摘要
以32个云南高原粳稻主栽品种为试验材料,采用聚类及表型主成分分析法,对11个品质性状和淀粉RVA(Rapid Visco Analyser)谱特性进行分析.结果表明:1 9.4%的云南高原粳稻主栽品种稻米品质达2级标准,12.5%的品种稻米品质达3级标准,其余品种品质均在3级以下.2糙米率、透明度、碱消值、直链淀粉含量和蛋白质含量的平均值达2级标准,精米率和垩白度达3级标准.3差异最大的稻米品质性状和淀粉RVA谱特征值分别是垩白度和消减值,变异系数分别为99.77%和34.32%.4 32个品种聚为6类,其中第Ⅲ类包含的品种最多,占40.6%.5在选出的8个影响云南稻米品质和淀粉RVA谱特性的主成分因子中,第1主成分因子(整精米率和垩白粒率)和第2主成分因子(直链淀粉含量)对稻米品质的累积贡献率为44.6%.高原粳稻品质改良的重点是提高整精米率,降低垩白粒率和选择适宜的直链淀粉含量,同时拓宽亲本的遗传基础.
Eleven grain quality traits and RVA profiles were analyzed by the methods of cluster analysis and phenotypic principal component analysis ,using 32 main j aponica rice cultivars from Yunnan plateau as materials .Of the widely grown japonica rice cultivars from Yunnan plateau ,9.4% and 12.5% met the edible rice grain quality standard ,Grade 2 and Grade 3 ,respectively ,and all the others were below Grade 3 .The mean of brown rice rate ,transparency ,ADV ,amylose content and protein content met the stand‐ard of Grade 2 ,w hile milled rice rate and chalky area met the standard of Grade 3 .Of the grain quality traits and RVA profiles studied ,chalky area and setback value showed the greatest variation ,their varia‐tion coefficients being 99.77% and 34.32% ,respectively .The 32 cultivars were clustered into six groups according to genetic distance ,and 40.6% of them fell into Group III .Of the 8 principal component factors affecting grain quality traits and RVA profiles ,the first principal component factors (head rice rate and chalky rice rate) and the second principal component factor ,amylose content ,gave a cumulative contribu‐tion rate of 46.6% to grain quality .Therefore ,it is important to increase head rice rate ,decrease chalky rice rate and select medium amylose content in Yunnan j aponica rice breeding .At the same time ,it is also necessary to broaden its genetic basis .
出处
《西南大学学报(自然科学版)》
CAS
CSCD
北大核心
2014年第11期34-41,共8页
Journal of Southwest University(Natural Science Edition)
基金
云南省科技攻关资助项目(2012BB013)
云南省技术创新人才培养资助项目(2008PY089)
关键词
云南高原粳稻
稻米品质
淀粉RVA谱特性
聚类分析
主成分分析
j aponica rice of Yunnan Plateau
grain quality
RVA profile
cluster analysis
principal com-ponent analysis