Automatic baseline correction method for infrared spectroscopy

Abstract

The invention discloses an automatic baseline correction method for infrared spectroscopy. The automatic baseline correction method comprises the following steps: A1, preparing a to-be-detected sample; B1, acquiring an infrared spectrum of the to-be-detected sample by using a spectrograph, and preprocessing the spectrum to obtain the original absorbance spectrum; C1, for spectral intensity Y=[y1, y2...yN] in the original absorbance spectrum at equal interval, performing average minimum updating on the original absorbance spectrum, and obtaining the spectral intensity Y1=[y1,...yi+1] of the absorbance spectrum after the first update; D1, calculating the sum of range differences, namely Sabs(1) =Sigma of an absolute value of (Y-Y1); E1, repeating the step C1 for performing iterative computations for n times, updating the Y1 to obtain Y2, deducing the rest to obtain the Y1, Y2...Yn, further performing iterative computations on the step D1 to obtain Sabs(n), then calculating until the deltaSabs(n-1)/Sabs(n) is less than or equal to an iterative threshold lambda, and stopping calculating to obtain a baseline Yn of the absorbance spectrum, wherein n is more than 2; and F1, subtracting the baseline from the original absorbance spectrum, thereby obtaining the corrected absorbance spectrum.

Description

technical field [0001] The invention belongs to the field of infrared spectrum analysis, in particular to an automatic baseline correction method of infrared spectrum. Background technique [0002] Infrared spectral analysis is an important method for qualitative substance. Its analysis can provide a lot of information about functional groups. During the qualitative and quantitative analysis of Fourier transform infrared spectroscopy, baseline correction of the original spectrum is a key step in the spectral processing process. Infrared Changes in the light source of the spectrometer, the state of the instrument, and the atmospheric environment will cause a certain baseline drift in the measured infrared spectrum. In order to achieve real-time processing and analysis of the spectrum, it is first necessary to perform automatic baseline correction on the spectrum. The so-called baseline correction is to artificially pull the baseline of the infrared spectrum back to the 0 bas...

AI Technical Summary

Problems solved by technology

The so-called baseline correction is to artificially pull the baseline of the infrared spectrum back to the 0 baseline, but most of the baseline correction methods now need to set some empirical parameters, which reduces the universality and accuracy of the baseline correction method to a certain extent. Adaptability, it is difficult to meet the needs of real-time online analysis of infrared spectroscopy

For example, the polynomial fitting algorithm assumes that the baseline can be approximated as a low-order polynomial, fits the data with a suitable polynomial, discards any points on the fitted curve that are greater than a certain standard deviation, and repeats this step for the remaining points , until no data points need to be discarded, and this method performs poorly in the case of low signal-to-noise ratio or low signal-to-background ratio; the baseline correction of the automatic iterative moving average method is not smooth enough in the case of multiple peaks or overlapping peaks and the effect Not very good; both the ALS method and the airPLS method are based on Whittaker smooth baseline correction, and the baseline correction effect is better, but both require parameter optimization, and adjusting the optimal parameters is a process that requires experience; the traditional Rubber-band method will The spectrum is divided into several parts, the lowest point of each part is considered as the position of the baseline, and then the estimated baseline is completed by combining these points with linear interpolation or spline interpolation. This method needs to manually adjust the segment width and correct the results. Depends to some extent on the experience of the operator

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