摘要
以山西省栽培较多的18个柿品种为试材,通过果实外观品质性状、营养成分含量、功能性成分含量和抗氧化能力等指标的测定,对其果实品质进行比较与综合评价。结果表明,18个柿品种14个品质指标间差异较大,变异系数都在10%以上,其中1,1-二苯基-2-三硝基苯肼(1,1-diphenyl-2-picrylhydrazyl,DPPH)自由基清除能力、2,2′-联氮-双-3-乙基苯并噻唑啉-6-磺酸(2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid),ABTS)自由基清除能力、单果重、类黄酮含量、铁离子还原能力、总酚含量、单宁含量和V C含量变异系数大于27%以上;主成分分析共提取了4个主成分,累积方差贡献率为83.948%,可以反映原指标的大部分信息,综合品质排名前5的品种为火葫芦、笨盖柿、孝义牛心柿、盖柿和华县白旋柿;聚类分析结果与主成分分析结果基本一致,2种分析方法均可对柿果品质进行综合评价。
Taking 18 persimmon varieties cultivated in Shanxi Province as the test materials,the fruit quality was compared and comprehensively evaluated through the determination of fruit appearance quality traits,nutritional content,functional component content and antioxidant capacity,etc.The result showed that 14 quality indexes of 18 persimmon varieties were significantly different,and the coefficient of variation for these indexes was more than 10%.Among them,the coefficient of variation for DPPH free radical scavenging ability,ABTS free radical scavenging ability,single fruit weight,flavonoid content,Fe-ion reduction ability,total phenol content,tannin content and vitamin C content was more than 27%.Furthermore,four principal components were extracted by principal component analysis,and the cumulative variance contribution rate was 83.948%,which could reflect most information of the original indexes.So the top five varieties with the best comprehensive quality were Huohulu,Bengaishi,Xiaoyiniuxinshi,Gaishi and Huaxianbaixuanshi.The result of cluster analysis was basically consistent with those of principal component analysis,which indicated that both of the two methods could be used to evaluate the quality of persimmon fruit comprehensively.
作者
吕英忠
李卓
张拥兵
梁志宏
LYU Yingzhong;LI Zhuo;ZHANG Yongbing;LIANG Zhihong(Pomology Institute,Shanxi Agricultural University,Taigu 030815,China)
出处
《食品与发酵工业》
CAS
CSCD
北大核心
2020年第18期180-186,共7页
Food and Fermentation Industries
基金
山西省重点研发计划重点项目(201703D211011)
山西省重点研发计划项目(201703D221016-6)。
关键词
柿
果实品质
变异系数
主成分分析
聚类分析
综合评价
persimmon
fruit quality
coefficient of variation
principal component analysis
cluster analysis
comprehensive evaluation
