紫外-可见吸收光谱结合化学计量学算法的水体总有机碳浓度快速检测

Rapid Detection of Total Organic Carbon Concentration in Water Using UV-Vis Absorption Spectra Combined With Chemometric Algorithms

总有机碳(TOC)指悬浮或溶解于水中有机物的含碳总量,是以单位体积水体中含碳的质量来表示水中有机物的浓度,通过总有机碳可以更全面反映水中有机污染物的总量。总有机碳的监测能够推动我国实现“碳达峰”和“碳中和”的目标,也对我国海洋地球碳循环的研究具有重要的意义。目前,国标法测量水质TOC主要采用高温催化氧化法和湿法氧化法,这两种方法虽测量准确、可解释性强,但都具有测试方法复杂、测量时间长、易产生二次污染、人力物力消耗巨大等缺点,且仅能在实验室内完成,无法进行TOC的原位在线测量。因此发展水质TOC快速、实时、在线监测技术具有重要意义。为此,建立了TOC标准溶液浓度基于紫外吸收光谱的单波长检测模型,针对物质种类更为复杂的真实水样分别使用ACO-PLS和SPA算法筛选特征波长,对比S-G平滑处理、最小最大归一化、标准正态变换(SNV)、消除常数偏移量、导数校正等多种光谱预处理方法的效果,经过粒子群算法优化的最小二乘支持向量机算法(PSO-LSSVM)建立快速检测模型。结果表明,选取不同数量特征波长,经SNV算法预处理后的建模效果普遍优于其他预处理方法;选用不同预处理算法,最佳特征波长数量普遍为50个,过多或过少的波长数量会使建模精度降低;最佳建模参数为选用SNV预处理方法,经ACO-PLS算法筛选50个特征波长组合并利用PSO-LSSVM算法建模,最优模型结果训练集Rc达到0.984 3, RMSEC为0.457 4,验证集Rp为0.974 5, RMSEP为0.481 1。将最优光谱检测模型应用于新采集水样,预测结果较为准确,具有一定鲁棒性。表明ACO-PLS算法可以有效选取特征波长,结合PSO-LSSVM算法可以实现利用紫外-可见吸收光谱对水体中TOC的测量,为水体TOC含量快速检测提供一种快速、无污染的测量方案,给相应传感器的研发提供了科学支持。

Total Organic Carbon(TOC)refers to the total amount of carbon contained in suspended or dissolved organic matter in water.It represents the concentration of organic matter in water by the mass of carbon contained in a unit volume of water.Total organic carbon can reflect more comprehensively the total amount of organic pollutants in the water.Monitoring total organic carbon can promote China to achieve the goals of“carbon peaking”and“carbon neutrality”,and it also has great significant meaning to the study of China s ocean earth carbon cycle.The national standard method for measuring water quality TOC mainly adopts the high-temperature catalytic oxidation method or wet oxidation method.Although the above two methods are accurate in measurement and have a high interpretability,they have disadvantages,such as complicated test methods,long measurement time,secondary pollution,and huge workforce and material resources waste.These methods can only be completed in the laboratory,so it is impossible to realize the in-situ online measurement of TOC.Therefore,it is-greatly significant for us to study the method of rapid and in-situ monitoring of TOC in water.This paper has established a single wavelength concentration detection model for TOC standard solution based on UV absorption spectra.Duo to more complex substance content of real water samples,ACO-PLS and SPA algorithms were used to select characteristic wavelengths and the performance of different spectral pretreatment methods,including S-G smoothing,min-max normalization,Standard Normal Variation(SNV),elimination of constant offset,derivative correction,were compared.The fast detection model of real water samples based on spectral absorption was established the least squares support vector machine algorithm(LSSVM)optimized by particle swarm optimization(PSO).The experimental results show that the modeling effect of SNV algorithm pretreatment is generally better than that of other pretreatment methods when a different numbers of characteristic wavelengths are selected.Moreover,the optimal number of characteristic wavelengths is generally 50 with different preprocessing algorithms because too many or too few wavelengths will reduce the modeling accuracy.The optimal modeling parameters are the SNV preprocessing method with 50 characteristic wavelength combinations selected by the ACO-PLS algorithm.The optimal PSO-LSSVM model result shows R c=0.9843,RMSEC=0.4574,R p=0.9745,RMSEP=0.4811.The optimal TOC detection was successfully applied to newly collected water,demonstrating the robustness of the model.ACO-PLS can effectively select the characteristic wavelength combination.Thus,the rapid determination of TOC in water quality based on UV-Vis absorption spectroscopy can be realized with the PSO-LSSVM algorithm,which provides a fast and pollution-free measurement scheme for TOC in water and provides theoretical support for the development of TOC sensors.