智能手机用于检测水体中几种典型无机污染物的研究

发布时间：2018-05-08 18:10

本文选题：智能手机比色法 + 智能手机光谱法　；参考：《西南交通大学》2017年硕士论文

【摘要】：在环境监测领域,传统的水质监测都需要经过冗长的采样、送样和检测等过程。随着环境污染的日趋严重,迫切需要开发能够实现在线快速监测的新方法。现场快速检测(Point of care,POC)技术具有仪器便携、操作简单、成本低廉等优点,已经越来越受到研究学者们的广泛关注。本文讨论了一种基于智能手机的快速检测方法,该方法能够为水体中众多无机污染物提供即时检测,满足了现场快速检测的要求。本论文比较了两种基于智能手机的检测方法(即智能手机比色法和智能手机光谱法)以及两种图像处理方法(即RGB颜色模型和灰度模型)。首先设计并制作了两种检测方法所需的装置外接于智能手机,智能手机光谱法的装置类似于简易的分光光度计。随后分别使用智能手机比色法和光谱法对6组染料溶液进行测试,运用RGB模型和灰度模型对采集的图像进行颜色量化分析,建立颜色与浓度间的关系。筛选出灵敏度最高的智能手机检测方法和颜色量化模型,灵敏度通过检出限判定。根据实验结果得出了智能手机光谱法与RGB颜色量化模型的检测系统具有最高灵敏度。然后,根据上述筛选出的智能手机检测系统对几种典型无机污染物(Cu~(2+)、Ni~(2+)、氨氮和正磷酸盐)进行检测,四种污染物的检出限分别为Cu~(2+):0.02 mg/L、Ni~(2+):0.27mg/L、氨氮:0.024mg/L、正磷酸盐:0.018mg/L;检测范围分别为 Cu~(2+):0～4.8mg/L、Ni~(2+):0～6mg/L、氨氮:0～3.3mg/L、正磷酸盐:0～4.5mg/L;相对标准偏差Cu~(2+):1.8%、Ni~(2+):2.5%、氨氮:2.6%、正磷酸盐:2.9%。分别使用本方法与国标法对未知水样进行检测,两种检测方法的线性拟合曲线均有较好的线性关系;检测结果的相对误差分别为Cu~(2+):5.4%、Ni~(2+):8.9%、氨氮:11.8%、正磷酸盐:6.6%。故本论文所研究的智能手机检测技术具有较强可行性和准确性,在现场快速检测领域拥有极大的应用前景。
[Abstract]:In the field of environmental monitoring, traditional water quality monitoring requires lengthy sampling, sample delivery and testing. With the increasingly serious environmental pollution, there is an urgent need to develop a new method to realize on-line rapid monitoring. Because of the advantages of portable instrument, simple operation, low cost and so on, spot rapid detection of Point of care (POC) technology has attracted more and more attention of researchers. A rapid detection method based on smart phone is discussed in this paper. This method can provide real-time detection for many inorganic pollutants in water and meet the requirements of field rapid detection. In this paper, two detection methods based on smart phone (smart phone colorimetric method and smart phone spectrum method) and two image processing methods (RGB color model and gray scale model) are compared. In this paper, two kinds of devices are designed and manufactured, which are connected to smart phone. The device of spectrum method is similar to a simple spectrophotometer. Then six groups of dye solutions were tested by smart phone colorimetry and spectral method respectively. The color quantification analysis of collected images was carried out using RGB model and gray model to establish the relationship between color and concentration. The detection method and color quantization model of smart phone with the highest sensitivity are selected, and the sensitivity is determined by detection limit. According to the experimental results, the detection system of smart phone spectrum method and RGB color quantization model has the highest sensitivity. Then, according to the selected smart phone detection system mentioned above, several typical inorganic pollutants, such as Cucurbitum, nitride, ammonia nitrogen and orthophosphate, were detected. The detection limits of the four pollutants are: Cu~(2: 0.02 mg / L / L: 0.27 mg / L, ammonia-nitrogen 0.024 mg / L, orthophosphate: 0.018 mg / L; Cu~(2: 04.8 mg / L / L / L; ammonia: 03.3 mg / L; phosphate: 04.5 mg / L; relative standard deviation: 1.8mg / L; ammonia-nitrogen 2.6mg / L; normal phosphate 2.9g / L; relative standard deviation: 1.8mg / L; NH _ 3-N = 2.6mg / L; By using this method and the national standard method to detect the unknown water samples, the linear fitting curves of the two methods have good linear relationship, and the relative errors of the detection results are as follows: Cu~(2: 5. 4 and 2: 8; ammonia nitrogen: 11.8; orthophosphate: 6.6. Therefore, the smart phone detection technology studied in this paper has strong feasibility and accuracy, and has a great application prospect in the field of field rapid detection.
【学位授予单位】：西南交通大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：X832

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