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光谱图相似性匹配是推测化合物结构的重要研究方法之一,而如何在标准谱图数据库中进行相似性查找是关键步骤。传统的谱图匹配方法在数据量较大时,检索效率较低。本文首次将互关联后继树(TRST)算法思想应用于光谱图数据领域,从光谱图特征数据点出发,通过对算法的改进,提出了1种基于斜率序列的互关联后继树算法(SSIRST)实现光谱图相似性匹配查找,旨在通过减少匹配过程中的数据量缩短查找时间。实验结果表明,算法可以有效提高光谱图相似性匹配查找效率1倍以上。
Spectral similarity matching is one of the most important research methods to infer the structure of a compound, and how to search for similarities in a standard spectral database is a key step. The traditional spectral matching method has a low retrieval efficiency when the amount of data is large. This paper, for the first time, applies the idea of TRST algorithm to spectrum data. Based on the characteristic data points of spectrogram, this paper proposes an improved algorithm based on slope sequence (SSIRST) Spectral Similarity Matching Search, designed to reduce lookup time by reducing the amount of data in the matching process. The experimental results show that the algorithm can effectively improve the search efficiency of spectral similarity matching by more than 1 time.