Spectral Pre-processing - Revision history

Laschp: /* Reduce resolution */

2025-04-06T19:04:19Z

Reduce resolution

← Older revision		Revision as of 21:04, 6 April 2025
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	Normalization does not require parameters. The normalization function is available from the ''Preprocessing'' pulldown menu or via the button normalize in the ''PREPROCESS'' tab.		Normalization does not require parameters. The normalization function is available from the ''Preprocessing'' pulldown menu or via the button normalize in the ''PREPROCESS'' tab.

	== Reduce resolution ==		== Reduce resolution (binning) ==

	:[[File:redes.jpg\|\|right\|frame\|Screenshot of the window ''reduce resolution'']]		:[[File:redes.jpg\|\|right\|frame\|Screenshot of the window ''reduce resolution'']]

Laschp: /* Baseline subtraction */

2025-03-27T11:15:46Z

Baseline subtraction

← Older revision		Revision as of 13:15, 27 March 2025
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	== Baseline subtraction ==		== Baseline subtraction ==

			:[[File:baselinecorr.jpg\|right\|frame\|Screenshot of the dialog box ''baseline correction'']]

	The function divides a spectrum into segments, or intervals, for each of which a minimum MS intensity value is determined. These values are then used to generate a baseline correction curve (by shape preserving piecewise cubic interpolation). The interpolated baseline correction curves are subtracted from the original spectra.		The function divides a spectrum into segments, or intervals, for each of which a minimum MS intensity value is determined. These values are then used to generate a baseline correction curve (by shape preserving piecewise cubic interpolation). The interpolated baseline correction curves are subtracted from the original spectra.

Laschp: /* Cut */

2025-03-27T11:11:13Z

Cut

← Older revision		Revision as of 13:11, 27 March 2025
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	[[File:cut.jpg\|\|right\|frame\|Screenshot of the window ''cut mass spectra'']]		[[File:cut.jpg\|\|right\|frame\|Screenshot of the window ''cut mass spectra'']]
	Cutting (truncating) mass spectra is useful to narrow the m/z of the spectra. For large data sets this can be useful to free up some memory before performing memory consuming calculations. Define the mass range to be retained [m/z], then press the ''cut'' button to start the function. Quit the function by pressing the ''cancel'' button.		Cutting (truncating) mass spectra is useful to narrow the m/z range covered by the mass spectra. For large data sets this can be useful to free up some memory before performing memory consuming calculations. Define the mass range to be retained [m/z], then press the ''cut'' button to start the function. Quit the function by pressing the ''cancel'' button.
	If the check box ''org. spectra'' is checked (see the VIEW tab in the main MicrobeMS user interface), ''cut'' will overwrite existing pre-processed spectra without warning. If no pre-processed spectra exist, ''cut'' will create pre-processed spectra from original mass spectra. Check the checkbox ''cut original data'' to allow cutting also the original spectra. Note that cutted original spectra cannot be restored to their original state (see also the ''clear preprocessing'' function).		If the check box ''org. spectra'' is checked (see the VIEW tab in the main MicrobeMS user interface), ''cut'' will overwrite existing pre-processed spectra without warning. If no pre-processed spectra exist, ''cut'' will create pre-processed spectra from original mass spectra. Check the checkbox ''cut original data'' to allow cutting also the original spectra. Note that cutted original spectra cannot be restored to their original state (see also the ''clear preprocessing'' function).

Laschp: /* Normalization */

2025-03-27T11:06:43Z

Normalization

← Older revision		Revision as of 13:06, 27 March 2025
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	== Normalization ==		== Normalization ==

	This function normalizes mass spectra by means of a modified 1-norm algorithm. Normalization is carried out in the following way: The mass spectrum is first subdivided into bins, usually 100, in the m/z range between '''mzstart''' and '''mzstart''' + 3/5*('''mzend'''- '''mzstart'''). For example, if a spectrum spans the m/z ~~region~~ 2000 to 20000, the range between m/z 2000 and 12800 ~~will be~~ subdivided into 100 bins. The minimum and maximum intensity values are then obtained from each bin. The next steps are to calculate the intensity difference values for each individual m/z bin and to extract the 16 ~~topmost~~ intensity difference values. From these values the 3 largest values are deleted before the sum of the remaining 13 intensity difference values is calculated. Finally, this sum is divided by 1000~~. The sum serves~~ as the normalization factor.		This function normalizes mass spectra by means of a modified 1-norm algorithm. Normalization is carried out in the following way: The mass spectrum is first subdivided into bins, usually 100, in the m/z range between '''mzstart''' and '''mzstart''' + 3/5*('''mzend'''- '''mzstart'''). For example, if a spectrum spans the m/z range 2000 to 20000, the range between m/z 2000 and 12800 is subdivided into 100 bins. The minimum and maximum intensity values are then obtained from each bin. The next steps are to calculate the intensity difference values for each individual m/z bin and to extract the 16 highest intensity difference values. From these values the 3 largest values are deleted before the sum of the remaining 13 intensity difference values is calculated. Finally, this sum is divided by 1000 and the ratio is used as the normalization factor.
	Spectral normalization is achieved by dividing the spectral intensities by the normalization factor.<br>		Spectral normalization is achieved by dividing the spectral intensities by the normalization factor.<br>
	Normalization does not require parameters. The normalization function is available from the ''Preprocessing'' pulldown menu or via the button normalize in the ''PREPROCESS'' tab.		Normalization does not require parameters. The normalization function is available from the ''Preprocessing'' pulldown menu or via the button normalize in the ''PREPROCESS'' tab.

