Background correction in near-infrared spectra of plant extracts by orthogonal signal correction

In near-infrared (NIR) analysis of plant extracts, excessive background often exists in near-infrared spectra. The detection of active constitutents is difficult because of excessive background, and correction of this problem remains difficult. In this work, the orthogonal signal correction (OSC) method was used to correct excessive background. The method was also compared with several classical background correction methods, such as offset correction, multiplicative scatter correction (MSC), standard normal variate (SNV) transformation, de-trending (DT), first derivative, second derivative and wavelet methods. A simulated dataset and a real NIR spectral dataset were used to test the efficiency of different background correction methods. The results showed that OSC is the only effective method for correcting excessive background.

[1]  Steven D. Brown,et al.  Robust Calibration with Respect to Background Variation , 2001 .

[2]  D. Massart,et al.  The influence of data pre-processing in the pattern recognition of excipients near-infrared spectra. , 1999, Journal of Pharmaceutical and Biomedical Analysis.

[3]  Tom Fearn,et al.  On orthogonal signal correction , 2000 .

[4]  J. Nicholson,et al.  Application of chemometrics to 1H NMR spectroscopic data to investigate a relationship between human serum metabolic profiles and hypertension. , 2003, The Analyst.

[5]  D. Kell,et al.  An introduction to wavelet transforms for chemometricians: A time-frequency approach , 1997 .

[6]  Israel Schechter,et al.  Correction for nonlinear fluctuating background in monovariable analytical systems , 1995 .

[7]  Lauri Niinistö,et al.  Application of PLS multivariate calibration for the determination of the hydroxyl group content in calcined silica by DRIFTS , 2000 .

[8]  David E. Booth,et al.  Chemometrics: Data Analysis for the Laboratory and Chemical Plant , 2004, Technometrics.

[9]  Age K. Smilde,et al.  Direct orthogonal signal correction , 2001 .

[10]  R. Bonner,et al.  Application of wavelet transforms to experimental spectra : Smoothing, denoising, and data set compression , 1997 .

[11]  Olof Svensson,et al.  An evaluation of orthogonal signal correction applied to calibration transfer of near infrared spectra , 1998 .

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

[13]  S. Wold,et al.  Orthogonal projections to latent structures (O‐PLS) , 2002 .

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

[15]  S. Wold,et al.  Orthogonal signal correction of near-infrared spectra , 1998 .

[16]  Jianguo Sun,et al.  Statistical analysis of NIR data: data pretreatment , 1997 .

[17]  Zhanxia Zhang,et al.  Application of wavelet transform to background correction in inductively coupled plasma atomic emission spectrometry , 2003 .

[18]  Steven D. Brown,et al.  Wavelet analysis applied to removing non‐constant, varying spectroscopic background in multivariate calibration , 2002 .

[19]  Olav M. Kvalheim,et al.  Multivariate prediction and background correction using local modeling and derivative spectroscopy , 1991 .

[20]  Y. Tahboub,et al.  Evaluation of multiwavelength first- and second-derivative spectra for the quantitation of mixtures of polynuclear aromatic hydrocarbons , 1985 .