主成分分析联合Fisher判别在紫外-可见光谱法水质检测中的应用

水质类型的判别是实现光谱法水质参数准确检测的重要前提。针对直接光谱法水质检测系统采集的光谱数据信息冗余较大的问题, 利用主成分分析消除信息指标间的相关性,实现光谱数据的降维和特征信息提取。采集某化工厂和某溪水水样的紫外-可见光谱数据, 利用主成分分析联合Fisher判别的方法建立判别模型,以12组水样光谱数据作为训练样本,6组作为测试样本,对模型的判别能力进行论证和检验,并与传统的Fisher判别模型进行对比实验。实验结果表明,利用主成分分析联合Fisher判别模型可以有效消除信息冗余带来的影响,相比传统的Fisher判别模型具有分类精度高、回代误判率为零、计算时间短等优点,计算时间由传统Fisher判别方法的0.6733 s减少到0.6012 s。该方法为直接光谱法水质类型判别工程实用化提供了一种高效手段。

Abstract

The identification of water quality is an important prerequisite for accurate spectroscopic detection of water quality parameters. Aiming at the problem of large redundancy of spectral data collected by direct spectrum water quality detection system, the principal component analysis is used to eliminate the correlation of information indexes, and the spectral data is reduced and the feature information is extracted. The UV-Vis spectra of water from a chemical plant and a stream were collected. The discriminant model was established by using the method of principal component analysis and Fisher discriminant. First, 12 sets of water samples were used as training samples and 6 groups as test samples. Then, the discriminant ability of the model was demonstrated and tested, and compared with the traditional Fisher discriminant model. Finally, The experimental results show that the joint Fisher discriminant model can effectively eliminate the influence of information redundancy. Compared with the traditional Fisher discriminant model, it has the advantages of high classification precision, zero return error rate and short calculation time. The calculation time is reduced from 0.6733 s to 0.6012 s. This method provides an efficient means for practical application of direct spectrum method to determine the water quality.

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