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

  • 제50권8호
  • /
  • Pages.238-244
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  • 2013
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  • 2287-5026(pISSN)
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  • 2288-159X(eISSN)
대한전자공학회 (The Institute of Electronics and Information Engineers)
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EDXRF 스펙트럼을 위한 효율적인 배경 모델링과 보정 방법

An Efficient Background Modeling and Correction Method for EDXRF Spectra

  • Park, Dong Sun (IT Convergence Research Center, Chonbuk National University) ;
  • Jagadeesan, Sukanya (Dept. of Electronics and Communication Eng., Mahendra College of Engineering) ;
  • Jin, Moonyong (Division of Electronics Engineering, Chonbuk National University) ;
  • Yoon, Sook (Dept. of Multimedia Eng., Mokpo National University)
  • 투고 : 2013.05.15
  • 발행 : 2013.08.15
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초록

에너지 분산형 X-선 형광(EDXRF) 분석에서 X-선 스펙트럼에 존재하는 컨티넘(continuum)의 추정 및 제거는 필수적이다. 이를 위해 일반적으로 사용되는 알고리즘들은 많은 주의가 필요하며 복잡하다. 보통 이 알고리즘들은 제약적이거나 컨티넘의 데이터나 모양에 대한 가설을 필요로 한다. 본 논문에서는 제안된 에너지 분산형 X-선 형광 스펙트럼을 위한 효율적인 배경(background) 보정 방법은 배경 모델링과 배경 보정으로 구성된다. 이 방법은 스펙트럼에서 백그라운드영역과 피크영역을 구분하는 기본 개념을 기반으로 하며 성능향상을 위하여 SNIP알고리즘을 사용한다. 스펙트럼으로부터 배경에 속하는 점들을 획득한 후 이를 기반으로 곡선 근사화를 통해 배경을 모델링한다. 이후 획득된 배경 모델을 원 스펙트럼에서 뺌으로써 배경이 보정된 스펙트럼을 얻는다. 제안된 방법은 상대적으로 적은 사전 지식을 요구하면서 기존의 몇몇 방법들에 비해 우수한 결과를 보여주었다.

In energy dispersive X-ray fluorescence analysis, the removal of the continuum on which the X-ray spectrum is superimposed is one of the most important processes, since it has a strong influence on the analysis result. The existing methods which have been used for it usually require tight constraints or prior information on the continuum. In this paper, an efficient background correction method is proposed for Energy Dispersive X-ray fluorescence (EDXRF) spectra. The proposed method has two steps of background modeling and background correction. It is based on the basic concept which differentiates background areas from the peak areas in a spectrum and the SNIP algorithm, one of the popular methods for background removal, is used to enhance the performance. After detecting some points which belong to the background from a spectrum, its background is modeled by a curve fitting method based on them. And then the obtained background model is subtracted from the raw spectrum. The method has been shown to give better results than some of traditional methods, while working under relatively weak constraints or prior information.

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