研究生: |
洪培凱 Hung, Pei-Kai |
---|---|
論文名稱: |
主成分分析法與LabVIEW機器視覺功能模擬酸鹼指示劑吸收光譜圖的開發與研究 Development and Study of Principal Component Analysis and LabVIEW Machine Vision Function for Simulating Absorption Spectra of Acid-Base Indicator |
指導教授: |
林震煌
Lin, Cheng-Huang |
口試委員: |
林震煌
Lin, Cheng-Huang 李君婷 Li, Chun-Ting 何佳安 Ho, Ja-An |
口試日期: | 2024/06/05 |
學位類別: |
碩士 Master |
系所名稱: |
化學系 Department of Chemistry |
論文出版年: | 2024 |
畢業學年度: | 112 |
語文別: | 中文 |
論文頁數: | 52 |
中文關鍵詞: | 主成分分析 、廣用指示劑 、紫外/可見光光譜法 |
英文關鍵詞: | Principal Component Analysis (PCA), Universal Indicator, Ultraviolet/Visible Spectroscopy (UV/Vis) |
DOI URL: | http://doi.org/10.6345/NTNU202400820 |
論文種類: | 學術論文 |
相關次數: | 點閱:75 下載:0 |
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本研究收集了不同酸鹼值下廣用指示液的吸收光譜和對應的顏色數據,這些顏色數據包括添加指示液後液體樣品的顏色以及樣品經過廣用試紙測試後的顏色。使用LabVIEW編寫資料處理程式,通過主成分分析得到光譜數據的主成分和特徵值,並利用線性迴歸模型分析主成分與顏色數據之間的關聯性,最終實現了根據顏色推斷廣用指示液吸收光譜的目標。
當將添加了廣用指示液的未知酸鹼樣品顏色數據匯入程式後,模擬與實際吸收光譜的均方根誤差在2%以內,重疊率均在96%以上;若以廣用試紙的顏色進行計算,均方根誤差在4%以內,重疊率約為90%。使用碳酸鈉溶液作為無色未知樣品,通過指示液和試紙的顏色推斷的吸收光譜,均方根誤差約為1.2%,重疊率約為97%。將茜素溶液作為有色樣品進行測試,結果顯示均方根誤差在5%以內,重疊率在80%以上。這些數據表明,本研究的方法能夠僅憑顏色數據模擬出吸收光譜,且結果具有較高的準確性。
In this study, the absorption spectra and corresponding color data of the universal indicator solution at different pH values were collected, which included the color of the liquid samples after the addition of the indicator solution as well as the color of the samples after testing on pH test strips. The data processing program was written in LabVIEW, and the principal components and eigenvalues of the spectral data were obtained through principal component analysis, and the association between the principal components and the color data was analyzed by linear regression model, which finally realized the goal of inferring the absorption spectrum of the universal indicator solution based on the color.
When the color data of the unknown acid-alkali samples spiked with the universal indicator solution were imported into the program, the root-mean-square error between the simulated and actual absorption spectra was within 2%, and the overlap rate was over 96%. When the color of the pH test strips was used in the calculation, the root-mean-square error was within 4%, and the overlap rate was about 90%. Using sodium carbonate solution as a colorless unknown sample, the absorption spectra deduced from the color of the indicator solution and the test strip had a root mean square error of about 1.2% and an overlap rate of about 97%. Alizarin solution was tested as a colored sample and the results showed that the root mean square error was within 5% and the overlap rate was over 80%. These data indicate that the method in this study can simulate the absorption spectra based on the color data only, and the results have a high accuracy.
[1] Manz, A.; Graber, N.; Widmer, H. M. Miniaturized Total Chemical Analysis Systems: A Novel Concept for Chemical Sensing. Sensors and Actuators B: Chemical, 1990, 1 (1), 244–248.
[2] Fay, C. D.; Wu, L. Critical Importance of RGB Color Space Specificity for Colorimetric Bio/Chemical Sensing: A Comprehensive Study. Talanta, 2024, 266, 124957.
