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http://dx.doi.org/10.5762/KAIS.2019.20.3.500

Decision function for optimal smoothing parameter of asymmetrically reweighted penalized least squares  

Park, Aa-Ron (School of Electronics and Computer Engineering, Chonnam National University)
Park, Jun-Kyu (Korea Institute of Industrial Technology)
Ko, Dae-Young (School of Electronics and Computer Engineering, Chonnam National University)
Kim, Sun-Geum (School of Electronics and Computer Engineering, Chonnam National University)
Baek, Sung-June (School of Electronics and Computer Engineering, Chonnam National University)
Publication Information
Journal of the Korea Academia-Industrial cooperation Society / v.20, no.3, 2019 , pp. 500-506 More about this Journal
Abstract
In this study, we present a decision function of optimal smoothing parameter for baseline correction using Asymmetrically reweighted penalized least squares (arPLS). Baseline correction is very important due to influence on performance of spectral analysis in application of spectroscopy. Baseline is often estimated by parameter selection using visual inspection on analyte spectrum. It is a highly subjective procedure and can be tedious work especially with a large number of data. For these reasons, an objective procedure is necessary to determine optimal parameter value for baseline correction. The proposed function is defined by modeling the median value of possible parameter range as the length and order of the background signal. The median value increases as the length of the signal increases and decreases as the degree of the signal increases. The simulated data produced a total of 112 signals combined for the 7 lengths of the signal, adding analytic signals and linear and quadratic, cubic and 4th order curve baseline respectively. According to the experimental results using simulated data with linear, quadratic, cubic and 4th order curved baseline, and real Raman spectra, we confirmed that the proposed function can be effectively applied to optimal parameter selection for baseline correction using arPLS.
Keywords
Baseline Correction; Background Elimination; Smoothing Parameter; Penalized Least Squares; Raman Spectroscopy;
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