臺灣博碩士論文加值系統

當糖尿病檢測血糖值會分空腹、飯後，根據醫院標準空腹血糖值需空腹八小時後測得值，則飯後需由第一口飯算飯後兩小時才可測量。
本實驗可利用程式將實驗後樣品進行辨識，抓取樣品上顏色並計算得出CIE1931 xy座標值，CIE數值表示為(x,y)，因CIE1931無亮度變化，所以不會因為實驗環境的亮度不同而出現誤差，使實驗更為準確。
血糖值單位為mg/dL，每100毫升含有多少毫克(mg)葡萄糖，本實驗參照醫院空腹血糖值與飯後血糖值去做比較，醫院血糖過低為70 mg/dL以下正常值為70 mg/dL~99 mg/dL糖尿病前期為100 mg/dL~125 mg/dL糖尿病為126 mg/dL以上，使用本氏液試劑調配濃度0 mg/dL~15 mg/dL呈現藍色，CIE數值為(0.22,0.25)，70 mg/dL~105 mg/dL呈現綠色，CIE數值為(0.39,0.49)，110 mg/dL~145 mg/dL呈現橙色，CIE數值為(0.5,0.44)，150 mg/dL~ 220 mg/dL以上呈現紅色，CIE數值為(0.59,0.33)，本氏液與醫院空腹做對照，因人體最低血糖值為50 mg/dL不可能出現藍色，所以將藍色、綠色分為血糖正常，呈現橙色為糖尿病前期，呈現紅色時為糖尿病。醫院飯後血糖值，血糖過低為70 mg/dL以下正常值為70 mg/dL~140 mg/dL糖尿病前期為141 mg/dL~200 mg/dL糖尿病為201 mg/dL，當使用DNS試劑去做調配時濃度65 mg/dL以下呈現黃色，CIE數值為(0.49,0.46)，70 mg/dL~130 mg/dL呈現橙色，CIE數值為(0.53,0.43)，135 mg/dL~180 mg/dL呈現棕色，CIE數值為(0.58,0.38)，185 mg/dL以上呈現紅色，CIE數值為(0.75,0.25)，DNS與醫院飯後做對照，當呈現黃色為血糖過低，呈現橙色為正常範圍，呈現棕色為糖尿病前期，呈現深紅色為糖尿病本研究為預防性醫療檢測，當身體不適因立即就醫。
本論文提出免扎針方式來檢測葡萄糖含量，利用不同葡萄糖含量與DNS、本氏液混合物的藉由載具加熱，先使用CCD鏡頭辨識QR code，QR code含有病人名字、生日，再將DNS加熱125°C加熱5分鐘，晶片會因血糖濃度不同而出現不同顏色，DNS會呈現黃色、橙色、棕色、深紅色，本氏液加熱100°C加熱1分鐘晶片會呈現藍色、綠色、橙色、紅色再透過CCD鏡頭連接LabVIEW影像識別系統辨識顏色，以獲得不同顏色各項數據，再將此數據匯出Excel，並將檢測的日期、時間、血糖值範圍，儲存時會以患者的健保卡號。
本實驗開發的系統也可用於檢測水果的甜度，測試該水果的葡萄糖含糖量，因每種水果甜度不相同，可確認水果品質達到標準，讓消費者能購買到高品質的產品。

When testing blood glucose levels for diabetes, there are two main categories: fasting and post-meal measurements. According to hospital standards, fasting blood glucose levels should be measured after an eight-hour fasting period, while post-meal levels are measured two hours after the first bite of a meal.
This experiment utilizes a program to identify and capture colors on the samples obtained after the experiment. It calculates CIE1931 xy coordinates, denoted as (x, y). Since CIE1931 does not account for changes in brightness, it eliminates errors due to variations in ambient lighting, ensuring the experiment's accuracy.
