基于太赫兹衰减全反射光谱的水质分析

随着人口的增长和社会的迅速发展，水资源短缺和水污染问题日益严重。水质分类作为水质污染评估工作中的一项重要环节，其意义和作用也更加突出。基于太赫兹衰减全反射（THz-ATR）光谱和模式识别技术，提出了一种水质分析模型。利用太赫兹时域光谱系统和衰减全反射模块测量了纯净水、自来水、河水、海水A和海水B五种水样的太赫兹衰减全反射光谱，通过光学参数提取模型获得0.2～1.0 THz频率范围内五种水样的折射率、吸收系数、介电常数实部和介电常数虚部。利用主成分分析（PCA）对折射率进行降维和特征提取，分别作出样品在第一、二主成分上的二维得分图和前三个主成分上的三维得分图，结果显示，基于折射率的主成分得分图可以明显的区分不同的水样。为了进一步对不同水样进行准确分类，将降维之后的数据输入到支持向量机（SVM）中构建水样分类模型，每种水样随机选取其中的五分之三作为训练集，剩余的数据作为测试集，同时引入网格搜索（GridSearch）、遗传算法（GA）和粒子群（PSO）三种优化算法对支持向量机参数进行优化。结果显示，基于网格搜索算法的支持向量机最优参数c和g分别为1.414 2和2.0，准确率为99.0%；基于遗传算法的支持向量机最优参数c和g分别为1.675 4和5.966 5，准确率为99.5%；基于粒子群算法的支持向量机最优参数c和g分别为3.154 9和12.589，准确率为100%。可以看出，使用不同的优化算法得到的最优参数不同，所构建的支持向量机分类模型都可实现正确的分类，且分类准确率均高达99.0%以上。研究结果表明，利用粒子群优化算法基于折射率构建的PCA-SVM分类模型效果最优，可以准确识别不同水样，为水质分类奠定了基础。

Water Quality Analysis Based on Terahertz Attenuated Total Reflection Technology

With the growth of population and the rapid development of society,the problem of water shortage and water pollution have become increasingly serious.As an important aspect of water pollution assessment,water quality classification has a more prominent significance and role.Based on terahertz attenuated total reflection(THz-ATR)spectrum and pattern recognition technology,a water quality analysis model is proposed in this paper.The terahertz time-domain spectroscopy system and the attenuated total reflection module were used to measure the terahertz attenuated total reflection spectra of five water samples,including pure water,tap water,river water,seawater A and seawater B.The refractive index,absorption coefficient,real and imaginary parts of the dielectric constant of five water samples in the frequency range of 0.2~1.0 THz were obtained using the optical parameter extraction model.Principal Component Analysis(PCA)was applied to conduct refractive index reduction and feature extraction,and two-dimensional score charts of the first and second principal components and three-dimensional score charts of the first three principal components of the samples were made respectively.It can be seen that the principal component score chart based on the index of refraction can clearly distinguish different water samples.In order to further classify different water samples accurately,the data after dimension reduction is input into a support vector machine to construct a water sample classification model.Three-fifths of each water sample is randomly selected as the training set,and the remaining data is used as the test set.At the same time,three optimization algorithms,grid search(GridSearch),genetic algorithm(GA)and particle swarm algorithm(PSO)are introduced to optimize the parameters of the support vector machine.The results show that the optimal parameters c and g of the support vector machine based on the grid search algorithm are 1.4142 and 2.0,respectively,with an accuracy of 99.0%;the optimal parameters c and g of the support vector machine based on the genetic algorithm are 1.6754 and 5.9665,respectively,which are accurate The rate is 99.5%;the optimal parameters c and g of the support vector machine based on particle swarm optimization are 3.1549 and 12.589 respectively,and the accuracy rate is 100%.It can be seen that the optimal parameters obtained by different optimization algorithms are different,and all the SVM classification models constructed can achieve correct classification,and the classification accuracy is above 99.0%.The research results show that the PCA-SVM classification model based on the refractive index constructed by the particle swarm optimization algorithm has the best effect and can accurately identify different water samples,laying a foundation for water quality classification.