基于紫外光谱的水体硝酸盐浓度混合预测模型研究  

Study on Mixed Prediction Model of Nitrate Concentration in Water Based on Ultraviolet Spectroscopy

作　　者：陈颖[1] 何磊 崔行宁 韩帅涛[1] 朱奇光[2] 翟应俭 李少华 CHEN Ying;HE Lei;CUI Xing-ning;HAN Shuai-tao;ZHU Qi-guang;ZHAI Ying-jian;LI Shao-hua(Hebei Province Key Laboratory of Test/Measurement Technology and Instrument,School of Electrical Engineering,Yanshan University,Qinhuangdao 066004,China;Key Laboratory for Special Fiber and Fiber Sensor of Hebei Province,Yanshan University,Qinhuangdao 066004, China;Hebei Sailhero Environmental Protection Hi-tech Co.,Ltd.,Shijiazhuang 050000,China)

机构地区：[1]燕山大学电气工程学院河北省测试计量技术及仪器重点实验室,河北秦皇岛066004 [2]燕山大学信息科学与工程学院河北省特种光纤与光纤传感器重点实验室,河北秦皇岛066004 [3]河北先河环保科技股份有限公司,河北石家庄050000

出　　处：《光谱学与光谱分析》2019年第5期1489-1494,共6页Spectroscopy and Spectral Analysis

基　　金：国家自然科学基金项目(61201112;61475133);河北省自然科学基金项目(F2016203188);河北省普通高校学校青年拔尖人才计划项目(BJ2014056)资助

摘　　要：水体中的硝酸盐浓度过高不仅会造成水环境污染而且会对人类身体健康造成很大威胁,传统的检测硝酸盐的方法检测时间长且操作复杂。针对水体中硝酸盐氮难以快速在线检测的问题,基于紫外吸收光谱,提出了一种混合预测模型结合光谱积分快速定量检测水体中硝酸盐浓度的方法。混合预测模型为低浓度样本建立的双波长法预测模型与高浓度样本建立的偏最小二乘支持向量机(LS-SVM)预测模型数据融合之后的模型。按照合适的浓度梯度配备了19组硝酸盐氮标准溶液,通过实验测得不同浓度硝酸盐氮样本的光谱数据。首先基于双波长法对所有样本进行回归分析,按照A=A_(220)-2A_(275)计算不同实验样本的吸光度A,其中A_(220)和A_(275)是220和275 nm处样本的吸光度,将吸光度A与样本浓度值进行线性回归,拟合出样本浓度的预测值。结果显示当样本浓度较小时,相关性很好,r为0.997 4,随着实验样本浓度的上升,曲线发生严重的非线性漂移,因此双波长法只适合低浓度样本预测模型的建立。对于高浓度样本,光谱重叠严重,适合建立非线性的预测模型,支持向量机(SVM)与LS-SVM都适合小样本的非线性数据建模, LS-SVM预测精度稍高,运算速度稍快。通过对所有的实验样本进行全波长光谱积分,比较相邻样本光谱积分的变化率可以筛选出样本的临界浓度值, 4 mg·L^(-1)的硝酸盐样本积分值前后变化率最大,因此选择4 mg·L^(-1)作为临界浓度值较为合适。浓度高于4 mg·L^(-1)的实验样本建立LS-SVM预测模型,通过交叉验证的方法选择出合适的参数,正则化参数γ=50,核函数选择高斯核,核函数宽度σ~2=0.36,训练样本之后进行回归;其余样本建立双波长法预测模型,最后进行两种模型的数据融合,形成从低浓度到高浓度的水体中硝酸盐浓度的检测。为了验证混合预测模型的预测精度,另外建立了SVM, LS-SVM,�High concentration of nitrates in water will not only cause water environment pollution but also pose a great threat to human health.The traditional methods for detecting nitrates have a long detection time and are complex to operate.In view of the difficulty in rapid on-line detection of nitrate nitrogen in water,a method combined a mixed prediction model with spectral integration was proposed to rapidly detect nitrate concentration in water based on ultraviolet absorption spectroscopy.The mixed prediction model is a model after data fusion of the dual wavelength prediction model established by low-concentration samples and the partial least-squares support vector machine (LS-SVM) prediction model based on the high concentration samples.According to the appropriate concentration gradient,19 sets of nitrate nitrogen standard solution were equipped,and the spectral data of nitrate nitrogen samples of different concentrations were measured by experiment.First,Regression analysis was performed on all samples based on the dual wavelength method.Absorbance A was calculated for different experimental samples according to A=A220 -2A275,where A220 and A275 were the absorbance of the samples at220 and275 nm.The values were linearly regressed to fit the predicted values of the sample concentrations.The results showed that when the sample concentration is small,the correlation is very good,and r is 0.997 4.The two-wavelength method is only suitable for the establishment of low-concentration samples prediction model with a serious nonlinear drift in the rising curve of the experimental samples concentration.For high-concentration samples,spectral overlap is severe and it is suitable for establishing nonlinear prediction models.Both support vector machine (SVM) and partial LS-SVM are suitable for nonlinear data modeling of small samples.The LS-SVM has a slightly higher prediction accuracy and a slightly faster operating speed.By performing full-wavelength spectral integration on all experimental samples and comparing the rate

关 键 词：紫外光谱 混合预测 双波长 LS-SVM 数据融合 在线监测 

分 类 号：O433.4[机械工程—光学工程]