Effects of baseline correction algorithms on forensic classification of paper based on ATR-FTIR spectrum and principal component analysis (PCA)

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1 Citation (Scopus)

Abstract

Spectral data is often required to be pre-processed prior to applying a multivariate modelling technique. Baseline correction of spectral data is one of the most important and frequently applied pre-processing procedures. This preliminary study aims to investigate the impacts of six types of baseline correction algorithms on classifying 150 infrared spectral data of three varieties of paper. The algorithms investigated were Iterative Restricted Least Squares, Asymmetric Least Squares (ALS), Low-pass FFT Filter, Median Window (MW), Fill Peaks and Modified Polynomial Fitting. Processed spectral data were then analysed using Principal Component Analysis (PCA) to visually examine the clustering among the three varieties of paper. Results show that separation among the three varieties of paper is greatly improved after baseline correction via ALS, FP and MW algorithms.

Original languageEnglish
Pages (from-to)767-774
Number of pages8
JournalPertanika Journal of Science and Technology
Volume25
Issue number3
Publication statusPublished - 1 Jul 2017

Fingerprint

Fourier Transform Infrared Spectroscopy
Principal Component Analysis
Least-Squares Analysis
Principal component analysis
spectral analysis
principal component analysis
least squares
Median filters
Fast Fourier transforms
filters
Cluster Analysis
Polynomials
Infrared radiation
fill
Processing
filter
forensic sciences
effect
modeling
methodology

Keywords

  • Baseline correction
  • Forensic science
  • IR spectroscopy
  • Paper
  • Principal component analysis (PCA)

ASJC Scopus subject areas

  • Computer Science(all)
  • Chemical Engineering(all)
  • Environmental Science(all)
  • Agricultural and Biological Sciences(all)

Cite this

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title = "Effects of baseline correction algorithms on forensic classification of paper based on ATR-FTIR spectrum and principal component analysis (PCA)",
abstract = "Spectral data is often required to be pre-processed prior to applying a multivariate modelling technique. Baseline correction of spectral data is one of the most important and frequently applied pre-processing procedures. This preliminary study aims to investigate the impacts of six types of baseline correction algorithms on classifying 150 infrared spectral data of three varieties of paper. The algorithms investigated were Iterative Restricted Least Squares, Asymmetric Least Squares (ALS), Low-pass FFT Filter, Median Window (MW), Fill Peaks and Modified Polynomial Fitting. Processed spectral data were then analysed using Principal Component Analysis (PCA) to visually examine the clustering among the three varieties of paper. Results show that separation among the three varieties of paper is greatly improved after baseline correction via ALS, FP and MW algorithms.",
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AU - Jemain, Abdul Aziz

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