基于水质中石油类污染物的强荧光特性, 构建了荧光检测实验系统, 分别以50种不同浓度的汽油、柴油、煤油和机油的水溶液为测量样本, 分析了其荧光特性.由于四者荧光峰位置相似, 很难直接识别, 提出了一种运用神经网络进行模式识别的新方法, 将主成分分析法(PCA)和可拓神经网络(ENN)相结合对输入矢量进行降维并进行分类识别.结果表明, 该方法与ENN和PCA-BP算法相比, 迭代数由265降低到60；识别离差平方和由0.236 5降到0.014 5；识别效率由72.50%提升到96.25%；识别精度可达10-6级别.PCA-ENN算法具有较高的识别精度和识别效率, 同时也可用于水质中其它痕量有机物质的光谱识别.

Based on strong fluorescence property of oil pollutant in water quality, a fluorescence detection system is constructed, gasoline, diesel, kerosene, engine oil with different concentrations in water quality are prepared to analyze their fluorescence characteristics. In order to identify the three oil with the similar fluorescence peaks, the Principal Component Analysis (PCA) combined with Extension Neural Network (ENN) is proposed, which can reduce input vector and identify similar substances. Compare with the ENN and PCA-BP, the results show that the proposed method can make the iteration number dropped from 265 to 60, make the sum of the squares of dispersion decrease from 0.236 5 to 0.014 5, make recognition efficiency increase from 72.50% to 96.25%, and reach the 10-6 level of recognition precision. The proposed method possesses high recognition accuracy and recognition efficiency, which can be used in spectral identification of other trace organic materials in water.