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Correct baseline of signal with peaks

* Yout* = msbackadj(

`X`

`Intensities`

`Yout`

`X`

`Intensities`

`WindowSizeValue`

`Yout`

`X`

`Intensities`

`StepSizeValue`

`Yout`

`X`

`Intensities`

`RegressionMethodValue`

`Yout`

`X`

`Intensities`

`EstimationMethodValue`

`Yout`

`X`

`Intensities`

`SmoothMethodValue`

`Yout`

`X`

`Intensities`

`QuantileValueValue`

`Yout`

`X`

`Intensities`

`PreserveHeightsValue`

`Yout`

`X`

`Intensities`

`ShowPlotValue`

`X` | Vector of separation-unit values for
a set of signals with peaks. The number of elements in the vector
equals the number of rows in the matrix .
The separation unit can quantify wavelength, frequency, distance,
time, or m/z depending on the instrument that generates the signal
data.`Intensities` |

`Intensities` | Matrix of intensity values for a set
of peaks that share the same separation-unit range. Each row corresponds
to a separation-unit value, and each column corresponds to either
a set of signals with peaks or a retention time. The number of rows
equals the number of elements in vector .`X` |

Use the following syntaxes with data from any separation technique that produces signal data, such as spectroscopy, NMR, electrophoresis, chromatography, or mass spectrometry.

adjusts
the variable baseline of a raw signal with peaks by following steps:* Yout* = msbackadj(

`X`

`Intensities`

Estimates the baseline within multiple shifted windows of width

`200`

separation unitsRegresses the varying baseline to the window points using a spline approximation

Adjusts the baseline of the peak signals supplied by

`Intensities`

calls * Yout* = msbackadj(

`X`

`Intensities`

`PropertyName`

`PropertyValue`

`msbackadj`

with optional properties
that use property name/property value pairs. You can specify one or
more properties in any order. Each `PropertyName`

```
```

specifies
the width for the shifting window. * Yout* =
msbackadj(

`X`

`Intensities`

`WindowSizeValue`

`WindowSizeValue`

`X`

`200`

(baseline point
estimated for windows with a width of `200`

separation
units).The result of this algorithm depends on carefully choosing the window size and the step size. Consider the width of your peaks in the signal and the presence of possible drifts. If you have wider peaks toward the end of the signal, you may want to use variable parameters.

specifies
the steps for the shifting window. The default value is * Yout* = msbackadj(

`X`

`Intensities`

`StepSizeValue`

`200`

separation
units (baseline point is estimated for windows placed every `200`

separation
units). `StepSizeValue`

can
also be a function handle. The function is evaluated at the respective
separation-unit values and returns the distance between adjacent windows.

specifies the method to regress the window estimated
points to a soft curve. Enter * Yout* = msbackadj(

`X`

`Intensities`

`RegressionMethodValue`

`'pchip'`

(shape-preserving
piecewise cubic interpolation), `'linear'`

(linear
interpolation), or `'spline'`

(spline interpolation).
The default value is `'pchip'`

.

specifies the method for finding the likely baseline
value in every window. Enter * Yout* = msbackadj(

`X`

`Intensities`

`EstimationMethodValue`

`'quantile'`

(quantile
value is set to `10`

%) or `'em'`

(assumes
a doubly stochastic model). With `em`

, every sample
is the independent and identically distributed (i.i.d.) draw of any
of two normal distributed classes (background or peaks). Because the
class label is hidden, the distributions are estimated with an Expectation-Maximization
algorithm. The ultimate baseline value is the mean of the background
class.

specifies
the method for smoothing the curve of estimated points and eliminating
the effects of possible outliers. Enter * Yout* = msbackadj(

`X`

`Intensities`

`SmoothMethodValue`

`'none'`

, `'lowess'`

(linear
fit), `'loess'`

(quadratic fit), `'rlowess'`

(robust
linear), or `'rloess'`

(robust quadratic fit). Default
is `'none'`

.

specifies the quantile value. The default value is * Yout* = msbackadj(

`X`

`Intensities`

`QuantileValueValue`

`0.10`

.

, when * Yout* = msbackadj(

`X`

`Intensities`

`PreserveHeightsValue`

`PreserveHeightsValue`

`true`

,
sets the baseline subtraction mode to preserve the height of the tallest
peak in the signal. The default value is `false`

and
peak heights are not preserved.

plots
the baseline-estimated points, the regressed baseline, and the original
signal. When you call * Yout* = msbackadj(

`X`

`Intensities`

`ShowPlotValue`

`msbackadj`

without output
arguments, the signal is plotted unless `ShowPlotValue`

`false`

.
When `ShowPlotValue`

`true`

,
only the first signal in `Intensities`

`ShowPlotValue`

`Intensities`

Load a MAT-file, included with the Bioinformatics Toolbox™ software, that contains some sample data.

`load sample_lo_res`

Adjust the baseline for a group of spectra and show only the third spectrum and its estimated background.

`YB = msbackadj(MZ_lo_res,Y_lo_res,'SHOWPLOT',3);`

Plot the estimated baseline for the fourth spectrum in

`Y_lo_res`

using an anonymous function to describe an m/z dependent parameter.`wf = @(mz) 200 + .001 .* mz; msbackadj(MZ_lo_res,Y_lo_res(:,4),'STEPSIZE',wf);`

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