[3] Abels, K.; Salvo-Halloran, E. M.; White, D.; Ali, M.; Agarwal, N. R.; Leung, V.; Ali, M.; Sidawi, M.; Capretta, A.; Brennan, J. D.; et al. Quantitative Point-of-Care Colorimetric Assay Modeling Using a Handheld Colorimeter. ACS Omega, 2021, 6 (34), 22439–22446.
[4] Smits, B. An RGB-to-Spectrum Conversion for Reflectances. Journal of Graphics Tools, 1999, 4 (4), 11–22.
[5] Inagawa, A.; Saito, K.; Fukuyama, M.; Numata, M.; Uehara, N. Geometrical pH Mapping of Microfluids by Principal-Component-Analysis-Based Xyz-Spectrum Conversion Method. Analytica Chimica Acta, 2021, 1182, 338952.
[6] Inagawa, A.; Kimura, M.; Uehara, N. Total Protein Assay by PCA-Based RGB-Spectrum Conversion Methods with Smartphone-Acquired Digital Images. ANAL. SCI., 2022, 38 (6), 869–880.
[7] Li, J.; O’Neill, M. L.; Pattison, C.; Zhou, J. H.-W.; Ito, J. M.; Wong, C. S. T.; Yu, H.-Z.; Merbouh, N. Mobile App to Quantify pH Strips and Monitor Titrations: Smartphone-Aided Chemical Education and Classroom Demonstrations. J. Chem. Educ., 2023, 100 (9), 3634–3640.
[8] Inagawa, A.; Sasaki, A.; Uehara, N. Reproducing Absorption Spectra of pH Indicators from RGB Values of Microscopic Images. Talanta, 2020, 216, 120952.
[9] Inagawa, A.; Saito, K.; Sasaki, A.; Uehara, N. Dataset for Reproducing Absorption Spectra of Methyl Orange from the RGB Values of Microscopic Images. Data in Brief, 2020, 31, 105998.
[10] Zou, W.; Yeo, S. Y. Non-Destructive Prediction of the Mixed Mineral Pigment Content of Ancient Chinese Wall Paintings Based on Multiple Spectroscopic Techniques. Appl Spectrosc, 2024, 00037028241248199.
[11] Kou, D.; Shi, T.; Li, L.; Zhang, S.; Ma, W. Programmable Hierarchical Photonic Crystal Arrays Activated by Post-Hydrolysis for Highly Sensitive Biochemical pH Sensing. Chemical Engineering Journal, 2023, 463, 142176.
[12] Wold, S.; Esbensen, K.; Geladi, P. Principal Component Analysis. Chemometrics and Intelligent Laboratory Systems, 1987, 2 (1), 37–52.
[13] Magnaghi, L. R.; Alberti, G.; Zanoni, C.; Guembe-Garcia, M.; Quadrelli, P.; Biesuz, R. Chemometric-Assisted Litmus Test: One Single Sensing Platform Adapted from 1–13 to Narrow pH Ranges. Sensors, 2023, 23 (3), 1696.
[14] Jeng, J.-C. Adaptive Process Monitoring Using Efficient Recursive PCA and Moving Window PCA Algorithms. Journal of the Taiwan Institute of Chemical Engineers, 2010, 41 (4), 475–481.
[15] Valle, S.; Li, W.; Qin, S. J. Selection of the Number of Principal Components: The Variance of the Reconstruction Error Criterion with a Comparison to Other Methods. Industrial and Engineering Chemistry Research, 1999, 38 (11), 4389–4401.
[16] Bush, B. L.; Nachbar, R. B. Sample-Distance Partial Least Squares: PLS Optimized for Many Variables, with Application to CoMFA. J Computer-Aided Mol Des, 1993, 7 (5), 587–619.
[17] Zeng, Y.; Xiao, P.; Henkelman, G. Unification of Algorithms for Minimum Mode Optimization. The Journal of Chemical Physics, 2014, 140 (4), 044115.
[18] Sauvé, S.; Hendershot, W.; Allen, H. E. Solid-Solution Partitioning of Metals in Contaminated Soils: Dependence on pH, Total Metal Burden, and Organic Matter. Environ. Sci. Technol., 2000, 34 (7), 1125–1131.
