摘要
研究不同品种枸杞果实功能营养成分的差异,筛选优异品种,为枸杞资源开发利用提供依据。以22种枸杞果实为材料,采用比色法和HPLC对其功能营养成分含量进行分析,并运用主成分分析和聚类分析进行综合评价。结果表明:不同品种枸杞果实功能营养成分存在显著差异,但均富含多糖(4.12~15.49mg·g^(-1))和甜菜碱(1.23~7.36mg·g^(-1));枸杞果实中黄酮类化合物主要为芦丁,含量变化范围在19.35~131.90μg·g^(-1)(‘云南’除外);类胡萝卜素以玉米黄素和叶黄素为主,含少量β-隐黄质、β-胡萝卜素和新黄质,类胡萝卜素总量变化范围在1.22~283.62μg·g^(-1)。主成分分析选取了前4个主成分,累计方差贡献率达到81.79%。聚类分析将22种品种枸杞分为4大类。利用主成分和聚类分析综合评价得出,‘新疆’和‘柱筒’综合品质高,可作为枸杞育种、品质改良及枸杞资源开发利用的原材料。
The objectives of this study were to investigate the differences in nutritional ingredients of wolf- berry fruit among different varieties, to select fine varieties, and to provide references for the development of wolfberry. Twenty-two different wolfberry varieties were used as materials,the functional and nutritional nutrients in the fruit were analyzed by spectrophotometry and HPLC methods. The results showed that there existed significant differences in ingredients among the fruit of different varieties. Generally,wolfberry fruit was rich in polysaccharides（4.12-15. 49 mg ·g^-1）and betaine（1. 23-7. 36 mg ·g^-1）. The predominant flavonoid was identified as rutin （19.35--131.90μg·g^-1 ,except for the cultivar ＇Yunnan＇）. Amongst the individual carotenoids, zeaxanthin and lutein were present in larger amount,while β-cryptoxanthin,β-carotene and neoxanthin were in small amount. Total carotenoid content ranged from 1.22 to 283.62 μg·g^-1. Four main compositions were extracted to carry out principal component analysis,and their accumulative variance contribution was 81.79 %. Cluster analysis of 22 different varieties demonstrated that the wolf berry varieties could be divided into four types. Comprehensive evaluation of wolfberry fruit quality showed that ＇Xinjiang＇ and ＇Zhutong＇ had high quality. They could be used as materials of wolfberry breeding, quality improvement and exploitation and utilization of wolfberry resources.
出处
《西北林学院学报》
CSCD
北大核心
2017年第1期157-164,共8页
Journal of Northwest Forestry University
基金
国家自然科学基金项目(31360191)
宁夏回族自治区农业育种专项(2013NYYZ0101)
关键词
枸杞
功能营养成分
主成分分析
聚类分析
wolfberry
functional nutrition ingredient
principal component analysis
cluster analysis
