基線消除方法和峰檢測在微型光譜儀模組中的應用｜國立臺灣科技大學博碩士論文系統

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研究生：	郭福貴 Vincentius Christian Bintang Parikesit
論文名稱：	基線消除方法和峰檢測在微型光譜儀模組中的應用 Application of Baseline Elimination Methods and Peaks Detection for Micro Spectrometer Module
指導教授：	柯正浩 Cheng-Hao Ko
口試委員:	徐勝均 Sendren Sheng-Dong Xu) 沈志霖 Ji-Lin Shen
學位類別：	碩士 Master
系所名稱：	工程學院 - 自動化及控制研究所 Graduate Institute of Automation and Control
論文出版年：	2021
畢業學年度：	109
語文別：	英文
論文頁數：	89
中文關鍵詞：	微型光譜儀、基線校正、最小二乘、SWiMA 、BEADS 、VRA
外文關鍵詞：	Micro Spectrometer, Baseline correction, least-square, SWiMA, BEADS, VRA
相關次數：	點閱：250 下載：4
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上一筆

Spectrochip是一種以毫米等級開發的光柵元件，它被應用在微型光譜儀中作為分光元件。但是它輸出光譜訊息中含有我們不需要的基線，這種含有基線的訊號會影響光譜分析。為了找出訊號中的基線，普遍的基線提取方法，主要是根據興趣區間
，然後沿著估計點擬合曲線，形成基線輪廓。
本文對七種基線消除方法進行了綜合比較，以找出其中最合適的演算法。這七種方法分別是不對稱最小二乘法（aLS）、全自動基線校正（FABC）、溫哥華拉曼演算法（VRA）、自我調整反覆運算懲罰最小二乘法（airPLS）、基於小視窗移動平均（SWiMA）、基於稀疏導數（BEADS）進行基線估計和去噪以及非對稱重加權懲罰最小二乘法（arPLS）。
模擬結果表明，arPLS演算法是最佳的演算法。通過五個效能矩陣表明，該演算法能準確的估計並去除了基線雜訊。對光源的實驗結果顯示出雷射光源和汞氬燈採用SWiMA演算法表現最好，而白光led光源採用VRA法表現最好。對於牛奶樣品的實驗結果表明了SWiMA和arPLS，此兩種方法都能非常有效的進行基線估計並去除。

The Spectrochip is defined as a spectrometer module in a compact form as small as an SD card developed by miniaturizing the gratings component in micrometer order. The continuum or baseline is an unwanted component of the signals that disrupt the spectral interpretation. Most baseline extraction methods work by estimating the background noise point along the wavenumber region of interest and then drawing the curve fitting along the estimated points to form the baseline profile.
This thesis presents a comprehensive comparison of seven baseline remover methods to find the most suitable algorithm amongst them. The seven methods used for denoising the raw signals are: 1. Asymmetric Least-Squares (aLS), 2. Fully Automatic Baseline Correction (FABC), 3. Vancouver Raman Algorithm (VRA), 4. Adaptive Iteratively Reweighted Penalized Least-Squares (airPLS), 5. Small-Window Moving Average Based (SWiMA), 6. Baseline Estimation and Denoising using Sparse Derivative (BEADS), 7. Asymmetric Reweighted Penalized least-square(arPLS).
The simulated results show that arPLS is the best algorithm. It is indicated by the five performance matrices and shows that this algorithm carefully estimates and removes the baseline noise. In comparison, no sample experimental results show that the best method is SWiMA for laser and mercury sources and VRA for white-led sources. Experimental results show that the SWiMA and arPLS can very well estimate the baseline for the milk sample.

Acknowledgment    I
摘要        II
ABSTRACT    III
Contents    IV
List of Figures    VII
List of Tables    X
Chapter 1    Introduction    1
1    Research Background and Motivation    1
2    Thesis Objective    4
3    Thesis Structure    5
Chapter 2    Basic Theory    6
1    Diffraction principle    6
2    Diffraction Grating    7
3    Beer-Lambert Law    8
4    Types and Range of Spectrometer    10
5    Spectrometer Architecture    11
Chapter 3    Baseline Elimination and Peak Detection Method    12
1    Spectral Datasets    13
1.1    Simulated Spectral    13
1.2    Three Light Sources    16
1.3    Milk Data    17
2    Baseline Correction Method    19
2.1    Asymmetric Least-Squares    19
2.2    Fully Automatic Baseline Correction    20
2.3    Vancouver Raman Algorithm    21
2.4    Adaptive Iteratively Reweighted Penalized Least-Squares    22
2.5    Small-Window Moving Average Based    23
2.6    Baseline Estimation and Denoising using Sparse Derivative    24
2.7    Asymmetric Reweighted Penalized Least-Square    25
3    Method Assessment    26
3.1    Root Mean Squared Error    26
3.2    Coefficient of Determination    27
3.3    The goodness of Fit Coefficient    28
3.4    Chi-Squared    29
3.5    Time Cost    29
4    Peak Detection    30
Chapter 4    Experimental Results and Discussion    31
1    Simulated Signals    31
1.1    Constant    31
1.2    Line    33
1.3    Inverse Line    35
1.4    Growth    37
1.5    Decay    39
1.6    Hill    41
1.7    Parabola    43
1.8    Sine    45
1.9    Cosine    47
1.10    Tangent Hyperbolic    49
1.11    Performance Summary    51
2    Three Light Sources    52
2.1    Laser 1D Correction    52
2.2    Mercury 1D Correction    56
2.3    White LED 1D Correction    59
3    Milk Data    61
Chapter 5    Conclusion and Future Work    71
Future Work        69
References        73


                                

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