Laschp: /* Normalization */

2025-03-27T11:02:55Z

Normalization

← Older revision		Revision as of 13:02, 27 March 2025
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	== Normalization ==		== Normalization ==

	This function normalizes mass spectra by means of a modified 1-norm algorithm. Normalization is carried out in the following way: The mass spectrum is first subdivided into bins, usually 100, in the m/z range between '''mzstart''' and '''mzstart''' + 3/5*('''mzend'''- '''mzstart'''). For example, if a spectrum spans the m/z region 2000 to 20000, the range between m/z 2000 and 12800 will be subdivided into 100 bins. The minimum and maximum intensity values are then obtained from each bin. The next steps are to calculate the intensity difference values for each individual m/z bin and to extract 16 topmost intensity difference values. From these values the 3 ~~highest intensities~~ are deleted before the sum of the remaining 13 intensity difference values is calculated. Finally, this sum is ~~then~~ divided by 1000 ~~and can be can be subsequently applied~~ as the normalization factor.		This function normalizes mass spectra by means of a modified 1-norm algorithm. Normalization is carried out in the following way: The mass spectrum is first subdivided into bins, usually 100, in the m/z range between '''mzstart''' and '''mzstart''' + 3/5*('''mzend'''- '''mzstart'''). For example, if a spectrum spans the m/z region 2000 to 20000, the range between m/z 2000 and 12800 will be subdivided into 100 bins. The minimum and maximum intensity values are then obtained from each bin. The next steps are to calculate the intensity difference values for each individual m/z bin and to extract the 16 topmost intensity difference values. From these values the 3 largest values are deleted before the sum of the remaining 13 intensity difference values is calculated. Finally, this sum is divided by 1000. The sum serves as the normalization factor.
	Spectral normalization is achieved by dividing the spectral intensities by the normalization factor.<br>		Spectral normalization is achieved by dividing the spectral intensities by the normalization factor.<br>
	Normalization does not require parameters. The normalization function is available from the ''Preprocessing'' pulldown menu or via the button normalize in the ''PREPROCESS'' tab.		Normalization does not require parameters. The normalization function is available from the ''Preprocessing'' pulldown menu or via the button normalize in the ''PREPROCESS'' tab.

Laschp: /* Normalization */

2025-03-27T11:01:13Z

Normalization

← Older revision		Revision as of 13:01, 27 March 2025
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	== Normalization ==		== Normalization ==

	This function normalizes mass spectra. Normalization is carried out in the following way: The mass spectrum is first subdivided into bins, usually 100, in the m/z range between '''mzstart''' and '''mzstart''' + 3/5*('''mzend'''- '''mzstart'''). For example, if a spectrum spans the m/z region 2000 to 20000, the range between m/z 2000 and 12800 will be subdivided into 100 bins. The minimum and maximum intensity values are then obtained from each bin. The next steps are to calculate the intensity difference values for each individual m/z bin and to extract ~~the~~ 16 ~~most~~ intensity difference values. From these values the 3 highest intensities are deleted before the sum of the remaining 13 intensity difference values is calculated. Finally, this sum is then divided by 1000 and can be can be subsequently applied as the normalization factor.		This function normalizes mass spectra by means of a modified 1-norm algorithm. Normalization is carried out in the following way: The mass spectrum is first subdivided into bins, usually 100, in the m/z range between '''mzstart''' and '''mzstart''' + 3/5*('''mzend'''- '''mzstart'''). For example, if a spectrum spans the m/z region 2000 to 20000, the range between m/z 2000 and 12800 will be subdivided into 100 bins. The minimum and maximum intensity values are then obtained from each bin. The next steps are to calculate the intensity difference values for each individual m/z bin and to extract 16 topmost intensity difference values. From these values the 3 highest intensities are deleted before the sum of the remaining 13 intensity difference values is calculated. Finally, this sum is then divided by 1000 and can be can be subsequently applied as the normalization factor.
	Spectral normalization is achieved by dividing the spectral intensities by the normalization factor.<br>		Spectral normalization is achieved by dividing the spectral intensities by the normalization factor.<br>
	Normalization does not require parameters. The normalization function is available from the ''Preprocessing'' pulldown menu or via the button normalize in the ''PREPROCESS'' tab.		Normalization does not require parameters. The normalization function is available from the ''Preprocessing'' pulldown menu or via the button normalize in the ''PREPROCESS'' tab.

