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基于主成分与聚类分析综合评价西藏不同产地芜菁的品质
引用本文:于翠翠,陈 锋,马路凯,张文会.基于主成分与聚类分析综合评价西藏不同产地芜菁的品质[J].食品安全质量检测技术,2022,13(19):6312-6319.
作者姓名:于翠翠  陈 锋  马路凯  张文会
作者单位:西藏自治区农牧科学院农产品开发与食品科学研究所,西藏自治区农牧科学院农产品开发与食品科学研究所,仲恺农业工程学院轻工食品学院,西藏自治区农牧科学院农产品开发与食品科学研究所
基金项目:西藏自治区财政专项资金项目(XZNKYSPS-2021-C-001)
摘    要:基于主成分与聚类分析探究西藏不同产地芜菁的品质。方法 以不同产地芜菁为原料, 对其基本营养及功能性成分和抗氧化能力进行测定; 采用相关性分析、主成分分析及聚类分析方法对芜菁品质进行综合评价。结果 分析表明12项指标可简化为5个主成分, 累计方差贡献率76.206%; 结合相关性分析, 从12项品质指标中筛选出5项核心品质指标, 即总酸、黄酮、维生素C、还原糖及1,1-二苯基-2-三硝基苯肼(1,1-diphenyl-2-trinitrophenylhydrazine, DPPH)自由基清除能力; 聚类分析结果显示15个产地芜菁可划分为3类, 各产地综合品质优劣排序为: LS>YD>DZ>SN>SG>LL>DQ>BR>SX>LW>BQ>BB>CY>MK>BS, 其中排序较相近的产地被聚为一类, 表明两种分析方法得出的结果较为一致。结论 该研究可为不同产地芜菁品质评价提供理论依据。

关 键 词:芜菁  品质  主成分分析  聚类分析
收稿时间:2022/8/9 0:00:00
修稿时间:2022/9/28 0:00:00

Comprehensive evaluation of the quality of Brassica rapa L. ssp. rapa from different regions in Tibet based on principal component and cluster analysis
YU Cui-Cui,CHEN Feng,MA Lu-Kai,ZHANG Wen-Hui.Comprehensive evaluation of the quality of Brassica rapa L. ssp. rapa from different regions in Tibet based on principal component and cluster analysis[J].Food Safety and Quality Detection Technology,2022,13(19):6312-6319.
Authors:YU Cui-Cui  CHEN Feng  MA Lu-Kai  ZHANG Wen-Hui
Affiliation:Tibet Institute of Agricultural Products Development and Food Science;China,Tibet Institute of Agricultural Products Development and Food Science;China,Zhongkai University of Agriculture and Engineering,Tibet Institute of Agricultural Products Development and Food Science;China
Abstract:ABSTRACT: Objective To explore the quality of Brassica rapa L. ssp. rapa from different regions in Tibet based on principal component analysis and cluster analysis. Methods Using Brassica rapa L. ssp. rapa from different origins as raw materials, their basic nutritional and functional components and antioxidant capacity were determined. Correlation analysis, principal component analysis and cluster analysis were used to comprehensively evaluate the quality of Brassica rapa L. ssp. rapa. Results The analysis showed that the 12 indicators are simplified into 5 principal components, and the cumulative variance contribution rate was 76.206%; combined with correlation analysis, 5 core quality indicators are selected from the 12 quality indicators, namely total acid, flavonoids, vitamin C, Reducing sugar and 1,1-diphenyl-2-trinitrophenylhydrazine (DPPH) free radical scavenging ability; the results of cluster analysis results showed that Brassica rapa L. ssp. rapa from 15 producing areas could be divided into 3 categories, and the comprehensive quality ranking of each producing area was LS>YD>DZ>SN>SG>LL>DQ> BR>SX>LW>BQ>BB>CY>MK>BS, the production areas with similar rankings were grouped into one category, indicating that the results obtained by 2 kinds of analysis methods were relatively consistent. Conclusion This study can provide a theoretical basis for the quality evaluation of Brassica rapa L. ssp. rapa in different origins.
Keywords:turnip  quality  principal component analysis  cluster analysis
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