Blood glucose levels are measured in mg/dL, indicating how many milligrams (mg) of glucose are present per 100 milliliters. This experiment compares fasting and post-meal glucose levels to hospital standards. Normal fasting glucose levels are below 70 mg/dL, while 70 mg/dL - 99 mg/dL is considered normal, 100 mg/dL - 125 mg/dL is pre-diabetic, and above 126 mg/dL indicates diabetes. By using a reagent with concentrations ranging from 0 mg/dL - 15 mg/dL, it appears blue with CIE coordinates (0.22, 0.25), green from 70 mg/dL - 105 mg/dL with CIE coordinates (0.39, 0.49), orange from 110 mg/dL - 145 mg/dL with CIE coordinates (0.5, 0.44), and red from 150 mg/dL - 220 mg/dL and above with CIE coordinates (0.59, 0.33). By comparing this reagent with fasting glucose levels, it becomes evident that blue cannot occur, making blue and green represent normal blood glucose, orange indicating pre-diabetes, and red indicating diabetes.For post-meal glucose levels, blood glucose below 70 mg/dL is considered low, 70 mg/dL - 140 mg/dL is normal, 141 mg/dL - 200 mg/dL is pre-diabetic, and above 201 mg/dL is diabetic. When using DNS reagent, concentrations below 65 mg/dL appear yellow with CIE coordinates (0.49, 0.46), 70 mg/dL - 130 mg/dL appear orange with CIE coordinates (0.53, 0.43), 135 mg/dL - 180 mg/dL appear brown with CIE coordinates (0.58, 0.38), and above 185 mg/dL appear red with CIE coordinates (0.75, 0.25). By comparing DNS with post-meal glucose levels, yellow represents low blood glucose, orange falls within the normal range, brown indicates pre-diabetes, and deep red indicates diabetes.
This study focuses on preventive medical testing, encouraging immediate medical attention when experiencing discomfort. The proposed system offers a non-invasive method to measure glucose levels by heating samples with different glucose concentrations mixed with DNS and Benedict's reagent. First, a CCD camera recognizes a QR code containing the patient's name and date of birth. Then, DNS is heated to 125°C for 5 minutes, causing the chip to display different colors based on glucose concentration. DNS produces yellow, orange, brown, and deep red colors. Benedict's reagent is heated to 100°C for 1 minute, causing the chip to display blue, green, orange, and red colors. These colors are recognized by a LabVIEW image recognition system connected to a CCD camera, and the data are exported to Excel, including the test date, time, and blood glucose range, linked to the patient's health insurance card number.
The system developed in this experiment can also be used to test the sweetness of fruits by measuring their glucose content. Since the sweetness of each fruit varies, this system can ensure that fruits meet quality standards, allowing consumers to purchase high-quality products.

摘要 I
ABSTRACT III
誌謝 VI
目錄 VII
圖目錄 X
表目錄 XIII
第一章 緒論 1
1-1前言 1
1-2研究動機與目的 1
第二章 文獻回顧與基礎理論 2
2-1糖尿病介紹 2
2-1-1糖尿病前期 2
2-1-2糖尿病後期 3
2-2血糖監測 3
2-2-1無扎針血糖監測 4
2-3影像辨識 4
2-3-1臉部辨識 4
2-3-2車牌辨識 5
2-4物品追蹤 6
2-4-1影片追蹤 6
2-4-2圖像追蹤 7
2-5邊緣檢測 9
2-6圖像處理 10
2-7影像雜訊處理 11
(a)高斯濾波器 11
(b)中值濾波器 12
2-8水果甜度 13
2-8-1糖度測量儀器 13
2-8-2 Brix度 14
2-8-3 水果換算Brix度表 14
2-9 藥品介紹 15
2-9-1本氏液 15
2-9-2二硝基水楊酸(DNS) 16
2-9-3氫氧化鈉 17
2-9-4 酒石酸鉀鈉 17
2-10 色彩空間 19
2-10-1 CIE 1931xyz 19
2-10-2 CIE LAB 19
2-10-3 RGB 20
第三章 研究方法及實驗架設 21
3-1 實驗流程、架構圖說明 21
3-2 實驗設備 24
(a)LabVIEW系統 24
(b)CCD鏡頭 25
(c)加熱片 27
(d)載玻片 27
3-3溶液調配、保存方式 28
(a)二硝基水楊酸(DNS) 28
3-4實驗架設與流程 28
(a)QR code身分識別 29
(b)物品追蹤及濾波 30
(c)顏色識別 30
(d)數據匯出 31
3-5水果葡萄糖含量 39
3-6實驗器材比較 40
3-7針對不同結果血糖濃度探討 42
(a)DNS針對不同血糖濃度 42
(b)本氏液針對不同血糖濃度 44
第四章 結果與討論 47
第五章 結論與未來展望 50
5-1結論 50
5-2未來展望 51
參考文獻 52

[1]https://helloyishi.com.tw/diabetes/blood-sugar-normal-levels-and-testing/
[2]D. Tomic,J.-E. SHAW, D.-J. Magliano, “The burden and risks of emerging complications of diabetes mellitus.” Nature Reviews Endocrinology, vol. 18, pp. 525-539, 2022
[3]S. Yadav, “Deep learning based safe social distancing and face mask detection in public areas for covid-19 safety guidelines adherence.” International Journal for Research in Applied Science and Engineering Technology,vol. 8., pp. 1368-1375, 2020
[4]S.-Z. Masood, G. Shu, A. Dehghan, E.-G. Ortiz, “License plate detection and recognition using deeply learned convolutional neural networks.” arXiv preprint arXiv, vol. 1703.07330, 2017.
