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http://dx.doi.org/10.5573/ieie.2016.53.3.124

Automatic Selection of Optimal Parameter for Baseline Correction using Asymmetrically Reweighted Penalized Least Squares  

Park, Aaron (Department of Electronics and Computer Engineering, Chonnam National University)
Baek, Sung-June (Department of Electronics and Computer Engineering, Chonnam National University)
Park, Jun-Qyu (Department of Electronics and Computer Engineering, Chonnam National University)
Seo, Yu-Gyung (Department of Electronics and Computer Engineering, Chonnam National University)
Won, Yonggwan (Department of Electronics and Computer Engineering, Chonnam National University)
Publication Information
Journal of the Institute of Electronics and Information Engineers / v.53, no.3, 2016 , pp. 124-131 More about this Journal
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;
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1 D. Chen, X. Shao, B. Hu, and Q. Su, "A Background and noise elimination method for quantitative calibration of near infrared spectra," Analytica Chimica Acta, Vol. 511, Issue 1, pp. 37-45, May 2004.   DOI
2 P. Heraud, B. R. Wood, J. Beardall, and D. McNaughton, "Effects of pre-processing of Raman spectra on in vivo classification of nutrient status of microalgal cells," Journal of Chemometrics, Vol. 20, No. 5, pp. 193-197, May 2006.   DOI
3 J. Zhao, H. Lui, D. I. McLean, and H. Zeng, "Automated autofluorescence background subtraction algorithm for biomedical Raman spectroscopy," Applied. Spectroscopy, Vol. 61, Issue 11, pp. 1225-1232, 2007.   DOI
4 Z.-M. Zhang, S. Chen, and Y.-Z. Liang, "Baseline correction using adaptive iteratively reweighted penalized least squares," Analyst, Vol. 135, Issue 5, pp. 1138-1146, Feb. 2010.   DOI
5 P. H. Eilers, "A perfect smoother," Analytical Chemistry, Vol. 75, no. 14, pp. 3631-3636. Jul. 2003.   DOI
6 S.-J. Baek, A. Park, Y.-J. Ahn, and J. Choo, "Baseline correction using asymmetrically reweighted penalized least squares smoothing," Analyst, Vol. 140, Issue 1, pp. 250-257, Jan. 2015.   DOI
7 Y. Hu, T. Jiang, A. Shen, W. Li, X. Wang, and J. Hu, "A background elimination method based on wavelet transform for Raman spectra," Chemometrics and Intelligent Laboratory Systems, Vol. 85, Issue 1, pp. 94-101, Jan. 2007.   DOI
8 S.-J. Baek, A. Park, J. Kim, A. Shen, and J. Hu, "A simple background elimination method for Raman spectra," Chemometrics and Intelligent Laboratory Systems, Vol. 98, Issue 1, pp. 24-30, Aug. 2009.   DOI
9 S.-J. Baek, A. Park, A. Shen, and J. Hu, "A background elimination method based on linear programming for Raman spectra," Journal of Raman Spectroscopy, Vol. 42, Issue 11, pp. 1987-1993, Nov. 2011.   DOI
10 R. L. McCreery, Raman Spectroscopy for Chemical Analysis, John Wiley & Sons, pp. 199-202, 2000.
11 T. Dieing, O. Hollricher, and J. Toporski, Confocal Raman Microscopy, Springer, pp. 68-70, 2010.
12 J. Hwang, N. Choi, A. Park, et al., "Fast and sensitive recognition of various explosive compounds using Raman spectroscopy and principal component analysis," Journal of Molecular Structure, Vol. 1039, pp. 130-136, 2013.   DOI