| 000 | 02909nab a22004577a 4500 | ||
|---|---|---|---|
| 999 |
_c57935 _d57927 |
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| 001 | 57935 | ||
| 003 | MX-TxCIM | ||
| 005 | 20240919020948.0 | ||
| 008 | 121211s2016 s |||p op||| | eng d | ||
| 022 | 0 | _a0023-6438 (Online) | |
| 024 | 8 | _ahttps://doi.org/10.1016/j.lwt.2016.01.068 | |
| 040 | _aMX-TxCIM | ||
| 041 | 0 | _aeng | |
| 100 | 1 |
_9957 _aGuzman, C. _gGlobal Wheat Program _8INT3466 |
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| 245 | 1 | 0 | _aUse of rapid tests to predict quality traits of CIMMYT bread wheat genotypes grown under different environments |
| 260 |
_aSwitzerland : _bElsevier, _c2016. |
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| 500 | _aPeer review | ||
| 520 | _aAt the International Maize and Wheat Improvement Center (CIMMYT), wheat quality improvement is an important goal of breeding. CIMMYT scientists develop germplasm, which is diverse for quality traits intended for use in the preparation of different wheat-based products. The integration of quality traits is complex due to the high cost of conducting traditional quality tests. One option for tackling this problem is the use of such rapid-small-scale methods as Solvent Retention Capacity (SRC), SDS Sedimentation (SDSS) and Swelling Index of Glutenin (SIG) to predict flour performance. The objectives of this study were to investigate the effect of genotypes, contrasting environmental conditions and their interactions (GxE) on different rapid-small-scale tests, and to identify their suitability for use in prediction of quality traits. A significant GxE effect was observed for all three methodologies. Overall, SIG was found to be the best predictor of gluten strength across different environments. It was also best at determining bread-making quality in some environments, followed by SDSS for bread making. SRC was found to be useful to select for gluten strength, but for extensibility and bread-making more grain data is needed. | ||
| 536 | _aGlobal Wheat Program | ||
| 536 | _aGenetic Resources Program | ||
| 546 | _aText in english | ||
| 591 | _bCIMMYT Informa: 1970 (May 31, 2016) | ||
| 594 | _aINT3211 | ||
| 594 | _aINT2983 | ||
| 594 | _aCCJL01 | ||
| 594 | _aINT0610 | ||
| 650 | 7 |
_91265 _aSoft wheat _2AGROVOC |
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| 650 | 7 |
_91134 _aGenotypes _2AGROVOC |
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| 700 | 1 |
_aMondal, S. _gFormerly Global Wheat Program _8INT3211 _9904 |
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| 700 | 1 |
_aVelu, G. _9880 _8INT2983 _gGlobal Wheat Program |
|
| 700 | 1 |
_aAutrique, E. _9969 _8N1203511 _gGlobal Wheat Program |
|
| 700 | 1 |
_aPosadas Romano, G. _92181 |
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| 700 | 1 |
_aCervantes, F. _93541 |
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| 700 | 1 |
_aCrossa, J. _gGenetic Resources Program _8CCJL01 _959 |
|
| 700 | 1 |
_aVargas, M. _93542 |
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| 700 | 1 |
_aSingh, R.P. _gGlobal Wheat Program _8INT0610 _9825 |
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| 700 | 1 |
_aPeña-Bautista, R.J. _8INT0368 _gGlobal Wheat Program _9645 |
|
| 773 | 0 |
_tFood Science and Technology _gv. 69, p. 327-333 _dSwitzerland : Elsevier, 2016. _x0023-6438 |
|
| 856 | 4 |
_uhttps://hdl.handle.net/20.500.12665/406 _yAccess only for CIMMYT Staff |
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| 942 |
_cJA _2ddc _n0 |
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