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研究生:金昆詠
研究生(外文):JIN, KUN-YONG
論文名稱:血糖量測之葡萄糖生物感測器製程技術影響與評估
論文名稱(外文):Evaluation of The Effect of Processing Technology on Glucose Biosensor Measurement
指導教授:張合李仁方李仁方引用關係
指導教授(外文):CHANG, HOLEE, JEN-FANG
口試委員:張合李仁貴李文德李仁方許春耀張鴻銘陳治豪
口試委員(外文):CHANG, HOLEE, REN-GUEILEE, WIN-DERLEE, JEN-FANGSYU, CHUN-YAOJHANG, HONG-MINGCHEN, JHIH-HAO
口試日期:2020-03-24
學位類別:博士
校院名稱:國立臺北科技大學
系所名稱:製造科技研究所
學門:工程學門
學類:機械工程學類
論文種類:學術論文
論文出版年:2020
畢業學年度:108
語文別:英文
論文頁數:181
中文關鍵詞:噴射點膠生化感測器線性回歸標準差變異係數
外文關鍵詞:Jetting DispenserbiosensorLinear Regressionstandard deviation (STD)coefficient of variation(CV)
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本論文研究探討噴射點膠技術,對於黃金電極生化感測器(試片)的準確度與穩定度之影響,從噴射點膠製程參數和血糖儀的電流電壓特性及血糖值大數據預測演算法分析。該研究分為兩個主要方向: 1. 點膠製程對試片準確性提升,2. 血糖儀參數調整及血糖數據的管理和預測。點膠機安裝有補償壓力的裝置,可以調節噴射點膠壓力的穩定,使噴射點膠的液滴重量保持一致,調節壓力會影響流體壓力改變,進而影響噴射點膠的穩定性。然而,點膠高度不當也會導致葡萄糖氧化酶(Glucose Oxidase, GOD)液滴飛濺,溢出或偏移,而造成噴射點膠的液滴分佈不均,進一步影響試片讀值的準確性。而點膠閥內部的撞針上下作動,產生噴射點膠液滴的效果。因撞針在密封的腔體內,如果潤滑不足,撞針表面會產生大量摩擦,導致噴射點膠液滴不均勻,進而影響試片的精度。試片的讀值是透過血糖儀的電化學分析得到結果,調整血糖儀內部電阻提供不同電流,電阻會改變血糖儀內部電流供應,對試片產生不同的電化學變化。血糖儀內部供電電位給試片,過低的電位會造成高濃度的標準液無法呈現正確的血糖讀值,顯示血糖儀對試片反應不完整,而造成試片讀值無法正確呈現。血糖儀所量測每一筆實驗數據整理後,進行線性回歸分析(Linear regression analysis)觀察Correlation coefficient(R2)愈接近1,顯示試片讀值準確度最佳,使用Consensus柵格分析 (Consensus Error Grid analysis) 圖觀察試片讀值對臨床使用判定依據,數據換算STD和CV值比較差異,再透過Bias圖分析誤差±15 %範圍,更容易地觀察試片讀值的精度偏差。應用智慧血糖儀收集血糖大數據,血糖儀必須使用ARM架構的裝置,選寫簡單線性迴歸的演算法,透過遠端高運算電腦分析結果,可預測飯前飯後的前後差異,血糖讀值落點分佈在簡單線性迴歸的預測模型上,線性趨勢線上下的讀值落點,愈貼近線性趨勢線顯示預測準確度愈準確,這個血糖預測演算結果可應用詞向量(word vector)演算及語音,發展未來智慧血糖的概念應用。
This thesis aims to study the influence of blood glucose meter measurement on the accuracy and stability of the gold-electrode biochemical sensor (Test Strip) by analyzing areas such as the jet dispensing process parameters, the current and voltage characteristics of the blood glucose meter, and the blood glucose big data algorithm. The study is divided into two major directions: the pre-measurement test strips accuracy improvement and the post-measurement blood glucose data management and prediction. After installing the pressure stabilizing device with compensation mechanism on the jet dispenser, the fluid pressure can be adjusted to stabilize the dispensing process, allowing the weight of the dispensed droplets to be consistent. Adjusting the feeding pressure of the dispenser can affect the fluid pressure, which in turn changes the stability of the dispensed droplets. The gap between the dispensing nozzle and the reaction zone of the test strip (the dispensing height), can affect the dispensing of the GOD (Glucose oxidase) droplet on the reaction zone of the test strip. Improper dispensing height will cause splashing, overflow or offset of droplets, leading to inconsistent sizes of droplets, which further affects the accuracy of the test strip readings. The dispensing process is made possible by the up and down movement of the striker (needle) in the chamber of the dispensing valve hitting the seat. During the movement of the striker against the seal, substantial amount of friction will be generate on the striker surface if there is not enough lubrication, resulting in the dispense of uneven droplets, which in turn affects the accuracy of the test strip. The reading of the test strip is obtained through the analysis of the electrochemical reaction by a blood glucose meter. Adjusting the internal resistance of the blood glucose meter can provide different currents, altering the internal current supply of the blood glucose meter to induce different electrochemical reaction for the test strip. The internal potential of the blood glucose meter is supplied to the test strip. Too low in potential will cause the blood glucose meter unable to display the correct blood glucose reading for standard solution with high concentration, suggesting that the electrochemical reaction of the test trip induced by the blood glucose meter is not complete, which results in the reading limitation. After all the measurements