光譜學已經成為21世紀最重要的科學領域之一。其在食品安全、生命科學和環境分析等各個領域的應用都依賴於光譜分析，實現了對材料結構和組分的定性定量無損分析。然而，傳統的光譜儀通常體積龐大、價格昂貴，並且缺乏空間效率，因為它們需要外部電腦控制。為了解決這些挑戰，微型化光譜儀的概念，也稱為微光譜儀，日益受到關注。
本文提出了利用微電子機械系統 (MEMS) 技術構建的光譜晶片和樹莓派作為控制器的系統，以創建一個微型化光譜儀。通過利用這些元件，該系統相對於傳統光譜儀具有緊湊設計、成本效益和便攜性。
為支援所提出的系統，本文使用Python程式設計語言開發了兩個光譜儀介面軟體應用程式。第一個軟體專注於捕捉光譜，並包括光譜資料分析、水準掃描ROI、自動強度調整和資料匯出等功能。該介面使使用者能夠高效地捕獲和分析光譜資料。
第二個軟體介面專用於光譜儀校準。它提供自動峰值識別等功能，用於校準目的，並採用三次多項式擬合來建立一個將圖元空間資訊轉換為波長空間資訊的轉換方程。校正過程旨在最微型光譜儀得到的轉換波長值與相應的標準波長值之間的差異，實現的精度具有均方根誤差低至3 nm內的水準，表明波長測量非常精確。

Spectroscopy has emerged as one of the most significant scientific fields in the 21st century. Its applications in various domains, including food safety, life sciences, and environmental analysis, have relied on spectral analysis, enabling qualitative and quantitative nondestructive analysis of material structures and components. However, traditional spectrometers are often bulky, expensive, and lack space efficiency since they require external computer control. To address these challenges, the concept of miniaturized spectrometers, also known as microspectrometer, has gained popularity.
This thesis proposes a system that utilizes a spectral chip constructed using Micro Electro Mechanical Systems (MEMS) technology, along with a Raspberry Pi as the controller, to create a miniaturized spectrometer. By leveraging these components, the system achieves a compact design, cost-effectiveness, and portability compared to traditional spectrometers.
To support the proposed system, two spectrometer interface software applications were developed using the Python programming language. The first software focuses on capturing spectra and includes features such as spectrum data analysis, horizontal scanning to identify the Region of Interest (ROI), auto intensity adjustment, and data export for further analysis. This interface enables users to efficiently capture and analyze spectral data.
The second software interface is dedicated to spectrometer calibration. It offers functions such as automatic peak identification for calibration purposes and cubic polynomial fitting to establish a conversion equation that converts pixel spatial information to wavelength spatial information. The calibration process aims to minimize the discrepancy between the converted wavelength values obtained from the spectrometer and the corresponding standard wavelength values. The achieved accuracy is reported to have a root mean square (RMS) difference as low as 3 nm, indicating precise wavelength measurements.

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