基于太赫兹光谱的水体重金属检测

Detection of heavy metals in water based on terahertz spectrum

[目的]基于太赫兹时域光谱技术,对水体中汞(Hg)、镉(Cd)、铜(Cu)3种重金属进行检测,旨在太赫兹光谱范围内找出3种重金属的特征频率点,同时为构建水体中3种重金属类别和浓度识别以及含量的预测模型提供方法借鉴。[方法]分别配置不同浓度的Hg、Cd、Cu重金属溶液,用太赫兹光谱衰减反射模式采集样品的时域数据,通过离散余弦变换(discrete cosine transform,DCT)、标准正态变换(standard normal transformation,SNV)与二阶导数(second derivative,SD)去噪,主成分分析(principal component analysis,PCA)、多维度缩放(multiple dimension scaling,MDS)与线性判别分析(linear discriminant analysis,LDA)降维,再通过随机森林(random forest,RF)、k邻近算法(k-nearest neighbor,KNN)和概率神经网络(probabilistic neural network,PNN)进行重金属类别与浓度的检测建模,最后采用最小二乘支持向量机(least squares support vector machines,LS-SVM)与反向传播神经网络(back propagation neural network,BPNN)进行浓度预测建模。[结果]一定浓度范围内Hg和Cd溶液的吸收系数谱分别在1.7 THz与1.2 THz处有明显吸收峰变化,未发现Cu溶液吸收系数谱随浓度改变的规律。PNN与KNN模型可对3种重金属水体进行准确检测,构建的PCA-PNN模型可分别对Hg、Cd和Cu溶液进行浓度识别,准确率分别为99.45%、95.93%和99.25%。构建的DCT-LDA-BPNN模型可用于溶液中的Hg、Cd和Cu这3种重金属含量预测,决定系数分别为0.996、0.986和0.999,均方误差分别为0.008、0.026和2.164。[结论]本试验证明太赫兹光谱对不同浓度的Hg、Cd、Cu溶液有较好的定性与定量分析能力,能为水体重金属检测提供重要参考。

[Objectives]Based on terahertz time domain spectroscopy,three heavy metals of mercury(Hg),cadmium(Cd)and copper(Cu)in water were detected,aiming to find out the characteristic frequency points of three heavy metals within the terahertz spectrum,and provide a method reference for the construction of the classification and concentration identification of three heavy metals in water and the prediction model of the concentration content.[Methods]Hg,Cd and Cu heavy metal solutions of different concentrations were respectively configured,and time domain data of the samples were collected using terahertz spectral attenuation reflection mode,and denoising was performed by discrete cosine transform(DCT),standard normal transformation(SNV)and second derivative(SD).Dimensionality reduction was performed by principal component analysis(PCA),multiple dimension scaling(MDS)and linear discriminant analysis(LDA).Then the detection modeling of heavy metal categories and concentrations were carried out by random forest(RF),k-nearest neighbor(KNN)and probabilistic neural network(PNN).The least squares support vector machines(LSSVM)and back propagation neural network(BPNN)were adopted for concentration prediction modeling.[Results]The results showed that at 1.7 THz and 1.2 THz,the absorption coefficient spectra of Hg and Cd in a certain heavy metal concentration range had obvious peak changes,respectively,while the absorption coefficient spectra of Cu solution in the tested terahertz range with the concentration change rule was not found.The PNN and KNN models could accurately detect and identify three heavy metals in water.The PCA-PNN model could identify the concentrations of Hg,Cd and Cu solutions with the accuracy of 99.45%,95.93%and 99.25%,respectively.The DCT-LDA-BPNN model could be used to predict the contents of Hg,Cd and Cu in solution with the determination coefficients of 0.996,0.986 and 0.999,respectively,and the mean square errors of 0.008,0.026 and 2.164,respectively.[Conclusions]This experiment proved that terahertz spectrum had good qualitative and quantitative analysis ability for Hg,Cd and Cu solutions with different concentrations,which could provide important reference for the detection of heavy metals in water.