from the experiment were organized, linear regression analysis was performed to determine and observe the corresponding correlation coefficient (R2). If the closer the correlation coefficient is to 1, the more accurate the test strip reading is. Consensus Error Grid analysis was adopted to observe the test strip readings, serving as the basis for deciding its clinical use. The readings were converted to STD and CV values for deviation analysis. By adopting the bias analysis with the accuracy range of ± 15%, the deviation in the accuracy of the test strip readings can be more easily observed. An ARM-based smart blood glucose meter is required to obtain the big data from the measurements of the test strips. By building the algorithms and analyzing the results through a remote high-performance computer, the glucose level difference before and after meals can be predicted. The blood glucose readings are distributed on the trend line calculated by the simple linear regression model. The closer the readings are to the linear trend line, the more accurate the prediction is. The developed blood glucose calculation can be applied to smart blood glucose management, exploring the future smart blood glucose conceptual applications.
摘要 i
ABSTRACT iii
誌謝 vi
Table of Content vii
Table of Tables xiii
Table of Figures xvi
Chapter 1 Introduction 1
1.1 Introduction 1
1.2 Research Background 2
1.3 Research Motivation and Objectives 3
1.4 Thesis Structure 5
Chapter 2 Literature Review 6
2.1 Literature review 6
2.1.1Glucose biochemical sensor characteristics 6
2.1.2 Electrochemical characteristics of glucose biochemical sensor 11
2.1.3 Dispensing process for making glucose biochemical sensor 15
2.1.4 Blood glucose meter analysis characteristics 19
2.1.5 big data prediction 22
2.2 Glucose concentration standard 24
2.3 The effect of rheology (fluid flow) in jet dispensing 27
2.4 Chemical reaction of glucose oxidase 29
2.5 Cyclic Voltammetry (CV) 30
2.6 Chronoamperometry(CA) 34
2.7 Electrochemical impedance spectroscopy (EIS) 35
2.8 Measurement accuracy of blood glucose meter 37
2.9 Working principle of YSI 38
2.10 YSI whole blood error grid analysis 39
2.10.1 Fasting blood glucose 43
2.10.2 Pre-prandial blood glucose 43
2.10.3 Random blood glucose 43
2.12 Simple Linear Regression 43
2.13 NumPy 44
2.14 Numpy array 45
2.15 Decision Model (tree structure) 46
2.16 Naive Bayes classifier 47
Chapter 3 Experiment and Design 49
3.1 Glucose biochemical sensor reaction zone structure 49
3.2 Fluid pressure of jet dispensing 50
3.3 Asytemk Dispensing System 52
3.4 non-contact Dispenser Jet 53
3.5 DJ-9000 jet dispensing valve operation 55
3.6 Blood glucose test strip substrate 57
3.7 Fabrication of glucose biochemical sensor 60
3.8 Preparation of GOD reagent (solution) 61
3.9 Blood glucose meter circuit measurement duty cycle 62
3.10 Circuit design of blood glucose meter 63
3.11 Blood glucose meter operating circuit characteristics 64
3.12 Blood glucose meter signal resolution 66
3.13 Smart blood sugar design process 66
3.14 Cyclic voltammetry analysis 70
3.15 CHI1221 Electrochemical analyzer 70
3.16 Working principle of YSI 2300 analyzer 73
3.17 YSI 2300 Stat Plus Measurement Methodology 74
3.18 Blood glucose measurement process 75
Chapter 4 Results and Discussion 77
4.1 Standard solution drawn by glucose biochemical sensor 77
4.2 Electrochemical analysis 78
4.3 Effect of the dispensing valve without pressure stabilizing device on the stability of the dispensed droplets 83
4.4 Effect of the dispensing valve with pressure stabilizing device on the stability of the dispensed droplets 85
4.5 Effect of dispensing Valve with automatic pressure compensation device on the stability of the dispensed droplets 87
4.6 Effect of dispensing fluid pressure on test strip readings 89
4.6.1 Effect of dispensing fluid pressure on the STD (Standard Deviation) and the CV (Coefficient of Variation) of the test strip 89
4.6.2 Accuracy of test strip prepared under different fluid pressures 91
