光纤SPR传感器结合多分类器的水体DOM检测  

DOM detection of water based on fiber SPR sensors and multi-classifiers

作　　者：付丽辉 戴峻峰 Fu Lihui;Dai Junfeng(Faculty of Automation,Huaiyin Institute of Technology,Huaian 223003,China;Faculty of Electronic Information Engineering,Huaiyin Institute of Technology,Huaian 223003,China)

机构地区：[1]淮阴工学院自动化学院,淮安223003 [2]淮阴工学院电子信息工程学院,淮安223003

出　　处：《农业工程学报》2022年第22期133-140,共8页Transactions of the Chinese Society of Agricultural Engineering

基　　金：国家自然科学基金项目(62141502);江苏省建设系统科技项目(2019ZD0064);淮安市“1111”工程合作项目(Z413H22507)。

摘　　要：针对单传感器难以完成水体可溶解有机物(Dissolved Organic Matter,DOM)总量与组份的测试问题,该研究提出利用光纤表面等离子共振(Surface Plasma Resonance,SPR)传感器的非特异选择性来构建传感阵列。通过对光纤SPR传感器的交叉敏感性分析,获得多模光纤镀以7种不同厚度金膜的传感器设计方案,膜厚为55~85 nm,使其对不同的DOM组份产生类似味蕾的交叉敏感性响应。利用粒子群算法(ParticleSwarmOptimization,PSO)训练的BP神经网络构建3个分类器:PSO-BP(波长)、PSO-BP(谱宽)、PSO-BP(光强),实现对待测量响应信息的有效提取,并对里运河(A)、洪泽湖(B)、公园景观湖(C)、校园景观湖(D)4种水体的5种DOM组份(酪氨酸类蛋白质、色氨酸类蛋白质、富里酸、溶解性微生物代谢产物、腐殖酸)及其浓度进行预测,在洪泽湖水的色氨酸类蛋白质组份测试试验中,最高正确率可达85%。同时,对多分类器的结构参数进行试验分析,重点考察隐含层节点个数及神经网络结构对DOM组份测试的影响。试验结果表明:隐层节点数取15时可以获得最佳测试效果,通过基于传统神经网络RBF、BP与PSO-BP的比较试验可知,基于PSO-BP的3个分类器在DOM组份测试中的精度最高,对4种水体的色氨酸类蛋白质及溶解性微生物代谢产物组份测试的平均分类精度可达87.50%、86.28%。研究结果为基于光纤SPR传感器及多分类器在DOM组份测试的应用提供依据及新的思路。Dissolved organic material(DOM) has posed adverse impacts on the detection of water quality between different water pollutants. Once the total amount of DOM reaches a critical level, the explosive growth of algae can be induced by eutrophication in the water, leading to a more complicated composition. There is a more serious interference in the detection,as the DOM aggravated during this time. The previous research also shows that the effect of DOM is closely related to the total amount, and the components. It is a high demand to accurately measure the DOM components for effective water quality monitoring. Particularly, the DOM component measurement is highly required to effectively implement, due to the complex organic structure. For this reason, it is difficult for a single sensor to complete the complicated test of the total amount and components of DOM in water. In this study, the fiber sensing array was proposed to detect the DOM components using the non-specific selectivity of the fiber SPR sensor. The cross-sensitivity analysis was carried out to obtain the different SPR sensing arrays using the fiber SPR sensor. A field test was been realized by the SPR sensing array in large-scale water bodies.Particle Swarm Optimization(PSO) was selected to optimize the artificial neural network(ANN). As such, effective predictions were obtained for the five DOM components and their concentrations in four kinds of measured water. The SPR sensors were then prepared with different optimal refractive indices using multimode fiber and gold film with seven thicknesses of 55-85 nm. The optimal refractive index of each sensor was effectively distributed in the range of 1.33 to 1.43,according to the design requirements. Correspondingly, each sensor presented excellent sensitivity and linearity in the best measurement interval. The sensitive crossing-response was achieved in the measurement interval corresponding to other sensors through the wavelength, spectrum width, and light intensity. In terms of the classifier and intellig

关 键 词：传感器 水质 多分类器 交叉敏感性 光纤SPR传感器 DOM组份检测 PSO-BP神经网络 

分 类 号：TP212[自动化与计算机技术—检测技术与自动化装置]