Polynomial baseline correction

For physically uniform samples, simple techniques such as offset or polynomial baseline correction may suffice to minimize the physical/optical artifacts from the spectral data, but there are also a number of more advanced techniques available, which are quite effective. For example, it is common to convert the spectral data to a Savitzky-Golay33... 

When the beam enters the pyrolysis cell, it is focused directly above the platinum filament of the pyrolyser. An IBM PS2 computer was used to collect the data obtained. The spectra were baseline corrected using a polynomial baseline correction routine. Sample sizes ranged from 100 to 500 pm. 

More complex means of attacking the particle size question have also been attempted. These include experiments using mathematical modeling for simultaneous removal of particle size and water [29], the use of Fourier deconvolution [30], multiplicative scatter corrections [31 ], and principal components elimination [32]. Barnes et al. [33] introduced a procedure termed detrending that uses standard normal variate (SNV) with polynomial baseline correction [34]. These corrections for particle size may not always improve accuracy of NIRS analysis for two reasons. First, none of these procedures does a perfect job of removing particle size effects independent of absorption information. Second, particle size may be useful information in the calibration even though linear mathematics is used to derive the analytical equation. 

Gan, F. Ruan, G. Mo, J. (2006). Baseline correction by improved iterative polynomial fitting with automatic threshold. Chemometrics and Intelligent Laboratory Systems, Vol.82, No.l (May 2006), pp. 59-65, ISSN 0169-7439... 

The original algorithm may be extended to ensure the obtention of corrected results in the case in which the sample spectrum contains, besides the chemical components represented in the standard spectra, a G degree polynomial baseline in relation to the wavelength. The spectrum of the sample is thus considered to consist of the spectra of the standard solutions... 

Methods used for preliminary examination of the data included smoothing the spectral data, multiplicative scatter correction, standard normal variance correction, baseline correction, and first- or second-derivative transformation of log (1/T) data. The smoothing and derivative transformations were based on the Savitzki-Golay second-order polynomial filter (22). 

Many automatic baseline correction routines that may be applied without operator intervention are available. These routines may be applied by default for operations such as spectral searching. Automatic basehne functions typically use linear or polynomial baseline fits in regions of the spectmm where no peaks are detected. 

When the polynomial is fitted to signal points, which are manually selected, that can be attributed only to baseline (background), then baseline (background) removal is achieved and the drawbacks of detrending are overcome. A variant for baseline correction is to do adopt a weighted least squares automatic procediue (asymmetric least squares [63]). This is an automatic approach to determine which points most likely belong to baseline only, by... 

The BLC performs several different types of baseline leveling operations on a spectrum two-point level, multipoint level, function fit, interactive polynomial, or auto level. These five methods are capable of baseline correcting a wide variety of data. Note that in the two-point level and the multipoint level methods, the baseline is leveled at a value that is the average of the baseline points. This allows the methods to be applied to both transmission and absorbance spectra. For the interpretation of both nonpolarized and polarized IR spectra, the two-point and the multipoint methods are usually used. 

Phase 2 - data preprocessing. There are many ways to process spectral data prior to multivariate image reconstruction and there is no ideal method that can be generally applied to all types of tissue. It is usual practice to correct the baseline to account for nonspecific matrix absorptions and scattering induced by the physical or bulk properties of the dehydrated tissue. One possible procedure is to fit a polynomial function to a preselected set of minima points and zero the baseline to these minima points. However, this type of fit can introduce artifacts because baseline variation can be so extreme that one set of baseline points may not account for all types of baseline variation. A more acceptable way to correct spectral baselines is to use the derivatives of the spectra. This can only be achieved if the S/N of the individual spectra is high and if an appropriate smoothing factor is introduced to reduce noise in the derivatized spectra. Derivatives serve two purposes they minimize broad... 

The program fits the intensity at these point values to a polynomial (up to 5th order) function and then subtracts the polynomial function from the whole dataset. This is repeated for each ID slice (row or column) of the 2D data matrix. More sophisticated methods calculate the baseline points automatically and use functions other than polynomials. For example, a program called FLATT (by Kurt Wiithrich) is very effective at removing horizontal or vertical streaks resulting from baseline curvature in rows or columns of the data matrix. Especially with NOESY and ROESY databaseline correction is essential to getting clean 2D displays and plots. 

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