Algorithms for high-level data processing in gas chromatography

Hippe, Zdzisław S.; Bierowska, A.; Pietryga, T.

doi:10.1016/s0003-2670(01)83224-4

Cited by 10 publications

(3 citation statements)

References 28 publications

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“…This method could be said to have antecedents in minimum search and interpolation methods based on the identification of local and global minima in different successive segments of a spectrum and in methods based on the means in different successive segments of a spectrum. 6,13,19,20,22,23,25,29,36 Lewis and Chatwin 84 also used segment minima to estimate the position of the baseline. In later work a median filter has been used by Keselbrener et al 85 Theory.…”

Section: Methods Requiring No Explicit Knowledge Of P B or Nmentioning

confidence: 99%

“…21 TBC involves automatic peak recognition using the squared derivative of the spectrum, polynomial fitting to the remaining spectral regions to obtain an estimate of the baseline, and subsequent removal of the fit from the original spectrum to produce a baseline-corrected result. Some earlier 8,13,16,17,19,20,25 and later works 30,36,38,41,84,99,100 employ one or more similar concepts. The feature-based classification method by Rouh et al, 96 based on pattern classification principles, 101 is also in many respects conceptually related to TBC.…”

Section: Methods Requiring Explicit Estimates Of Bmentioning

confidence: 99%

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Investigation of Selected Baseline Removal Techniques as Candidates for Automated Implementation

et al. 2005

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Observed spectra normally contain spurious features along with those of interest and it is common practice to employ one of several available algorithms to remove the unwanted components. Low frequency spurious components are often referred to as 'baseline', 'background', and/or 'background noise'. Here we examine a cross-section of non-instrumental methods designed to remove background features from spectra; the particular methods considered here represent approaches with different theoretical underpinnings. We compare and evaluate their relative performance based on synthetic data sets designed to exemplify vibrational spectroscopic signals in realistic contexts and thereby assess their suitability for computer automation. Each method is presented in a modular format with a concise review of the underlying theory, along with a comparison and discussion of their strengths, weaknesses, and amenability to automation, in order to facilitate the selection of methods best suited to particular applications.

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Section: Methods Requiring No Explicit Knowledge Of P B or Nmentioning

confidence: 99%

Section: Methods Requiring Explicit Estimates Of Bmentioning

confidence: 99%

Investigation of Selected Baseline Removal Techniques as Candidates for Automated Implementation

et al. 2005

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“…Among others, Hedegus and Petersen, 1 Hippe et al, 2 Woerlee and Mol 3 and Kipiniak 4 have interpolated the baseline using a straight line. Caesar and Klier 5 have advocated interpolating the baseline using a quadratic spline and Goldberg 6 has proposed a cubic spline.…”

Section: Introductionmentioning

confidence: 99%

The effect of different baseline estimators on the limit of quantification in chromatography

McNulty

MacFie

1997

J. Chemometrics

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INTRODUCTIONChromatography is an important tool in analytical chemistry for identifying and quantifying the constituents of a chemical mixture. A typical chromatographic signal consists of a series of Gaussian-like peaks superimposed on a noisy background, (chromatogram = peaks + baseline + noise). The peaks represent the constituents of the chemical mixture while the background (baseline + noise) represents the physicochemical environment used to generate the chromatogram. The baseline is special in the sense that it is the notional zero from which all other points are measured and thus defines the chromatographic frame of reference.Although the baseline defines the chromatographic frame of reference, the baseline is obscured by both peaks and noise and needs to be estimated. A baseline can be estimated using interpolation, curve fitting or filtering.Interpolation methods estimate the value of a function between fixed points. When the magnitude of a peak is negligible (compared with the noise) at the detected start and end of the CCC SUMMARYThe ASC guidelines published in 1980 (ASC Committee on Environmental Improvement, 'Guidelines for data acquisition and data quality evaluation in environmental chemistry', Anal. Chem. 52, 2242-2249 (1980)) define the limit of quantification between a signal and its notional zero (baseline), i.e. the value of the signal without an analyte, as ten times the standard deviation of the signal's noise. If the value of the blank signal is estimated, the limit of quantification is dependent upon the variability of the estimated baseline rather than a simple multiple of the noise. It is shown that the limit of peak quantification (via numerical integration) of a Gaussian peak on an arbitrary background is strongly dependent upon the baseline estimator. In particular a peak should have a signal-to-noise ratio of approximately 60 : 1 using a linear baseline estimator and 10 : 1 for a cubic baseline estimator. Further, the expected standard deviation of peak parameters obtained via curve fitting is inflated by a factor of seven for a linear baseline estimator and two for a cubic baseline estimator.

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