baselineCorrection {TargetSearch} | R Documentation

## Baseline correction algorithm

### Description

Functions for baseline correction of GC-MS chromatograms.

### Usage

        baselineCorrection(int, threshold = 0.5, alpha = 0.95, bfraction = 0.2,
               segments = 100, signalWindow = 10, method = "linear")

        baseline(ncData, baseline.opts = NULL)

### Arguments

`int` | A matrix object of spectra peak intensities to be baseline corrected, where the columns are retention times and rows mass traces.
`threshold` | A numeric value between 0 and 1. A value of one sets the baseline above the noise, 0.5 in the middle of the noise and 0 below the noise.
`alpha` | The alpha parameter of the high pass filter.
`bfraction` | The percentage of the fragments with the lowest intensities of the filtered signal that are assumed to be baseline signal.
`segments` | The number of segments in which the filtered signal is divided.
`signalWindow` | The window size (number of points) used in the signal windowing step.
`method` | The method used to approximate the baseline. `"linear"` (default) uses linear interpolation. `"spline"` fits a cubic smoothing spline (warning: really slow).
`ncData` | A list returned by the function `TargetSearch:::.open.ncdf`

.

`baseline.opts` | A list with parameters to be passed to `baselineCorrection` function. For example `baseline.opts = list(threshold = 0.5, alpha = 0.95)`.

### Details

The baseline correction algorithm is based on the work of Chang et al, and it works as follows. For every mass trace, i.e., rows of matrix `int`, the signal intensity is filtered by a first high pass filter: y[i] = alpha * (y[i-1] + x[i] - x[i-1]). The filtered signal is divided into evenly spaced segments (`segments`) and the standard deviation of each segment is calculated. A percentage (`bfraction`) of the segments with the lowest values are assumed to be baseline signal and the standard deviation (stdn) of the points within those segments is calculated.

Once stdn has been determined, the points with absolute filtered values larger than 2 * stdn are considered signal. After that, the signal windowing step takes every one of the points found to be signal as the center of a signal window (`signalWindow)` and marks the points within that window as signal. The remaining points are now considered to be noise.

The baseline signal is obtained by either using linear interpolation (default) or fitting a cubic smoothing spline taking only the noise. The baseline can be shifted up or down by using the parameter (threshold), which is done by the formula: B' = B + 2*(threshold - 0.5)*2*stdn, where B is the fitted spline, stdn the standard deviation of the noise, and `threshold` a value between 0 and 1. Finally, the corrected signal is calculated by subtracting B' to the original signal.

The `baseline` function is called by the function `NetCDFPeakFinding` before the peak picking algorithm is performed. Since it is an internal function, it is not intended to be executed directly, but it is exported for debugging purposes.

### Value

A matrix of the same dimensions of `int` with the baseline corrected intensities.

### Author(s)

Alvaro Cuadros-Inostroza

### References

David Chang, Cory D. Banack and Sirish L. Shah, Robust baseline correction algorithm for signal dense NMR spectra. Journal of Magnetic Resonance 187 (2007) 288-292

### See Also

`RIcorrect`

[Package TargetSearch version 1.40.3 Index]