Journal of the Institute of Electronics and Information Engineers (전자공학회논문지)

The Institute of Electronics and Information Engineers (대한전자공학회)
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Automatic Selection of Optimal Parameter for Baseline Correction using Asymmetrically Reweighted Penalized Least Squares

Asymmetrically Reweighted Penalized Least Squares을 이용한 기준선 보정에서 최적 매개변수 자동 선택 방법

  • 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)
  • 박아론 (전남대학교 전자컴퓨터공학부) ;
  • 백성준 (전남대학교 전자컴퓨터공학부) ;
  • 박준규 (전남대학교 전자컴퓨터공학부) ;
  • 서유경 (전남대학교 전자컴퓨터공학부) ;
  • 원용관 (전남대학교 전자컴퓨터공학부)
  • Received : 2015.10.14
  • Accepted : 2016.03.03
  • Published : 2016.03.25
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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.

분광법을 이용한 많은 응용에서 스펙트럼 데이터의 기준선 보정은 분석 시스템의 성능을 좌우하는 매우 중요한 과정이다. 기준선은 많은 경우에 육안 검사로 매개변수를 선택하여 추정한다. 이 과정은 매우 주관적이고 특히 대량의 데이터인 경우 지루한 작업을 동반하므로 좋은 분석 결과를 보장하기 어렵다. 이러한 이유로 기준선 보정에서 최적의 매개변수를 자동으로 선택하기 위한 객관적인 방법이 필요하다. 이전의 연구에서 PLS(penalized least squares) 방법에 새로운 가중 방식을 도입하여 기준선을 추정하는 arPLS(asymmetrically reweighted PLS) 방법을 제안하였다. 본 연구에서는 arPLS 방법에서 최적의 매개변수를 자동으로 선택하는 방법을 제안한다. 이 방법은 가능한 매개변수의 범위에서 추정한 기준선의 적응도와 평활도를 계산한 다음 정규화한 적응도와 평활도의 합이 최소가 되는 매개변수를 선택한다. 경사 기준선, 곡선 기준선, 이중 곡선 기준선의 모의실험 데이터와 실제 라만 스펙트럼을 이용한 실험에서 제안한 방법이 기준선 보정을 위한 최적 매개변수의 선택에 효과적으로 적용될 수 있음을 확인하였다.

Keywords

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