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基于主成分和聚类分析的油菜薹营养与感官品质评价 被引量:23

Evaluation of nutrition and sensory quality of flowering Chinese cabbage based on principal component and cluster analysis
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摘要 为了探讨不同品种油菜薹品质的差异,采用主成分分析和聚类分析对9种不同品种油菜薹的感官和营养成分指标(维生素C、可溶性蛋白、可溶性糖、总酚、总黄酮、钙、镁、铁、锌、叶绿素、纤维素和有机酸)进行分析。结果表明,采用主成分分析将12个指标简化为3个主成分,其累积方差贡献率为85.04%,可反映油菜薹品质的绝大部分信息。基于主成分分析的综合评价函数显示,狮山菜薹与白菜薹、红菜薹的品质相近。采用聚类分析可将9个油菜薹品种分为3类,且狮山菜薹与白菜薹品质较为相近。结合感官和营养品质分析,狮山菜薹维生素C、可溶性蛋白、钙含量较高且口感佳,具有较好的推广价值。 To explore the differences in quality of flowering Chinese cabbage varieties,the principal components and cluster analysis were used to determine the sensory and nutritional indicators of nine varieties of flowering Chinese cabbage(including vitamin C,soluble protein,soluble sugar,total phenolics,total flavonoids,calcium,magnesium,iron,zinc,chlorophyll,cellulose and organic acid).Results showed that the 12 indicators were simplified into three principal components by principal component analysis,and the cumulative variance contribution rate was 85.04%,which reflected most of the quality characteristics of flowering Chinese cabbage,and the quality of Shishan flowering Chinese cabbage was equivalent to that of sample eight and sample nine.Nine varieties of flowering Chinese cabbage species were divided into three categories by cluster analysis,and Shishan flowering Chinese cabbage and sample eight were similar in quality.Combined with the analysis of sensory and nutritional quality,contents of vitamin C,soluble protein and calcium in Shishan flowering Chinese cabbage were high and the taste was good,which has good promotion values in the food industry.
作者 黄颖 吴婷 曹伟伟 马朝芝 潘思轶 徐晓云 HUANG Ying;WU Ting;CAO Weiwei;MA Chaozhi;PAN Siyi;XU Xiaoyun(College of Food Sciences and Technology,Huazhong Agricultural University,Wuhan 430070,China;College of Plant Science and Technology,Huazhong Agricultural University,Wuhan 430070,China)
出处 《食品与发酵工业》 CAS CSCD 北大核心 2020年第17期253-258,共6页 Food and Fermentation Industries
基金 湖北省农业科技创新中心农产品加工与综合利用团队(2019-620-000-001-31)。
关键词 菜薹 主成分分析 聚类分析 flowering Chinese cabbage principal component analysis cluster analysis
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