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Effect of high molecular weight glutenins and rye translocations on soft wheat flour cookie quality |
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| Institution: | 1. Facultad de Ciencias Agropecuarias, Universidad Nacional de Córdoba, CC 509, 5000 Córdoba, Argentina;2. Instituto de Ciencia y Tecnología de Alimentos Córdoba, ICYTAC-CONICET, Argentina;3. INTA EEA Marcos Juárez, CC 21, 2580 Marcos Juárez, Argentina;4. INTA Instituto de Recursos Biológicos, CIRN. De Los Reseros y N. Repetto, 1686 Hurlingham, Argentina;1. State Engineering Laboratory for Cereal Fermentation Technology, Jiangnan University, 1800 Lihu Avenue, Wuxi 214122, Jiangsu Province, PR China;2. School of Food Science and Technology, Jiangnan University, 1800 Lihu Avenue, Wuxi 214122, Jiangsu Province, PR China;3. Wheat Marketing Center, Inc., 1200 N.W. Naito Parkway, Suite 230, Portland, OR 97209, USA;1. Food Technology Area, College of Agricultural Engineering, University of Valladolid, 34004 Palencia, Spain;2. Institute of Agrochemistry and Food Technology (IATA-CSIC), Avda Agustin Escardino, 7, Paterna 46980, Spain;1. Advanced Plant Technology Program, Clemson University, 105 Collings St., Clemson, SC, 29634-0141, USA (formerly Western Wheat Quality Laboratory);2. USDA-ARS Western Wheat Quality Laboratory, E-202 Food Quality Building, Washington State University, Pullman, WA, 99164-6394, USA;3. Department of Crop and Soil Sciences, Washington State University, Pullman, WA, 99164, USA;1. School of Tea and Food Science, Anhui Agricultural University, No. 130 West Changjiang Road, Hefei, Anhui 230036, China;2. Wheat Marketing Center, Inc., 1200 NW Naito Parkway, Suite 230, Portland, OR 97209, USA;3. Chopin Technologies, Villeneuve-la-Garenne Cedex 92396, France |
| Abstract: | The influence of high molecular weight glutenin subunits (HMW-GS) on wheat breadmaking quality has been extensively studied but the effect of different Glu-1 alleles on cookie quality is still poorly understood. This study was conducted to analyze the effect of HMW-GS composition and wheat-rye translocations on physicochemical flour properties and cookie quality of soft wheat flours. Alleles encoded at Glu-A1, Glu-B1 and Glu-D1 locus had a significant effect over physicochemical flour properties and solvent retention capacity (SRC) profile. The null allele for Glu-A1 locus presented the highest cookie factor observed (CF = 7.10), whereas 1BL/1RS and 1AL/1RS rye translocations had a negative influence on CF. The three cultivars that showed the highest CF (19, 44 and 47) had the following combination: Glu-A1 = null, Glu-B1 = 7 + 8, Glu-D1 = 2 + 12 and no secalins. Two prediction equations were developed to estimate soft wheat CF: one using the HMW-GS composition and the other using physicochemical flour parameters, where SRCsuc, SRC carb, water-soluble pentosans, damaged starch and protein turned out to be better CF predictors. This data suggests that grain protein allelic composition and physicochemical flour properties can be useful tools in breeding programs to select soft wheat of good cookie making quality. |
| Keywords: | Soft wheat High molecular weight glutenins Wheat-rye translocation Cookie HMW-GS"} {"#name":"keyword" "$":{"id":"kwrd0035"} "$$":[{"#name":"text" "_":"high molecular weight glutenin subunit LMW-GS"} {"#name":"keyword" "$":{"id":"kwrd0045"} "$$":[{"#name":"text" "_":"low molecular weight glutenin subunit SDS-PAGE"} {"#name":"keyword" "$":{"id":"kwrd0055"} "$$":[{"#name":"text" "_":"sodium dodecyl sulfate polyacrylamide gel electrophoresis A-PAGE"} {"#name":"keyword" "$":{"id":"kwrd0065"} "$$":[{"#name":"text" "_":"acid polyacrylamide gel electrophoresis PSA"} {"#name":"keyword" "$":{"id":"kwrd0075"} "$$":[{"#name":"text" "_":"particle size analysis WSP"} {"#name":"keyword" "$":{"id":"kwrd0085"} "$$":[{"#name":"text" "_":"water-soluble pentosans TP"} {"#name":"keyword" "$":{"id":"kwrd0095"} "$$":[{"#name":"text" "_":"total pentosans DS"} {"#name":"keyword" "$":{"id":"kwrd0105"} "$$":[{"#name":"text" "_":"damaged starch SRC"} {"#name":"keyword" "$":{"id":"kwrd0115"} "$$":[{"#name":"text" "_":"solvent retention capacity profile SRCsuc"} {"#name":"keyword" "$":{"id":"kwrd0125"} "$$":[{"#name":"text" "_":"solvent retention capacity sucrose SRCcarb"} {"#name":"keyword" "$":{"id":"kwrd0135"} "$$":[{"#name":"text" "_":"solvent retention capacity carbonate SRCw"} {"#name":"keyword" "$":{"id":"kwrd0145"} "$$":[{"#name":"text" "_":"solvent retention capacity water SRClac"} {"#name":"keyword" "$":{"id":"kwrd0155"} "$$":[{"#name":"text" "_":"solvent retention capacity lactic CF"} {"#name":"keyword" "$":{"id":"kwrd0165"} "$$":[{"#name":"text" "_":"cookie factor ML"} {"#name":"keyword" "$":{"id":"kwrd0175"} "$$":[{"#name":"text" "_":"maximum likelihood LSD"} {"#name":"keyword" "$":{"id":"kwrd0185"} "$$":[{"#name":"text" "_":"least significant difference MSPE"} {"#name":"keyword" "$":{"id":"kwrd0195"} "$$":[{"#name":"text" "_":"square predictive error MANOVA"} {"#name":"keyword" "$":{"id":"kwrd0205"} "$$":[{"#name":"text" "_":"multivariance analysis of variance BLUP"} {"#name":"keyword" "$":{"id":"kwrd0215"} "$$":[{"#name":"text" "_":"best linear unbiased predictor coefficient |
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