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

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. © 2017 universiti putra malaysia press.