[19] Manne, R. Analysis of Two Partial-Least-Squares Algorithms for Multivariate Calibration. Chemometrics and Intelligent Laboratory Systems, 1987, 2 (1), 187–197.
[20] Rodríguez, L. C.; Campa[nbreve]Ta, A. M. G.; Linares, C. J.; Ceba, M. R. Estimation of Performance Characteristics of an Analytical Method Using the Data Set Of The Calibration Experiment. Analytical Letters, 1993, 26 (6), 1243–1258.
[21] Soares, S. F. C.; Gomes, A. A.; Araujo, M. C. U.; Filho, A. R. G.; Galvão, R. K. H. The Successive Projections Algorithm. TrAC Trends in Analytical Chemistry, 2013, 42, 84–98.
[22] Todeschini, R.; Consonni, V.; Mauri, A.; Pavan, M. Detecting “Bad” Regression Models: Multicriteria Fitness Functions in Regression Analysis. Analytica Chimica Acta, 2004, 515 (1), 199–208.
[23] Thissen, U.; Üstün, B.; Melssen, W. J.; Buydens, L. M. C. Multivariate Calibration with Least-Squares Support Vector Machines. Anal. Chem., 2004, 76 (11), 3099–3105.
[24] Haaland, D. M.; Thomas, E. V. Partial Least-Squares Methods for Spectral Analyses. 1. Relation to Other Quantitative Calibration Methods and the Extraction of Qualitative Information. Anal. Chem., 1988, 60 (11), 1193–1202.
[25] Haaland, D. M.; Thomas, E. V. Partial Least-Squares Methods for Spectral Analyses. 2. Application to Simulated and Glass Spectral Data. Anal. Chem., 1988, 60 (11), 1202–1208.
[26] Coxon, J. A. Merging of Least-Squares Parameters: The Approach of Stepwise Merging. Journal of Molecular Spectroscopy, 1978, 72 (2), 252–263.
[27] de Souza, S. V. C.; Junqueira, R. G. A Procedure to Assess Linearity by Ordinary Least Squares Method. Analytica Chimica Acta, 2005, 552 (1), 25–35.
[28] Li, H.; Zhao, C.; Wei, S.; Liu, X.; Li, J.; Wang, J. A pH-Regulated Colorimetric Sensor Array for Discrimination of Biothiols Based on Two Different-Sized β-Cyclodextrin-Functionalized AuNPs. J. Anal. Test., 2023, 7 (2), 101–109.
[29] Li, Z.; Askim, J. R.; Suslick, K. S. The Optoelectronic Nose: Colorimetric and Fluorometric Sensor Arrays. Chem. Rev., 2019, 119 (1), 231–292.
[30] Primpke, S.; Wirth, M.; Lorenz, C.; Gerdts, G. Reference Database Design for the Automated Analysis of Microplastic Samples Based on Fourier Transform Infrared (FTIR) Spectroscopy. Anal Bioanal Chem, 2018, 410 (21), 5131–5141.
[31] Almeida, J. A. S.; Barbosa, L. M. S.; Pais, A. A. C. C.; Formosinho, S. J. Improving Hierarchical Cluster Analysis: A New Method with Outlier Detection and Automatic Clustering. Chemometrics and Intelligent Laboratory Systems, 2007, 87 (2), 208–217.
[32] El-Abassy, R. M.; Donfack, P.; Materny, A. Rapid Determination of Free Fatty Acid in Extra Virgin Olive Oil by Raman Spectroscopy and Multivariate Analysis. Journal of the American Oil Chemists’ Society, 2009, 86 (6), 507–511.
[33] Yang, M.; Xu, D.; Chen, S.; Li, H.; Shi, Z. Evaluation of Machine Learning Approaches to Predict Soil Organic Matter and pH Using Vis-NIR Spectra. Sensors, 2019, 19 (2), 263.
[34] Li, G.; Jiang, D.; Zhou, Y.; Jiang, G.; Kong, J.; Manogaran, G. Human Lesion Detection Method Based on Image Information and Brain Signal. IEEE Access, 2019, 7, 11533–11542 .
