Method for performing clustering analysis and identification on substances by adopting Raman spectrums

Abstract

The invention discloses a method for performing clustering analysis and identification on substances by adopting Raman spectrums, and relates to a substance identification method. The method comprises the steps of (1) acquiring the Raman spectrums of samples through a Raman spectrometer, and forming a sample set consisting of p samples; (2) calculating difference coefficients among all the samples, and establishing a difference coefficient matrix; (3) removing isolated samples according to the difference coefficients among the samples, and performing re-arrangement to form a new difference coefficient matrix; (4) sequentially clustering various samples of the sample set from the new difference coefficient matrix; (5) repeating the step (4) until all elements are distinguished; and (6) re-arranging an original sample sequence according to a clustering sequence, and re-drawing the difference coefficient matrix for enabling various samples to be accurately distinguished into corresponding blocks. According to the method, Raman signal clustering under high background interference is finished by utilizing the difference coefficients of the spectrums; a clustering method is unsupervised; and the method is simple, convenient and low in cost, meets exploratory detection and field actual application demands, and can be widely applied to the fields of drugs, foods, chemicals and the like.

Description

Technical field [0001] The invention relates to a method for cluster analysis and identification of substances in the fields of chemical analysis and instrument analysis, in particular to a method for cluster analysis and identification of substances by using Raman spectroscopy. Background technique [0002] Raman spectroscopy is often used for material characterization, such as "Surface-enhanced Raman Spectroscopy Identification Method for Foodborne Pathogens" published as CN102115778A, and "A method for identifying cancer cells using surface-enhanced Raman spectroscopy" published as CN103487425A . However, due to the weak signal of the substance itself, the accompanying strong fluorescent signal, and the overlap of various samples and isolated samples in the sample set, cause problems for clustering and identification. Especially for miniature Raman spectrometers with low signal-to-noise ratio, it affects the practicability of such instruments. In the background of strong flu...

AI Technical Summary

Problems solved by technology

However, due to the weak signal of the substance itself and the strong fluorescent signal accompanied by it, and the overlap of various samples and isolated samples in the sample set, clustering and identification are troubled

Especially for micro-Raman spectrometers with low signal-to-noise ratio, which affects the practicability of such instruments

In the background of strong fluorescence, if the common principal component analysis (PCA) is used for visual description, it is difficult to identify and cluster analysis

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