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研究生：	林凱文 Lin, Kai-Wen
論文名稱：	輕便型免疫分析系統應用於幽門螺旋桿菌檢測之研究 Development of Compact Immunoassay System for Helicobacter Pylori Detection
指導教授：	賴槿峰 Lai, Chin-Feng
學位類別：	碩士 Master
系所名稱：	工學院 - 工程科學系 Department of Engineering Science
論文出版年：	2019
畢業學年度：	107
語文別：	中文
論文頁數：	111
中文關鍵詞：	幽門螺旋桿菌、免疫分析、影像處理、快速響應矩陣碼、藍芽傳輸、校正光源
外文關鍵詞：	Detection system, QR Code, Immunoassay, Image processing, Bluetooth transmission, Optical calibration
相關次數：	點閱：65 下載：1
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本研究成功開發出具有高靈敏度、高線性度及穩定度之輕便型免疫分析系統，將免疫呈色反應結果作為檢測標的，使免疫呈色反應偵測於定量分析上更加精確。免疫分析系統包含硬體及軟體兩部分，硬體方面開發出一台判讀系統外殼主體，裝載Raspberry Pi Zero W開發板、Universal Serial Bus (USB)攝影機、Light-emitting diode (LED)光源、Liquid Crystal Display (LCD)觸控式液晶顯示、Real-Time Clock (RTC)模組、導光板及光學暗房，並製作出檢測卡匣載台、校正色卡及快速響應矩陣圖碼(Quick Response Code, QR Code)載台。軟體部分使用Python整合模組OpenCV、NumPy、Matplotlib、Zbar及SQLite，並撰寫出偏光校正程式、QR Code解碼、影像處理、資料庫、藍芽傳輸與使用者介面之程式。由實驗結果可發現，本研究所開發之免疫分析系統應用於幽門螺旋桿菌之檢測試劑量測下，正確讀取使用者輸入檢測範圍內之Control、Test線(C、T線)位置，並建立出高線性度之濃度與灰階值擬合曲線，作為量化分析依據，在不同幽門螺旋桿菌濃度偵測下，該曲線之R square值可達0.9965並且在重複實驗下仍可保持良好的精準度(變異係數<15 %)。

The research has successfully developed a highly sensitive and stable compact immunoassay system for the detection of Helicobacter pylori (HPY). Through immunochromatography, the results of the detection device show an excellent correlation of readout with the concentration of HPY. The system is composed of both hardware and software. The detection device is designed by SolidWorks. The software, including the main program, optical calibration, QR Code decoding, and image processing is compiled by Python. After the integral verification of the system, the compact immunoassay system successfully detected the HPY concentration from 0 CFU/mL to 20000 CFU/mL.

摘要	I
Extended Abstract	II
誌謝	VI
目錄	VII
圖目錄	XIII
表目錄	XVII
縮寫表	XIX
第一章 緒論	1
1-1 研究背景	1
1-2 幽門螺旋桿菌檢測方式簡介	2
1-3 免疫分析法	4
  1-3-1 抗原與抗體定義	5
  1-3-2 抗原與抗體結合力	6
  1-3-3 免疫分析檢測種類	7
  1-3-4 三明治免疫分析法	9
1-4 側流層析免疫反應試紙	10
1-5 影像判讀文獻回顧	13
  1-5-1 影像判讀簡介	13
  1-5-2 影像判讀之發展	15
1-6 研究動機與目的	19
1-7 章節架構	22
第二章 輕便型免疫分析系統設計與製作	24
2-1 系統流程	24
2-2 檢測設備設計	28
  2-2-1 檢測設備外殼	28
  2-2-2 檢測設備之實體	30
2-3 程式開發環境建立	31
  2-3-1 開發板選用-ARM_Raspberry pi	31
  2-3-2 作業系統選用-Raspbian	33
  2-3-3 程式語言選用-Python	34
2-4 環境參數設置	36
  2-4-1 環境設置系統架構	36
  2-4-2 人機介面與溝通	37
  2-4-3 LED與電路設計	38
  2-4-4 環境設置系統程式撰寫	38
2-5 影像處理	40
  2-5-1 檢測資訊匯入	40
  2-5-2 影像分析流程	41
  2-5-3 C、T線之抓取位置調整	45
  2-5-4 C、T線之半波峰演算法	45
第三章 實驗與研究方法	47
3-1 實驗儀器	47
  3-1-1 輕便型免疫分析系統硬體架構	47
    3-1-1-1 感光元件	48
    3-1-1-2 LCD顯示器	49
    3-1-1-3 RTC模組	50
  3-1-2 環境設置系統	51
    3-1-2-1 光照強度感測器	51
    3-1-2-2 LED光源	52
3-2 實驗藥品	53
3-3 設備及軟體整合測試	55
  3-3-1 C、T線讀值位置之測試	55
  3-3-2 QR Code之產生與測試	56
  3-3-3 C、T線判讀測試	57
    3-3-3-1 色階線性度測試	57
    3-3-3-2 色階偵測極限測試	58
  3-3-4 校正色卡實驗測試	59
  3-3-5 田口方法應用於免疫試紙判讀之最佳化	60
    3-3-5-1 理想機能與S/N比	61
    3-3-5-2 實驗的控制因子	63
    3-3-5-3 直交表	64
  3-3-6 輕便型免疫分析系統整合測試	65
    3-3-6-1 市售HPY試紙測試	65
    3-3-6-2 確認實驗與導入擬合曲線之定量測及濃度測試	66
第四章 結果與討論	67
4-1 光強度測試實驗與結果	67
4-2 C、T線讀值位置測試結果	70
4-3 QR Code之產生與測試結果	72
4-4 C、T線判讀測試結果	74
  4-4-1 色階線性度測試結果	74
  4-4-2 色階偵測極限結果	77
4-5 校正色卡實驗測試結果	80
4-6 最佳化分析	86
  4-6-1 灰階值判讀之控制因子及水準	86
  4-6-2 L9(34)直交表	87
  4-6-3 S/N比因子反應分析	89
  4-6-4 S/N比變異分析	91
  4-6-5 預測值與確認實驗	92
  4-6-6 品質損失計算	95
4-7 輕便型免疫分析系統整合測試結果	95
  4-7-1 HPY試紙測試結果	95
  4-7-2 確認實驗與導入擬合曲線之定性量測及濃度測試結果	98
第五章 結論與建議	103
5-1 結論	103
5-2 建議	105
參考文獻	106

                                    

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校內：2024-12-31公開
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