[35] Otto, M. Fuzzy Theory Explained. Chemometrics and Intelligent Laboratory Systems, 1988, 4 (2), 101–120.
[36] Zuo, H.; Luo, Z.; Guan, J.; Wang, Y. Identification on Rock and Soil Parameters for Vibration Drilling Rock in Metal Mine Based on Fuzzy Least Square Support Vector Machine. J. Cent. South Univ., 2014, 21 (3), 1085–1090.
[37] Otto, Matthias.; Wegscheider, Wolfhard.; Lankmayr, E. P. A Fuzzy Approach to Peak Tracking in Chromatographic Separations. Anal. Chem., 1988, 60 (6), 517–521.
[38] Bandemer, H.; Otto, M. Fuzzy Theory in Analytical Chemistry. Mikrochim Acta, 1986, 89 (1), 93–124.
[39] Smith, T.; Guild, J. The C.I.E. Colorimetric Standards and Their Use. Trans. Opt. Soc., 1931, 33 (3), 73.
[40] Hunt, R. W. G.; Pointer, M. R. A colour-appearance transform for the CIE 1931 standard colorimetric observer. Color Research & Application, 1985, 10 (3), 165–179.
[41] Li, H.; Wang, X.; Li, X.; Yu, H.-Z. Quantitative pH Determination Based on the Dominant Wavelength Analysis of Commercial Test Strips. Anal. Chem., 2021, 93 (46), 15452–15458.
[42] Hao, J.; Studenikin, S. A.; Cocivera, M. Blue, Green and Red Cathodoluminescence of Y2O3 Phosphor Films Prepared by Spray Pyrolysis. Journal of Luminescence, 2001, 93 (4), 313–319.
[43] Kuehni, R. G. Hue Uniformity and the CIELAB Space and Color Difference Formula. Color Research & Application, 1998, 23 (5), 314–322.
[44] Smet, P. F.; Parmentier, A. B.; Poelman, D. Selecting Conversion Phosphors for White Light-Emitting Diodes. J. Electrochem. Soc., 2011, 158 (6), R37.
[45] Almeida Jr, P. L. de; Lima, L. M. A.; Almeida, L. F. de. A 3D-Printed Robotic System for Fully Automated Multiparameter Analysis of Drinkable Water Samples. Analytica Chimica Acta, 2021, 1169, 338491.
[46] Katin, O.; Lukyanov, A.; Goryanina, K. Optimization of the Automated Colorimetric Measurement System for pH of Liquid. MATEC Web Conf., 2017, 132, 04010.
[47] Choi, I.; Lee, J. Y.; Lacroix, M.; Han, J. Intelligent pH Indicator Film Composed of Agar/Potato Starch and Anthocyanin Extracts from Purple Sweet Potato. Food Chemistry, 2017, 218, 122–128.
[48] Pourjavaher, S.; Almasi, H.; Meshkini, S.; Pirsa, S.; Parandi, E. Development of a Colorimetric pH Indicator Based on Bacterial Cellulose Nanofibers and Red Cabbage (Brassica Oleraceae) Extract. Carbohydrate Polymers, 2017, 156, 193–201.
[49] Buryak, A.; Severin, K. A Chemosensor Array for the Colorimetric Identification of 20 Natural Amino Acids. J. Am. Chem. Soc., 2005, 127 (11), 3700–3701.
[50] Promphet, N.; Rattanawaleedirojn, P.; Siralertmukul, K.; Soatthiyanon, N.; Potiyaraj, P.; Thanawattano, C.; Hinestroza, J. P.; Rodthongkum, N. Non-Invasive Textile Based Colorimetric Sensor for the Simultaneous Detection of Sweat pH and Lactate. Talanta, 2019, 192, 424–430.
[51] Christodouleas, D. C.; Nemiroski, A.; Kumar, A. A.; Whitesides, G. M. Broadly Available Imaging Devices Enable High-Quality Low-Cost Photometry. Anal. Chem., 2015, 87 (18), 9170–9178.