基于表面增强拉曼光谱技术和GWO-SVR算法快速实现水中总氮总磷含量检测

提出了一种将表面增强拉曼光谱技术(SERS)和基于灰狼优化(GWO)算法的支持向量回归(SVR)相结合快速定量检测水中总氮(TN)、总磷(TP)含量的定量分析方法.传统的TN、TP检测方法不但过程繁杂,实验环境要求高,而且耗时较长,不能实现快速检测.而SERS技术操作简单,耗时短,将其与GWO-SVR算法相结合可以实现...

Rapidly Detection of Total Nitrogen and Phosphorus Content in Water by Surface Enhanced Raman Spectroscopy and GWO-SVR Algorithm

A new quantitative analysis method was proposed, which combined surface-enhanced Raman spectroscopy (SERS) and support vector regression (SVR) based on Grey Wolf Optimization (GWO) algorithm to quickly and quantitatively detect the total nitrogen (TN) and total phosphorus (TP) content in water. The traditional TN and TP detection methods are complicated in process and time-consuming in the experimental environment. Therefore, rapid detection cannot be realized. However, SERS technology is easy to operate and time consuming, so combining it with the GWO-SVR algorithm can realize fast and accurate detection. With laboratory silver sol as the Raman enhanced substrate and TN ,TP solutions with different concentration gradients as the research objects.TN and TP sample solutions were allocated to 26 and 23 groups respectively, in which 8 groups were selected as the test set for TN solution, 7 groups as the test set for TP solution, and the remaining sample solutions as the training set. The optimal experimental scheme was determined according to the different volume ratios of the tested solution and the silver sol. TN ,TP were mixed with silver sol for 1∶1, 1∶2, 1∶3, 2∶1, 3∶1, respectively. The results showed that the enhancement effect was the best when the solution and the silver sol were mixed at a ratio of 2∶1. Spectral information was collected, and characteristic peaks were assigned. The original spectral data were preprocessed by dark current deduction, background deduction (baseline correction) and smoothing processing. The spectral analysis results show that the intensity of characteristic spectral peak varies with the concentration of solution due to the difference of functional group concentration in different concentrations of solution. The GWO-SVR quantitative analysis model was established by taking the spectral characteristic peak strength and solution concentration of the training set sample as the input and output values of the regression prediction model. Themodel’s prediction ability was analyzed by correlation coefficient (r) and mean square error (MSE) of the sample solution of the test set, and the GWO-SVR model was compared with the other two models. The results showed that the GWO-SVR model predicted the TN solution with a correlation coefficient of 0.9995 and a mean square error of 0.005 8, which were higher than the 0.993 8, 0.052 7 and 0.998 3, 0.022 7 of the artificial bee colony algorithm optimization support vector regression (ABC-SVR) and particle swarm optimization neural network (PSO-BP).The correlation coefficient of TP solution prediction was 0.998 5, and the mean square error was 0.037 6, which was also higher than the other two models. Moreover, compared with ABC-SVR and PSO-BP models, GWO-SVR has fewer input parameters, faster convergence speed, and easier to find the optimal global solution. Therefore, this method can realize the rapid and accurate detection of TN and TP content in water and provides a new method for water quality detection.