摘要
测定35个甜玉米品种的各项指标,利用数理统计分析方法,明确各指标的水平、变化趋势和定量关系,筛选出适宜的甜玉米品质评价指标并进行分类。结果表明,甜玉米品种间生育期、果穗长度、果穗粗、行粒数和鲜百粒质量差异均较小,变异系数分别为7.7%、6.6%、4.5%、7.8%和9.5%;株高、穗位高、可溶性固形物含量和皮渣率差异均很大,变异系数分别高达10.9%、19.9%、30.6%和25.0%;9项品质指标均服从正态分布;株高和穗位高呈极显著正相关(0.847 3),并存在极显著的线性函数变化趋势,其决定系数为0.985 7;确定甜玉米品质评价指标由生育期、鲜百粒质量和可溶性固形物(分别代表熟期指标、适口性指标和甜度指标)构成,利用这3项指标可将35个甜玉米品种分为3类。
The various indexes of 35 sweet corn varieties were measured, and the level, change trend and quantitative relationship of each index were clarified using mathematical statistical analysis methods, and suitable sweet corn quality evaluation indexes were selected and classified. The results showed that the differences in growth period, ear length, ear thickness, grain numbers of per row and quality of fresh 100 grains among sweet corn varieties were small, and the coefficients of variation were 7.7%, 6.6%, 4.5%, 7.8% and9.5%, respectively. The differences in plant height, ear height, soluble solids and slag rate were all very big, with coefficients of variation as high as 10.9%, 19.9%, 30.6% and 25.0%, respectively. The nine quality indicators all obeyed the normal distribution. The plant height and panicle height showed a very significant positive correlation, with a correlation coefficient of 0.847 3, and there was a very significant linear function trend, and its determining coefficient R^2 was 0.985 7. The evaluation indexes of sweet corn quality were determined to be composed of growth period, fresh 100 grain quality and soluble solids(representing maturity index, palatability index and sweetness index respectively). By using these three indexes, 35 sweet corn varieties could be divided into three categories.
作者
张雪彪
闫建宾
王瑞钢
张沛敏
王俊花
庞旭
梁海英
ZHANG Xuebiao;YAN Jianbin;WANG Ruigang;ZHANG Peimin;WANG Junhua;PANG Xu;LIANG Haiying(Institute of Sorghum,Shanxi Academy of Agricultural Sciences,Jinzhong 030600,China)
出处
《山西农业科学》
2020年第2期157-160,171,共5页
Journal of Shanxi Agricultural Sciences
基金
山西省农业科学院生物育种工程项目（17yzgc027）
关键词
甜玉米
品质
评价指标
聚类分析
因子分析
回归分析
sweet corn
quality
evaluation index
cluster analysis
factor analysis
regression analysis
