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Automatic Selection of Optimal Parameter for Baseline Correction using Asymmetrically Reweighted Penalized Least Squares
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 Title & Authors
Automatic Selection of Optimal Parameter for Baseline Correction using Asymmetrically Reweighted Penalized Least Squares
Park, Aaron; Baek, Sung-June; Park, Jun-Qyu; Seo, Yu-Gyung; Won, Yonggwan;
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 Abstract
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, it is an objective and automatic procedure is necessary to select optimal parameter value for baseline correction. Asymmetrically reweighted penalized least squares (arPLS) based on penalized least squares was proposed for baseline correction in our previous study. The method uses a new weighting scheme based on the generalized logistic function. In this study, we present an automatic selection of optimal parameter for baseline correction using arPLS. The method computes fitness and smoothness values of fitted baseline within available range of parameters and then selects optimal parameter when the sum of normalized fitness and smoothness gets minimum. According to the experimental results using simulated data with varying baselines, sloping, curved and doubly curved baseline, and real Raman spectra, we confirmed that the proposed method can be effectively applied to optimal parameter selection for baseline correction using arPLS.
 Keywords
baseline correction;noise removal;optimal parameter;penalized least squares;Raman spectroscopy;
 Language
Korean
 Cited by
 References
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