Blind source separation in diffuse reflectance NIR spectroscopy using independent component analysis

Maunu Toiviainen (Corresponding Author), F. Corona, Janne Paaso, Pekka Teppola

Research output: Contribution to journalArticleScientificpeer-review

11 Citations (Scopus)

Abstract

Near‐infrared (NIR) spectroscopy permits non‐contact analysis of solid samples in the diffuse reflectance (DR) measurement mode. However, uncontrolled physical variations between solid samples, such as changes in packing density and particle size distribution, have a complex nonlinearizing effect on the NIR spectra which complicates the extraction of chemical information from data.

Blind source separation (BSS) methods attempt to blindly factorize the measured mixture spectra into the pure analyte spectra and their concentration profiles. The physical interferences, however, make the application of BSS methods difficult on the NIR spectra of solids. The application of independent component analysis (ICA) on NIR DR spectra is discussed, and a three‐phase preprocessing procedure of the measured spectral signals designed to improve the separation capability of ICA is proposed in this work. The method involves the removal of nonlinear effects from the measured spectra using scatter correction, denoising with rank reduction and alteration of the sample statistics of the signals via differentiation with respect to the wavelength. The procedure is tested and the explanatory power of BSS is demonstrated using a laboratory data set comprising ternary mixtures of pharmaceutical powders.
Original languageEnglish
Pages (from-to)514-522
Number of pages9
JournalJournal of Chemometrics
Volume24
Issue number7-8
DOIs
Publication statusPublished - 2010
MoE publication typeA1 Journal article-refereed

Fingerprint

Near-infrared Spectroscopy
Blind source separation
Blind Source Separation
Independent component analysis
Independent Component Analysis
Reflectance
Spectroscopy
Reflectometers
Particle size analysis
Powders
Drug products
Rank Reduction
Statistics
Sample statistic
Factorise
Wavelength
Non-contact
Pharmaceuticals
Nonlinear Effects
Scatter

Keywords

  • near-infrared spectroscopy
  • blind source separation
  • independent component analysis
  • spectral preprocessing

Cite this

Toiviainen, Maunu ; Corona, F. ; Paaso, Janne ; Teppola, Pekka. / Blind source separation in diffuse reflectance NIR spectroscopy using independent component analysis. In: Journal of Chemometrics. 2010 ; Vol. 24, No. 7-8. pp. 514-522.
@article{c21b4e6ab19e4dae8d67c642b20f00e4,
title = "Blind source separation in diffuse reflectance NIR spectroscopy using independent component analysis",
abstract = "Near‐infrared (NIR) spectroscopy permits non‐contact analysis of solid samples in the diffuse reflectance (DR) measurement mode. However, uncontrolled physical variations between solid samples, such as changes in packing density and particle size distribution, have a complex nonlinearizing effect on the NIR spectra which complicates the extraction of chemical information from data.Blind source separation (BSS) methods attempt to blindly factorize the measured mixture spectra into the pure analyte spectra and their concentration profiles. The physical interferences, however, make the application of BSS methods difficult on the NIR spectra of solids. The application of independent component analysis (ICA) on NIR DR spectra is discussed, and a three‐phase preprocessing procedure of the measured spectral signals designed to improve the separation capability of ICA is proposed in this work. The method involves the removal of nonlinear effects from the measured spectra using scatter correction, denoising with rank reduction and alteration of the sample statistics of the signals via differentiation with respect to the wavelength. The procedure is tested and the explanatory power of BSS is demonstrated using a laboratory data set comprising ternary mixtures of pharmaceutical powders.",
keywords = "near-infrared spectroscopy, blind source separation, independent component analysis, spectral preprocessing",
author = "Maunu Toiviainen and F. Corona and Janne Paaso and Pekka Teppola",
year = "2010",
doi = "10.1002/cem.1316",
language = "English",
volume = "24",
pages = "514--522",
journal = "Journal of Chemometrics",
issn = "0886-9383",
publisher = "Wiley",
number = "7-8",

}

Blind source separation in diffuse reflectance NIR spectroscopy using independent component analysis. / Toiviainen, Maunu (Corresponding Author); Corona, F.; Paaso, Janne; Teppola, Pekka.

In: Journal of Chemometrics, Vol. 24, No. 7-8, 2010, p. 514-522.

Research output: Contribution to journalArticleScientificpeer-review

TY - JOUR

T1 - Blind source separation in diffuse reflectance NIR spectroscopy using independent component analysis

AU - Toiviainen, Maunu

AU - Corona, F.

AU - Paaso, Janne

AU - Teppola, Pekka

PY - 2010

Y1 - 2010

N2 - Near‐infrared (NIR) spectroscopy permits non‐contact analysis of solid samples in the diffuse reflectance (DR) measurement mode. However, uncontrolled physical variations between solid samples, such as changes in packing density and particle size distribution, have a complex nonlinearizing effect on the NIR spectra which complicates the extraction of chemical information from data.Blind source separation (BSS) methods attempt to blindly factorize the measured mixture spectra into the pure analyte spectra and their concentration profiles. The physical interferences, however, make the application of BSS methods difficult on the NIR spectra of solids. The application of independent component analysis (ICA) on NIR DR spectra is discussed, and a three‐phase preprocessing procedure of the measured spectral signals designed to improve the separation capability of ICA is proposed in this work. The method involves the removal of nonlinear effects from the measured spectra using scatter correction, denoising with rank reduction and alteration of the sample statistics of the signals via differentiation with respect to the wavelength. The procedure is tested and the explanatory power of BSS is demonstrated using a laboratory data set comprising ternary mixtures of pharmaceutical powders.

AB - Near‐infrared (NIR) spectroscopy permits non‐contact analysis of solid samples in the diffuse reflectance (DR) measurement mode. However, uncontrolled physical variations between solid samples, such as changes in packing density and particle size distribution, have a complex nonlinearizing effect on the NIR spectra which complicates the extraction of chemical information from data.Blind source separation (BSS) methods attempt to blindly factorize the measured mixture spectra into the pure analyte spectra and their concentration profiles. The physical interferences, however, make the application of BSS methods difficult on the NIR spectra of solids. The application of independent component analysis (ICA) on NIR DR spectra is discussed, and a three‐phase preprocessing procedure of the measured spectral signals designed to improve the separation capability of ICA is proposed in this work. The method involves the removal of nonlinear effects from the measured spectra using scatter correction, denoising with rank reduction and alteration of the sample statistics of the signals via differentiation with respect to the wavelength. The procedure is tested and the explanatory power of BSS is demonstrated using a laboratory data set comprising ternary mixtures of pharmaceutical powders.

KW - near-infrared spectroscopy

KW - blind source separation

KW - independent component analysis

KW - spectral preprocessing

U2 - 10.1002/cem.1316

DO - 10.1002/cem.1316

M3 - Article

VL - 24

SP - 514

EP - 522

JO - Journal of Chemometrics

JF - Journal of Chemometrics

SN - 0886-9383

IS - 7-8

ER -