The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
A Modified Colorimetric Method for Phytic Acid Analysis in Soybean
Abstract
A quantitative, reproducible, and efficient phytic acid assay procedure is needed to screen breeding populations and support genetic studies in soybeans [Glycine max (L.) Merr.]. The objective of this study was to modify the colorimetric Wade reagent method and compare the accuracy and applicability of this new method in determining seed phytic acid content in soybean with three well‐established phytic acid assay methods: anion exchange column (AEC), high‐performance liquid chromatography (HPLC), and 31 P nuclear magnetic resonance (NMR). The CV for repeated measurements of a low phytic acid soybean mutant, CX1834‐1‐6, ranged from 1.8 to 4.2% (n = 5), indicating the results were precise and reproducible. Phytic acid content of 42 soybean genotypes as determined by this method showed a correlation of 93.7 to 96.6% with the measurements by AEC, HPLC, and NMR. According to analysis of covariance, using inorganic P content as a predictor, phytic acid P content in a given sample analyzed by the four assay methods can be estimated with four linear regression models at the α = 0.01 level. Compared with HPLC, AEC, and 31 P NMR, this modified colorimetric method is simpler and less expensive for assaying a large number of samples, allowing its effective application in breeding and genetic studies of low phytic acid soybean.
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Cites
Microdetermination of Phosphorus
Chen, P. S., Toribara, T. Y., & Warner, H. (1956) · Analytical Chemistry · doi:10.1021/ac60119a033 · Cited by 7,199
Sensitive method for the rapid determination of phytate in cereals and cereal products
Haug, W., & Lantzsch, H. (1983) · Journal of the Science of Food and Agriculture · doi:10.1002/jsfa.2740341217 · Cited by 1,022
A simple and rapid colorimetric method for phytate determination
Latta, M., & Eskin, M. (1980) · Journal of Agricultural and Food Chemistry · doi:10.1021/jf60232a049 · Cited by 907
Vaintraub, I. A., & Lapteva, N. A. (1988) · Analytical Biochemistry · doi:10.1016/0003-2697(88)90382-x · Cited by 460
Origin and Seed Phenotype of Maize low phytic acid 1-1 and low phytic acid 2-1
Raboy, V., Gerbasi, P. F., Young, K. A., Stoneberg, S. D., Pickett, S. G., Bauman, A. T., Murthy, P. P. N., Sheridan, W. F., & Ertl, D. S. (2000) · Plant Physiology · doi:10.1104/pp.124.1.355 · Cited by 444
Isolation of High Seed Inorganic P, Low‐Phytate Soybean Mutants
Wilcox, J. R., Premachandra, G. S., Young, K. A., & Raboy, V. (2000) · Crop Science · doi:10.2135/cropsci2000.4061601x · Cited by 284
Phytates in Cereals and Legumes
Reddy, N. R. (1989) · Cited by 260
The Maize Low-Phytic Acid Mutant lpa2 Is Caused by Mutation in an Inositol Phosphate Kinase Gene
Shi, J., Wang, H., Wu, Y., Hazebroek, J., Meeley, R. B., & Ertl, D. S. (2003) · Plant Physiology · doi:10.1104/pp.014258 · Cited by 234
A Modified Method for the Indirect Quantitative Analysis of Phytate in Foodstuffs
Frühbeck, G., Alonso, R., Marzo, F., & Santidrián, S. (1995) · Analytical Biochemistry · doi:10.1006/abio.1995.1145 · Cited by 123
Harland, B. F., & Oberleas, D. (1977) · Cereal Chemistry · Cited by 79
Cited by
Ramesh, A., Sharma, S. K., Sharma, M. P., Yadav, N., & Joshi, O. P. (2014) · Applied Soil Ecology · doi:10.1016/j.apsoil.2013.08.009 · Cited by 416
Taherian, A. R., Mondor, M., Labranche, J., Drolet, H., Ippersiel, D., & Lamarche, F. (2011) · Food Research International · doi:10.1016/j.foodres.2011.01.030 · Cited by 157
Metadata
- Authors
- Gao, Y., Shang, C., Maroof, M. A. S., Biyashev, R. M., Grabau, E. A., Kwanyuen, P., Burton, J. W., & Buss, G. R.
- Venue
- Crop Science
- Publisher
- Wiley
- Published
- 2007-09
- Volume
- 47
- Issue
- 5
- Pages
- 1797-1803
- ISSN
- 0011-183X (ISSN-L)
- 1435-0653
- OpenAlex
- W2045356475
- Cited by
- 279
- References
- 10
- FWCI
- 3.47
- Type
- journal article
- Language
- en
- Open access
- closed
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- Gao, Y., Shang, C., Maroof, M. A. S., Biyashev, R. M., Grabau, E. A., Kwanyuen, P., Burton, J. W., & Buss, G. R. (2007). A Modified Colorimetric Method for Phytic Acid Analysis in Soybean. Crop Science. https://doi.org/10.2135/cropsci2007.03.0122
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