本文選題：智能手機(jī)比色法 + 智能手機(jī)光譜法��； 參考：《西南交通大學(xué)》2017年碩士論文

【摘要】：在環(huán)境監(jiān)測(cè)領(lǐng)域,傳統(tǒng)的水質(zhì)監(jiān)測(cè)都需要經(jīng)過冗長(zhǎng)的采樣、送樣和檢測(cè)等過程。隨著環(huán)境污染的日趨嚴(yán)重,迫切需要開發(fā)能夠?qū)崿F(xiàn)在線快速監(jiān)測(cè)的新方法�，F(xiàn)場(chǎng)快速檢測(cè)(Point of care,POC)技術(shù)具有儀器便攜、操作簡(jiǎn)單、成本低廉等優(yōu)點(diǎn),已經(jīng)越來越受到研究學(xué)者們的廣泛關(guān)注。本文討論了一種基于智能手機(jī)的快速檢測(cè)方法,該方法能夠?yàn)樗w中眾多無機(jī)污染物提供即時(shí)檢測(cè),滿足了現(xiàn)場(chǎng)快速檢測(cè)的要求。本論文比較了兩種基于智能手機(jī)的檢測(cè)方法(即智能手機(jī)比色法和智能手機(jī)光譜法)以及兩種圖像處理方法(即RGB顏色模型和灰度模型)。首先設(shè)計(jì)并制作了兩種檢測(cè)方法所需的裝置外接于智能手機(jī),智能手機(jī)光譜法的裝置類似于簡(jiǎn)易的分光光度計(jì)。隨后分別使用智能手機(jī)比色法和光譜法對(duì)6組染料溶液進(jìn)行測(cè)試,運(yùn)用RGB模型和灰度模型對(duì)采集的圖像進(jìn)行顏色量化分析,建立顏色與濃度間的關(guān)系。篩選出靈敏度最高的智能手機(jī)檢測(cè)方法和顏色量化模型,靈敏度通過檢出限判定。根據(jù)實(shí)驗(yàn)結(jié)果得出了智能手機(jī)光譜法與RGB顏色量化模型的檢測(cè)系統(tǒng)具有最高靈敏度。然后,根據(jù)上述篩選出的智能手機(jī)檢測(cè)系統(tǒng)對(duì)幾種典型無機(jī)污染物(Cu~(2+)、Ni~(2+)、氨氮和正磷酸鹽)進(jìn)行檢測(cè),四種污染物的檢出限分別為Cu~(2+):0.02 mg/L、Ni~(2+):0.27mg/L、氨氮:0.024mg/L、正磷酸鹽:0.018mg/L;檢測(cè)范圍分別為 Cu~(2+):0～4.8mg/L、Ni~(2+):0～6mg/L、氨氮:0～3.3mg/L、正磷酸鹽:0～4.5mg/L;相對(duì)標(biāo)準(zhǔn)偏差Cu~(2+):1.8%、Ni~(2+):2.5%、氨氮:2.6%、正磷酸鹽:2.9%。分別使用本方法與國(guó)標(biāo)法對(duì)未知水樣進(jìn)行檢測(cè),兩種檢測(cè)方法的線性擬合曲線均有較好的線性關(guān)系;檢測(cè)結(jié)果的相對(duì)誤差分別為Cu~(2+):5.4%、Ni~(2+):8.9%、氨氮:11.8%、正磷酸鹽:6.6%。故本論文所研究的智能手機(jī)檢測(cè)技術(shù)具有較強(qiáng)可行性和準(zhǔn)確性,在現(xiàn)場(chǎng)快速檢測(cè)領(lǐng)域擁有極大的應(yīng)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.
【學(xué)位授予單位】：西南交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：X832

【參考文獻(xiàn)】

相關(guān)期刊論文 前2條

1 劉廈;劉暢;李楠;徐向東;;智能手機(jī)應(yīng)用于便攜式檢測(cè)技術(shù)的研究進(jìn)展[J];分析試驗(yàn)室;2017年01期

2 代仕均;張新申;;流動(dòng)注射-分光光度法分析水體中的痕量銅[J];皮革科學(xué)與工程;2011年01期

，

本文編號(hào)：1862454