光电式液滴分析技术定量检测水中的盐度  

Quantitative detection on salinity of water by photoelectric droplet analysis technology

作　　者：冯国红[1] 裘祖荣[2] 杨慧敏[1] 

机构地区：[1]东北林业大学森林持续经营与环境生物工程黑龙江省重点实验室,黑龙江哈尔滨150040 [2]天津大学精密测试技术及仪器国家重点实验室,天津300072

出　　处：《传感器与微系统》2013年第7期123-125,135,共4页Transducer and Microsystem Technologies

基　　金：中央高校基本科研业务费专项资金资助(DL12BB27)

摘　　要：基于光纤—电容液滴分析方法的原理,研究了盐度的检测。由20种不同盐度溶液的实验结果得出:随着盐度的增加,光纤信号值和液滴指纹图曲线下的面积不断增大。利用1—12—1三层BP神经网络结构,分别以光纤信号的平均值和液滴指纹图曲线下的面积为输入向量,进行网络训练,并利用训练好的网络分别进行测试。比较测试结果得出:利用光纤信号的平均值作为BP网络的输入向量进行盐度检测效果较好,最大检测误差为0.14%。Based on the principle of fiber-capacitance drop analysis method, salinity detection is studied. The experimental results of 20 kinds of solution with different salinity show that the optical fiber signal value and area under liquid droplets fingerprints diagram curves increases with increase of salinity. Using 1-12-1 three-layer BP neural network structure, and average value of the optical fiber signal and the area under the curve of the liquid droplets fingerprints as input vectors respectively, and BP neural network is trained and uses the trained network to test respectively. It is derived by comparison of test results, salinity detection effect using mean value of optical signal as input vector of the BP networks is better than the others, and the maximum detecting error is 0.14 %.

关 键 词：光纤—电容液滴分析 盐度 液滴指纹图 BP神经网络 

分 类 号：TP206.1[自动化与计算机技术—检测技术与自动化装置]