4.6.3 Linear regression analysis of the results of test strips prepared under different fluid pressures 93
4.6.4 Consensus Error Grid analysis of the results of the test strips prepared under different fluid pressures 96
4.6.5 Bias analysis of the results of test strips prepared under different fluid pressures 98
4.7 Effect of dispensing height on test strip readings 101
4.7.1 Effect of dispensing height on the STD (Standard Deviation) and the CV (Coefficient of Variation) of the test strip 101
4.7.2 Accuracy of test strip prepared at different dispensing heights .103
4.7.3 Linear regression analysis of the results of test strips prepared at different dispensing heights 105
4.7.4 Consensus Error Grid analysis of the results of the test strips prepared at different dispensing heights 108
4.7.5 Bias analysis of the results of test strips prepared at different dispensing heights 111
4.8 Effect of striker (needle) lubrication on test strip readings 113
4.8.1 Effect of striker (needle) lubrication on the STD (Standard Deviation) and the CV (Coefficient of Variation) of the test strip 113
4.8.2 Accuracy of test strip prepared by the striker covered with different amount of grease 116
4.8.3 Linear regression analysis of the results of test strips prepared by the striker covered with different amount of grease 118
4.8.4 Consensus Error Grid analysis of the results of the test strips prepared by the striker covered with different amount of grease 121
4.8.5 Bias analysis of the results of test strips prepared by the striker covered with different amount of grease 123
4.9 Effect of resistance on accuracy of test strip readings 126
4.9.1 Effect of blood glucose meter resistance on the STD (Standard Deviation) and the CV (Coefficient of Variation) of the test strip 126
4.9.2 Effect of blood glucose meter resistance on the accuracy of the control solution tests 129
4.9.3 Linear regression analysis of the results of test strips measured by blood glucose meter with different resistances 131
4.9.4 Consensus Error Grid analysis of the results of the test strips measured by blood glucose meter with different resistances 134
4.9.5 Bias analysis of the results of test strips measured by blood glucose meter with different resistances 137
4.10 Effect of blood glucose meter potential on the performance of glucose biochemical sensor 139
4.10.1Effect of blood glucose meter potential on the STD (Standard Deviation) and the CV (Coefficient of Variation) of the test strip 139
4.10.2 Effect of blood glucose meter potential on the accuracy of the control solution tests 141
4.10.3 Voltage and time correlation curve analysis 143
4.10.4 Linear regression analysis of the results of test strips measured by blood glucose meter with different reference potential 147
4.10.5 Consensus Error Grid analysis of the results of the test strips measured by blood glucose meter with different reference potentials 150
4.10.6 Bias analysis of the results of test strips measured by blood glucose meter with different reference potential 153
4.11 Analysis of blood glucose prediction calculation 156
4.11.1 Python blood glucose prediction algorithm codes 156
4.11.2 blood glucose prediction trend 158
4.11.3 Linear regression model prediction accuracy 161
Chapter 5 Conclusion and Future Work 163
5.1 Stability of jet dispensing droplets 163
5.2 Deviation analysis of the effect of jet dispensing fluid pressure on test strip reading 163
5.3 Deviation analysis of the effect of jet dispensing height on test strip reading 164
5.4 Deviation analysis of the effect of grease on test strip reading 165
5.5 Deviation analysis of the effect of R3 resistance on test strip reading .165
5.6 Deviation analysis of the effect of blood glucose meter reference potential on test strip reading 166
5.7Analysis of blood glucose prediction calculation 167
5.8 Future Directions 167
References 169
Educational Background 181
Research Achievements 181

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