solution was mixed by inverting the tubes five times. The contents of each tube were emptied into a 500-mL volumetric flask. The flask was filled to 500 mL with distilled water, capped with a polyethylene snap cap, and mixed by inverting 25 times. The modified phenol-sulfuric acid method as described by Fox and Robyt (1990) was used to analyze the sugar content of the solution. A 25-µL aliquot of the test solution and 25 µL of 5% (w/v) phenol were pipetted in triplicate into a 96-well general assay plate. In addition to the test samples, standards of known glucose concentration were placed in triplicate wells of each plate. The standards were 0 µg mL^-1 (distilled water blank), 10 µg mL^-1, 30 µg mL^-1, 50 µg mL^-1, 70 µg mL^-1, and 90 µg mL^-1 of glucose. After all the samples were loaded, the plate was vortexed for 30 s at a speed setting of 1 on a Fisher Scientific Vortex Genie 2 Mixer fitted with a Fisher Scientific Microwell Plate Insert. The plate was placed on crushed ice, and 125 µL of concentrated H₂SO₄ was added to each well. The plate was mixed for 30 s at a speed setting of 1. The plate was sealed in a plastic zipper bag and warmed in a water bath at 80°C for 30 min. Each plate was read with a Bio-Tek Instruments (Winooski, VT), Model EL312e Bio-Kinetics Reader at 490 nm. The glucose concentration of each test sample was determined by comparing the absorbance of the test sample of a plot to the absorbances of the glucose standards. Total sugar content was expressed in grams per kilogram on a moisture-free basis by dividing the mean concentration of glucose (µg mL^-1) in a test sample by 300 µg mL^-1 (the concentration of soybean powder in the test sample) and multiplying by 1000 g kg^-1. Total sugar content was adjusted to a moisture-free basis by dividing the total sugar content of a sample by [(100–moisture percentage of the sample) / 100]. The mean total sugar of the triplicates for each test sample was used for data analysis.

Seed moisture, protein, oil, and fiber content were measured on a 100-g bulk sample of seed from each plot with a Tecator A/B Infratech 1221 whole grain NIR analyzer. Protein, oil, and fiber contents were expressed on a moisture-free basis. Seed size was expressed in milligrams per seed and was based on the weight of a random sample of 200 seeds.

The data were analyzed as a randomized complete-block design. Locations and replications were considered random effects, and cultivars were considered fixed effects. The analysis of variance was performed with the general linear models (GLM) procedure of SAS (SAS Institute, 1992). Phenotypic correlation coefficients among the traits were calculated for cultivar means across locations with the correlation procedure (CORR) of SAS (SAS Institute, 1992).

	Results and discussion

TOP NOTES ABSTRACT INTRODUCTION Materials and methods Results and discussion REFERENCES

 
The mean total sugar at the eight locations ranged from 190 to 214 g kg^-1. The main effect of locations in the combined analysis of variance was not significant when the replications/locations mean squares were used in the denominator for the F-test (Table 1) . There were highly significant differences (P < 0.01) among replications for total sugar content at five of the eight individual locations, which inflated the replications/locations mean squares and resulted in an underestimate of the significance of the differences among the location means. Geater et al. (2000) observed significant differences among locations in 1 yr and a significant year x location interaction for total sugar of small-seeded cultivars evaluated at three Iowa locations during 2 yr.

There were significant differences among the cultivars for protein, oil, fiber, P + O, P + O + F, and seed size (Tables 1 and 2). The variation for protein, oil, and seed size was expected because the cultivars selected for the study were bred for differences in the three traits.

The associations between total sugar and fiber and between fiber and seed size strongly influenced the associations of total sugar with protein and oil. The significant phenotypic correlation of 0.49 between total sugar and fiber for the 23 cultivars was similar to the phenotypic correlation of 0.46 between the two traits obtained by Geater et al. (2000) for 16 small-seeded genotypes (Table 3) . The phenotypic correlation between fiber and seed size for the 23 cultivars was -0.90, and the correlation between the traits reported by Geater et al. (2000) was -0.43. These correlations reflect the interrelationship between the three traits. The percentage of seed coat and fiber increases as the size of seed decreases (Krober and Cartter, 1962; Hurburgh et al., 1995). The fiber in the seed coat contains about 80% carbohydrate compared with 24% carbohydrate in whole seeds and with 30% in dehulled, defatted soy flour (Honig and Rackis, 1979). Some of the sugar components in the fiber of the seed coat are removed by acid hydrolysis and contribute to the total sugar content of the seed. The interrelationship of the three traits was demonstrated by the cultivars IA2023, IA2024, and IA2035, which had the highest total sugar and fiber contents and the smallest seed size (Table 2).

The association between total sugar and oil also was strongly influenced by the three small-seeded cultivars. There was a significant negative correlation of -0.42 between the two traits when the small-seeded cultivars were included and a positive correlation of 0.23 when they were excluded from the analysis. A significant positive correlation between total sugar and oil of 0.65 was reported by Geater et al. (2000) for small-seeded genotypes.

P + O had the greatest association with total sugar and was not influenced by the seed size of the cultivars (Table 3). The phenotypic correlation between the two traits was -0.81 when the three small-seeded cultivars were included and -0.87 when the cultivars were excluded from the analysis. Geater et al. (2000) reported a phenotypic correlation of -0.90 between the two traits for small-seeded genotypes. The correlation between total sugar and P + O + F was -0.69 when the small-seeded cultivars were included and -0.87 when the cultivars were excluded from the analysis.

The current methods for direct analysis of total sugar content are much less rapid than the analysis of protein, oil, and fiber content by NIR. For the evaluation of a large number of genotypes in a cultivar development program, P + O obtained by NIR analysis should serve as a useful predictor of total sugar content. To validate the relationship, it would be desirable to evaluate random soybean lines from breeding populations in multiple locations and years.

	NOTES

TOP NOTES ABSTRACT INTRODUCTION Materials and methods Results and discussion REFERENCES

 
Journal Paper no. J-18745 of the Iowa Agric. and Home Econ. Exp. Stn. Project no. 3107, and supported by the Hatch Act, State of Iowa, and Iowa Soybean Promotion Board.

Received for publication February 7, 2000.

	REFERENCES

TOP NOTES ABSTRACT INTRODUCTION Materials and methods Results and discussion REFERENCES

 

• Fox J.D., Robyt J.F. Miniaturization of three carbohydrate analyses using a microsample plate reader. Anal. Biochem. 1990;195:93-96.
• Geater C.W., Fehr W.R., Wilson L.A. Soybean seed traits influence natto quality. Crop Sci. 2000;40:1529-1534 (this issue).[Abstract/Free Full Text]
• Geater, C.W., W.R. Fehr, L.A. Wilson, and J.F. Robyt. 2001. An improved method of total sugar analysis for soybean seed. Crop Sci. 41(1).
• Honig D.H., Rackis J.J. Determination of the total pepsin-pancreatin indigestible content (dietary fiber) of soybean products, wheat bran, and corn bran. J. Agric. Food Chem. 1979;27:1262-1266.[Medline]
• Hurburgh C.R., Jr., Wu Y., Siska J. Effect of seed size and density on near-infrared transmittance analysis of corn and soybeans. Appl. Eng. Agric. 1995;11:677-684.
• Krober O.A., Cartter J.L. Quantitative interrelations of protein and nonprotein constituents of soybeans. Crop Sci. 1962;2:171-172.
• SAS Institute. The SAS system for Windows. Cary, NC: Release 6.12. SAS Inst, 1992.
• Taira H. Quality of soybeans for processed foods in Japan. Japan Agric. Res. Quarterly 1990;24:224-230.

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