Automatic baseline correction method

Abstract

The invention discloses an automatic baseline correction method, which includes the following steps: converting raw data into spectrogram data; utilizing continuous wavelet transform to calculate the numerical derivative of a spectrogram; respectively utilizing the sliding window method and the iterative threshold method to identify narrow peak signals and broad peak signals in the spectrogram; carrying out contour fitting on the broad peak signals to identify the edges of the broad peak signals; calculating a baseline model; correcting the spectrogram data according to the baseline model; searching distorted signals in the corrected spectrogram data, and recalculating the baseline model with the most severely distorted points as baseline points until distorted signals do not exist, so that a final baseline model and final corrected spectrogram data can be obtained. Compared with the prior art, the automatic baseline correction method can eliminate the inaccuracy of noise calculation caused by baseline distortion, accurately identify the narrow peak signals and the broad peak signals in the spectrogram, correct the spectrogram with the severely distorted baseline and prevent spectral peak distortion introduced into the complex spectrogram by baseline correction.

Description

Technical field [0001] The invention relates to the field of spectrogram analysis, and more particularly to an automatic baseline correction method. It is suitable for the analysis and post-data processing of various spectra such as nuclear magnetic resonance spectrum, chromatogram and Raman spectrum. Background technique [0002] Baseline distortion will have a great impact on spectrum analysis and quantitative analysis of the spectrum. Eliminating the baseline distortion is a necessary step in the later data processing of the spectrum. Currently, the widely used baseline correction algorithms are frequency domain baseline correction methods. The principle is to construct a smooth baseline model based on the spectrum data, and then subtract the baseline model from the original spectrum data to obtain the spectrum data without distortion. [0003] The general frequency domain baseline correction method is to build a baseline model on the identified baseline points. Compared with...

AI Technical Summary

Problems solved by technology

Using a specific function to fit the baseline model is not suitable for many spectra with large baseline distortion, because the baseline distortion may be of arbitrary shape and does not satisfy the specific function shape

Whittaker filtering is a more effective smoothing algorithm, but it may distort the spectral peak signal when processing complex data

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