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A Comparative Investigation of the Combined Effects of Pre-Processing, Wavelength Selection, and Regression Methods on Near-Infrared Calibration Model Performance

Lookup NU author(s): Emeritus Professor Julian Morris, Dr Suresh Thennadil



This is the authors' accepted manuscript of an article that has been published in its final definitive form by Sage Publications, Inc., 2017.

For re-use rights please refer to the publisher's terms and conditions.


Near-infrared (NIR) spectroscopy is being widely used in various fields ranging from pharmaceutics to the food industry for analyzing chemical and physical properties of the substances concerned. Its advantages over other analytical techniques include available physical interpretation of spectral data, nondestructive nature and high speed of measurements, and little or no need for sample preparation. The successful application of NIR spectroscopy relies on three main aspects: preprocessing of spectral data to eliminate nonlinear variations due to temperature, light scattering effects and many others, selection of those wavelengths that contribute useful information, and identification of suitable calibration models using linear/nonlinear regression . Several methods have been developed for each of these three aspects and many comparative studies of different methods exist for an individual aspect or some combinations. However, there is still a lack of comparative studies for the interactions among these three aspects, which can shed light on what role each aspect plays in the calibration and how to combine various methods of each aspect together to obtain the best calibration model. This paper aims to provide such a comparative study based on four benchmark data sets using three typical pre-processing methods, namely, orthogonal signal correction (OSC), extended multiplicative signal correction (EMSC) and optical path-length estimation and correction (OPLEC); two existing wavelength selection methods, namely, stepwise forward selection (SFS) and genetic algorithm optimization combined with partial least squares regression for spectral data (GAPLSSP); four popular regression methods, namely, partial least squares (PLS), least absolute shrinkage and selection operator (LASSO), least squares support vector machine (LS-SVM), and Gaussian process regression (GPR). The comparative study indicates that, in general, pre-processing of spectral data can play a significant role in the calibration while wavelength selection plays a marginal role and the combination of certain pre-processing, wavelength selection, and nonlinear regression methods can achieve superior performance over traditional linear regression-based calibration.

Publication metadata

Author(s): Wan J, Chen Yi-C, Morris AJ, Thennadi S

Publication type: Article

Publication status: Published

Journal: Applied Spectroscopy

Year: 2017

Volume: 71

Issue: 7

Pages: 1432-1446

Print publication date: 01/07/2017

Online publication date: 30/03/2017

Acceptance date: 23/01/2017

Date deposited: 09/01/2020

ISSN (print): 0003-7028

ISSN (electronic): 1943-3530

Publisher: Sage Publications, Inc.


DOI: 10.1177/0003702817694623


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