Peak Pattern Based Similarity Search for High-Dimensional Spectral Data

This chapter introduces a peak pattern-based similarity search framework designed to improve the accuracy and robustness of high-dimensional spectral data analysis. The framework integrates robust preprocessing techniques, including noise filtering, normalization, and peak detection, followed by flexible peak alignment with tunable tolerance parameters. The similarity metrics proposed, such as Jaccard, Rank, and Weighted Rank similarities, are evaluated using real-world datasets, demonstrating improved performance in tasks like compound identification and quality control. The experimental results highlight the impact of alignment tolerance and intensity weighting on retrieval performance, showcasing the framework's potential for applications in mass spectrometry and other high-dimensional spectral domains. The chapter also discusses the advantages of using Continuous Wavelet Transform (CWT) for peak detection and the importance of peak alignment in correcting spectral distortions. The evaluation experiments, including TOPN and DELTA accuracy assessments, provide a comprehensive analysis of the proposed methods' effectiveness in microbial identification and serotype classification. Overall, this framework offers a scalable and accurate solution for similarity search in spectral data, addressing the challenges posed by peak shifts and spectral noise.