[5]L. Wenhu, H. Weipeng, G. Wenchao, Y. Fan, Z. Lu, “An improved KCF algorithm and its application in video tracking. ” in: 2022 international conference on computers and artificial intelligence technologies (CAIT). IEEE, pp. 79-84, 2022.
[6]R. Holonec, R. Copindean, F. Dragan, V.-D. Zaharia, “Object tracking system using stereo vision and labview algorithms.” Acta Electrotehnica, vol. 1-2, pp. 71-76, 2014.
[7]R. Maini, H. Aggarwal, “Study and comparison of various image edge detection techniques.” International journal of image processing (IJIP), vol. 1, pp. 1-11, 2009.
[8]F. Merchant, K. Castleman, “Microscope image processing.” Academic press, 2022
[9]S. Satpathy, M.-C Pradhan, S. Sharma, “Comparative study of noise removal algorithms for denoising medical image using LabVIEW.” In: 2015 International Conference on Computational Intelligence and Communication Networks (CICN). IEEE, pp. 300-305, 2015.
[10]M. Elewa, G. El-Saady, K. Ibrahim, M. Tawfek, H. Elhossieny, “A novel method for brix measuring in raw sugar solution.” Egyptian Sugar Journal, vol. 15, pp. 69-86, 2020
[11]J.-Y. Serpen, “Comparison of sugar content in bottled 100% fruit juice versus extracted juice of fresh fruit.” Food and Nutrition Sciences, vol. 3, pp. 1509–1513, 2012.
[12]R.-D. Simoni, R.-L. Hill, M. Vaughan, “Benedict's solution, a reagent for measuring reducing sugars: the clinical chemistry of stanley R. Benedict.” Journal of Biological Chemistry, vol. 277, pp. 10-11, 2002
[13]C.-Y. Heah, H. Kamarudin, A.-M. Mustafa Al Bakri, M. Bnhussain, M. Luqman, I.-K Nizar, C.-M. Ruzaidi, Y.-M. Liew, “Study on solids-to-liquid and alkaline activator ratios on kaolin-based geopolymers.” Construction and Building Materials, vol. 35, pp. 912-922, 2012.
[14]T. Budtova, P. Navard, “Cellulose in NaOH–water based solvents: a review.” Cellulose, vol. 23, pp. 5-55, 2016
[15]R. Hu, L. Lin, T Liu, P. Ouyang, B. He, S.Liu, “Reducing sugar content in hemicellulose hydrolysate by DNS method: a revisit.” Journal of Biobased Materials and Bioenergy, vol. 2, pp. 156-161, 2008
[16]M. Tkalcic, J.-F. Tasic, “Colour spaces: perceptual, historical and applicational background” vol. 1, pp. 304-308, 2003
[17]楊高科. 圖像處理, 分析與機器視覺: 基於 LabVIEW. 清華大學出版社 (崧博), 2018.
[18]“http://www2.nkust.edu.tw/~ikuojm/file1/foodchem.pdf”
[19]R.S.S. Teixeira, A.S. da Silva, V.S. Ferreira-Leitão, E.P.S. da Bon, “Amino acids interference on the quantification of reducing sugars by the 3, 5-dinitrosalicylic acid assay mislead carbohydrase activity measurements.” Carbohydrate research, vol. 363, pp. 33-37, 2012
[20]S.-V. Ting, J.-G. Blair, “The relation of specific gravity of whole fruit to the internal quality of oranges.” In: Proceedings of the Florida State Horticultural Society, vol. 78, pp. 251-261, 1965.
[21]“https://science.cyc.edu.tw/upfile/science101/work_files/12261160206204.pdf”
[22]P.-E. Cryer, S.-N. Davis, H. Shamoon, “Hypoglycemia in diabetes.” Diabetes care, vol. 26, pp. 1902-1912, 2003.
[23]N. BANSAL, “Prediabetes diagnosis and treatment: A review.” World journal of diabetes,vol. 6, p. 296, 2015.
[24]N.-G. Forouhi, N.-J. Wareham, “Epidemiology of diabetes.” Medicine, vol. 38, pp. 602-606, 2010.