Automatic Correction of Continuum Background in Laser-Induced Breakdown and Raman Spectrometry

Abstract

The goal of this work was the development and evaluation of an algorithm for the approximation and automatic subtraction of continuum backgrounds in laser-induced breakdown and Raman spectra. The background correction algorithm was applied to simple and complex spectra and its effect on identification accuracy was studied. Linear correlation was used for the identification of plastic samples using both laser-induced breakdown and Raman spectra. For both techniques, the algorithm successfully eliminated continuum background without compromising spectral integrity. A significant improvement in the percentage of correct plastic identifications was observed for Raman spectra. The approach should be applicable to a wide range of background correction problems in atomic and molecular spectroscopy.

Automatic estimation of varying continuum background emission in laser-induced breakdown spectroscopy

“…The reasons for selecting polynomial functions for background approximation is that polynomial functions can approximate a large class of background and calculation speed is rapid [12]. Polynomial power is automatically chosen from 1 to 10, based on the least standard deviation between the polynomial and the minima left.…”

“…If a spectrum contains no isolated lines (pure continuum), or only a few isolated lines, or few groups of line overlaps, we define the spectrum as a simple one; otherwise, we define it as a complex one [12].…”

“…These depending correlations are not fixed in LIBS due to many factors, such as the fluctuation of laser power, inhomogeneity of sample surface, geometrical parameters, and ambient gas [9,11]. Therefore, continuum background must be determined dynamically from spectra to spectra and from samples to samples [12].…”

“…Gornushkin et al present another procedure for the automatic correction of continuum background [12]. This procedure first finds all minima on a spectrum as major minima, splits the spectrum into a certain number of groups N, and calculates the means and standard deviations of the major minima in each group.…”

Noninvasive Glucose Sensing with Raman Spectroscopy

“…For example, a 600 cm - 1 Raman shift occurs at 873. 5 In this chapter we focus on non-resonant spontaneous Raman scattering. A special case of Raman scattering, surface-enhanced Raman spectroscopy (SERS), is discussed in Chapter X.…”