主成分分析法用于厦门西港和香港维多利亚港沉积物样品分类研究

本文利用化学模式识别技术的主成分分析法对厦门西港及香港维多利亚港的１８个沉积物样品进行分类研究。所涉及的变量包括重金属Ｃｕ，Ｐｂ，Ｚｎ，Ｃｄ、有机污染物ＤＤＴ，ＰＡＨ，ＰＣＢ，ＨＣＨ，及碱性磷酸酶活性。正确的分类基于变量的适当选择，即选择代表来源不同的样品之典型特性的变量组合方式。

Application of principal component analysis to sediment sample classincation

Pattern recognition technique was was applied to classify the sediment samples fromXiamen Western Harbour and Victoria Harbour of HOng Kong. In this study the technique ofprincipal component analysis (PCA) of pattern recognition to distinguish the sediment sampleswas adopted. The variables included the heavey metals Cu.Pb. Zn.Cd, the organic pollutantsDDT.PAH .PCB .HCH, and alkaline phosphatase activity. The optimized classification is based onwell selecting the variables. The variables selected, on the other hand, most Often represert thetypical characteristics of the sediment samples collected from the different harbours. The resultsshow that PCA technique may offer the potential strategy in the distinguising classes of the environmental samples rome the different origins.