Peak-Detection-and-Separation-in-Mass-Spectrometry-Data

Repository Contents

• 1_orientation.ipynb
• 2_unsupervised_methods.ipynb
• 3_advanced_analysis.ipynb
• Project_Presentation_AI.pptx

Overview

This project focuses on improving the analysis and separation of overlapping spectral peaks in mass spectrometry data. Accurate detection and separation of these peaks are crucial for identifying substances in complex mixtures, which has applications in various fields such as biochemistry, pharmacology, and environmental science.

Background and Motivation

Mass spectrometry is a critical technique used in various scientific and clinical applications to analyze the composition of chemical compounds. One significant challenge in mass spectrometry data analysis is the accurate prediction and determination of overlapping peaks. These peaks, often represented as Gaussian distributions, can overlap due to different resolutions and compound similarities, leading to potential misinterpretations.

Presentation:

A presentation explaining the project's objectives, methods, results, and future work is available in this repository. It includes:

• Overview of mass spectrometry and the significance of peak detection.
• Challenges with overlapping peaks.
• Methods used for data preprocessing, smoothing, and analysis.
• Results and visualizations demonstrating the effectiveness of the methods.
• Future work and potential improvements.