Effect of HMM Glutenin Subunits on Wheat Quality Attributes

被引:0
作者
Horvat, Daniela [1 ]
Kurtanjek, Zelimir [2 ]
Drezner, Georg [1 ]
Simic, Gordana [1 ]
Magdic, Damir [3 ]
机构
[1] Agr Inst Osijek, HR-31000 Osijek, Croatia
[2] Univ Zagreb, Fac Food Technol & Biotechnol, HR-10000 Zagreb, Croatia
[3] Univ JJ Strossmayer Osijek, Fac Food Technol, HR-31000 Osijek, Croatia
关键词
wheat; technological quality; HMM-GS; principal components; cluster analysis; BREAD-MAKING QUALITY; CULTIVARS; PROTEINS; IMPACT; FLOUR;
D O I
暂无
中图分类号
Q81 [生物工程学(生物技术)]; Q93 [微生物学];
学科分类号
071005 ; 0836 ; 090102 ; 100705 ;
摘要
Glutenin is a group of polymeric gluten proteins. Glutenin molecules consist of glutenin subunits linked together with disulphide bonds and having higher (HMM-GS) and lower (LMM-GS) molecular mass. The main objective of this study is the evaluation of the influence of HMM-GS on flour processing properties. Seven bread wheat genotypes with contrasting quality attributes and different HMM-GS composition were analyzed during three years. The composition and quantity of HMM-GS were determined by SDS-PAGE and RP-HPLC, respectively. The quality diversity among genotypes was estimated by the analysis of wheat grain, and flour and bread quality parameters. The presence of HMM glutenin subunits 1 and 2* at Glu-A1 and the subunits 5+10 at Glu-D1 loci, as well as a higher proportion of total HMM-GS, had a positive effect on wheat quality. Cluster analysis of the three groups of data (genotype and HMM-GS, flour and bread quality, and dough rheology) yielded the same hierarchical structure for the first top three levels, and similarity of the corresponding dendrograms was proved by the principal eigenvalues of the corresponding Euclidian distance matrices. The obtained similarity in classification based on essentially different types of measurements reflects strong natural association between genetic data, product quality and physical properties. Principal component analysis (PCA) was applied to effectively reduce large data set into lower dimensions of latent variables amenable for the analysis. PCA analysis of the total set of data (15 variables) revealed a very strong interrelationship between the variables. The first three PCA components accounted for 96 % of the total variance, which was significant to the level of 0.05 and was considered as the level of experimental error. These data imply that the quality of wheat cultivars can be contributed to HMM-GS data and should be taken into account in breeding programs assisted by computer models with the aim to improve flour technological quality.
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页码:253 / 259
页数:7
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