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主成分和聚类分析在烤烟化学品质分类评价中的应用
引用本文:吕中显,赵铭钦,刘国顺,亚平,何元胜,王继明,代金金.主成分和聚类分析在烤烟化学品质分类评价中的应用[J].河南农业科学,2010(11).
作者姓名:吕中显  赵铭钦  刘国顺  亚平  何元胜  王继明  代金金
作者单位:1. 河南农业大学,国家烟草栽培生理生化研究基地,河南,郑州,450002
2. 云南省临沧市烟草公司,云南,临沧,677000
基金项目:国家烟草专卖局重大科技攻关项目
摘    要:为了对烤烟化学品质进行科学分类评价,应用SPSS软件对39个烤烟样本的10项化学指标进行主成分分析,按累积贡献率提取4个主成分,求得4个主成分的综合得分,并对综合得分进行系统聚类,将39个烟叶样本分为4类。结果表明:第1类综合化学品质最好,其综合得分≥80.08;第2类综合化学品质较好,其综合得分≥13.28;第3类综合化学品质较差,其综合得分≥-28.45;第4类综合化学品质最差,其综合得分≥-85.65。该结果与指数和法相比,总体表现一致,表明主成分分析及聚类分析方法在烤烟化学品质分类评价中可行。

关 键 词:烤烟  分类评价  主成分分析  聚类分析

Application of Principal Components Analysis and Cluster Analysis to Sort and Evaluation of the Flue-cured Tobacco Chemical Quality
L Zhong-xian,ZHAO Ming-qin,LIU Guo-shun,YA Ping,HE Yuan-sheng,WANG Ji-ming,DAI Jin-jin.Application of Principal Components Analysis and Cluster Analysis to Sort and Evaluation of the Flue-cured Tobacco Chemical Quality[J].Journal of Henan Agricultural Sciences,2010(11).
Authors:L Zhong-xian  ZHAO Ming-qin  LIU Guo-shun  YA Ping  HE Yuan-sheng  WANG Ji-ming  DAI Jin-jin
Institution:L(U) Zhong-xian,ZHAO Ming-qin,LIU Guo-shun,YA Ping,HE Yuan-sheng,WANG Ji-ming,DAI Jin-jin
Abstract:In order to assort and evaluate the quality of flue-cured tobacco,the contents of 10 components in 39 flue-cured tobacco samples were analyzed by principal component analysis with SPSS software.Four principal components with accumulative contribution rate were obtained,then the comprehensive scores of 4 principal components were also counted.The 39 samples were clustered with comprehensive scores as a measurement,and they were divided into 4 ranks at last.Rank 1 was of the best quality with comprehensive scores ≥80.08,rank 2 was of better quality with comprehensive scores ≥13.28,rank 3 was of worse quality with comprehensive scores ≥-28.45,rank 4 was of the worst quality with comprehensive scores ≥-85.65.The results showed that it is feasible to evaluate the flue-cured tobacco chemical quality by using principal component analysis and cluster analysis.
Keywords:Flue-cured tobacco  Assorted-evaluation  Principal component analysis  Clusteranalysis
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