Review of the most common pre-processing techniques for near-infrared spectra

Pre-processing of near-infrared (NIR) spectral data has become an integral part of chemometrics modeling. The objective of the pre-processing is to remove physical phenomena in the spectra in order to improve the subsequent multivariate regression, classification model or exploratory analysis. The most widely used pre-processing techniques can be divided into two categories: scatter-correction methods and spectral derivatives. This review describes and compares the theoretical and algorithmic foundations of current pre-processing methods plus the qualitative and quantitative consequences of their application. The aim is to provide NIR users with better end-models through fundamental knowledge on spectral pre-processing.

[1]  H. H. Madden.  Comments on the Savitzky-Golay convolution method for least-squares-fit smoothing and differentiation of digital data , 1976 .

[2]  Andrew G. Glen,et al.  APPL , 2001 .

[3]  Søren Balling Engelsen,et al.  Rapid Spectroscopic Analysis of Marzipan—Comparative Instrumentation , 2004 .

[4]  Desire L. Massart,et al.  The robust normal variate transform for pattern recognition with near-infrared data , 1999 .

[5]  M. Forina,et al.  Multivariate calibration. , 2007, Journal of chromatography. A.

[6]  Søren Balling Engelsen,et al.  Use of NIR spectroscopy and chemometrics for on-line process monitoring of ammonia in Low Methoxylated Amidated pectin production , 2005 .

[7]  Thomas A. Blake,et al.  Application of extended inverse scatter correction to mid‐infrared reflectance spectra of soil , 2005 .

[8]  A. Proctor,et al.  Smoothing of digital x-ray photoelectron spectra by an extended sliding least-squares approach , 1980 .

[9]  E. Martin,et al.  Empirical preprocessing methods and their impact on NIR calibrations: a simulation study , 2005 .

[10]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[11]  R. Barnes,et al.  Standard Normal Variate Transformation and De-Trending of Near-Infrared Diffuse Reflectance Spectra , 1989 .

[12]  Harald Martens,et al.  Near-Infrared Spectra of Penicillium Camemberti Strains Separated by Extended Multiplicative Signal Correction Improved Prediction of Physical and Chemical Variations , 2005, Applied spectroscopy.

[13]  Tormod Næs,et al.  Related versions of the multiplicative scatter correction method for preprocessing spectroscopic data , 1995 .

[14]  H. Martens,et al.  Light scattering and light absorbance separated by extended multiplicative signal correction. application to near-infrared transmission analysis of powder mixtures. , 2003, Analytical chemistry.

[15]  Rasmus Bro,et al.  Loopy MSC: A Simple Way to Improve Multiplicative Scatter Correction , 2008, Applied spectroscopy.

[16]  P. A. Gorry.  General least-squares smoothing and differentiation by the convolution (Savitzky-Golay) method , 1990 .

[17]  P. Geladi,et al.  Linearization and Scatter-Correction for Near-Infrared Reflectance Spectra of Meat , 1985 .

[18]  Hein Putter,et al.  The bootstrap: a tutorial , 2000 .

[19]  Bruce R. Kowalski,et al.  Piece-Wise Multiplicative Scatter Correction Applied to Near-Infrared Diffuse Transmittance Data from Meat Products , 1993 .

[20]  A. Savitzky,et al.  Smoothing and Differentiation of Data by Simplified Least Squares Procedures. , 1964 .

[21]  H. Martens,et al.  Near-Infrared Absorption and Scattering Separated by Extended Inverted Signal Correction (EISC): Analysis of Near-Infrared Transmittance Spectra of Single Wheat Seeds , 2002 .