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dc.contributor.authorLin Yan
dc.contributor.author林燕
dc.contributor.authorZhu Er-yi
dc.contributor.author朱尔一
dc.contributor.authorXu Xiao-qin
dc.contributor.author徐晓琴
dc.contributor.authorLee Frank S C
dc.contributor.authorWang Xiao-ru
dc.contributor.author王小如
dc.date.accessioned2011-05-29T15:00:32Z
dc.date.available2011-05-29T15:00:32Z
dc.date.issued2007
dc.identifier.citationSPECTROSCOPY AND SPECTRAL ANALYSIS,2007,27(10):2107-2110zh_CN
dc.identifier.issn1000-0593
dc.identifier.urihttp://dx.doi.org/doi:CNKI:SUN:GUAN.0.2007-10-050
dc.identifier.urihttps://dspace.xmu.edu.cn/handle/2288/9252
dc.description.abstractIn the present article the principle and advantages of the method to build classification model by partial least squares are briefly introduced. The method was applied to deal with the seawater data obtained from the primary polluted sea area of Jiaozhou bay and Laizhou bay by GC-MS. The classification models have been built for seawater samples from different contaminated areas; The results indicate that PLS is very suitable for dealing with the problems with the data sets that contain many variables and few samples and have serious co-linearity. Accurate classification models can be built by use of PLS to get the classification information of pollution sources from two or many kinds of polluted seawaters data sets from GC-MS. The cross validation relativities of the model comes to over 0.91. This result is Approving, which can provide a reliable foundation for distinguishing pollution, sources correctly. Moreover, compared with the traditional method, the classification figures constructed by model's y(i) in the article are more clear and intuitive, and can express the model's discrimination effect better.zh_CN
dc.language.isoenzh_CN
dc.publisherBEIJING UNIV PRESSzh_CN
dc.subjectGC-MSzh_CN
dc.subjectpartial least squares(PLS)zh_CN
dc.subjectclassification modelzh_CN
dc.titleStudy on classification model of Seawater samples with different pollution sourceszh_CN
dc.typeArticlezh_CN


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