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Support vector regression with digital band pass filtering for the quantitative analysis of near‐infrared spectra
Author(s) -
Malik Bilal,
Chaitanya Krishna,
Benaissa Mohammed
Publication year - 2014
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.2580
Subject(s) - filter (signal processing) , calibration , computer science , gaussian filter , frequency domain , support vector machine , high pass filter , chebyshev filter , algorithm , mathematics , artificial intelligence , low pass filter , statistics , computer vision , image (mathematics)
This paper proposes a novel calibration technique based on combining support vector regression with a digital band pass (DBP) filter for the quantitative analysis of near‐infrared spectra. The efficacy of the proposed method is investigated and validated in the determination of glucose from near‐infrared spectra of a mixture composed of urea, triacetin and glucose. In this paper, the DBP filtering was implemented as a pre‐processing technique in the frequency domain as a Gaussian band pass filter and in the time domain as a Chebyshev filter. The grid‐search optimization method was used to optimize the filter parameters. The results demonstrate that utilization of the optimized DBP filters as a pre‐processing technique improved the performance of the predictive models. Copyright © 2013 John Wiley & Sons, Ltd.