Laschp: /* Smoothing */

2025-03-27T10:56:09Z

Smoothing

← Older revision		Revision as of 12:56, 27 March 2025
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	== Smoothing ==		== Smoothing ==

	This function is used to smooth mass spectra using the Savitzky-Golay smoothing filter. Smoothing has a largely cosmetic effect on the spectra, reducing the noise at the expense of lowering the resolution of mass peaks. Possible values for smoothing points ~~are~~ 3 to 75. cf. popupmenu ''# smooth pts'' in the ''PREPROCESS'' tab of the main window of MicrobeMS. If the checkbox ''org. spectra'' is checked smoothing will create pre-processed spectra from original spectra. Note that existing pre-processed spectra will be overwritten without warning in this case. Press the ''smooth'' button to smooth selected mass spectra.		This function is used to smooth mass spectra using the Savitzky-Golay smoothing filter. Smoothing has a largely cosmetic effect on the spectra, reducing the noise at the expense of lowering the resolution of mass peaks. Possible values for smoothing points range between 3 and 75, cf. popupmenu ''# smooth pts'' in the ''PREPROCESS'' tab of the main window of MicrobeMS. If the checkbox ''org. spectra'' is checked smoothing will create pre-processed spectra from original spectra. Note that existing pre-processed spectra will be overwritten without warning in this case. Press the ''smooth'' button to smooth selected mass spectra.

	Details of the Savitzky-Golay algorithm can be found in the literature:		Details of the Savitzky-Golay algorithm can be found in the literature:

Laschp: /* Baseline subtraction */

2025-03-27T10:54:18Z

Baseline subtraction

← Older revision		Revision as of 12:54, 27 March 2025
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	The function divides a spectrum into segments, or intervals, for each of which a minimum MS intensity value is determined. These values are then used to generate a baseline correction curve (by shape preserving piecewise cubic interpolation). The interpolated baseline correction curves are subtracted from the original spectra.		The function divides a spectrum into segments, or intervals, for each of which a minimum MS intensity value is determined. These values are then used to generate a baseline correction curve (by shape preserving piecewise cubic interpolation). The interpolated baseline correction curves are subtracted from the original spectra.
	To perform baseline correction, first select the spectra to be corrected from the listbox in the top right corner ([[Screenshot_of_MicrobeMS\|Screenshot of MicrobeMS]]). Then select the number of intervals from the popup menu ''# of intervals'' in the ''VIEW'' tab. Valid values for the number of intervals (niv) are 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29, 31, 35, 45, 55, 65 and 75. Subsequently press the ''baseline'' button (''VIEW'' tab). If the checkbox ''org. spectra'' is selected baseline correction will create pre-processed spectra from original spectra. Note that existing pre-processed spectra will be overwritten without warning in this case. To perform baseline correction from existing pre-processed spectra, clear the ''org. spectra'' checkbox .		To perform baseline correction, first select the spectra to be corrected from the listbox in the top right corner ([[Screenshot_of_MicrobeMS\|Screenshot of MicrobeMS]]). Then select the number of intervals from the popup menu ''# of intervals'' in the ''VIEW'' tab. Valid values for the number of intervals (''niv'') are 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29, 31, 35, 45, 55, 65 and 75. Subsequently press the ''baseline'' button (''VIEW'' tab). If the checkbox ''org. spectra'' is selected baseline correction will create pre-processed spectra from original spectra. Note that existing pre-processed spectra will be overwritten without warning in this case. To perform baseline correction from existing pre-processed spectra, clear the ''org. spectra'' checkbox .

	Baseline correction should be repeated from the original spectra if negative intensities occur after baseline correction. For this purpose the checkbox ''org. spectra'' should be ticked and the parameter ''# of intervals'' should be reduced to 60 or less. Again, existing pre-processed spectra will be overwritten without warning.		Baseline correction should be repeated from the original spectra if negative intensities occur after baseline correction. For this purpose the checkbox ''org. spectra'' should be ticked and the parameter ''# of intervals'' should be reduced to 60 or less. Again, existing pre-processed spectra will be overwritten without warning.

Laschp: /* Autocalibration */

2025-03-27T10:53:28Z

Autocalibration

← Older revision		Revision as of 12:53, 27 March 2025
Line 10:		Line 10:
	:[[File:autocalibrate.jpg\|right\|frame\|Screenshot of the dialog box ''autocalibration'']]		:[[File:autocalibrate.jpg\|right\|frame\|Screenshot of the dialog box ''autocalibration'']]

	Performs a linear recalibration of ~~the~~ mass spectra ~~by utilizing~~ internal calibration peaks for which ~~the~~ m/z positions are ~~known~~. Note that non-linear methods are not available. Auto-calibration overwrites existing pre-processed spectra and modifies also existing peak tables. If no pre-processed spectra are available auto-calibration will create pre-processed spectra from the original mass spectra, i.e. the original spectra are not modified by this function.<br>		Performs linear recalibration of mass spectra using internal calibration peaks for which precise m/z positions are required. Note that non-linear methods are not available. Auto-calibration overwrites existing pre-processed spectra and modifies also existing peak tables. If no pre-processed spectra are available auto-calibration will create pre-processed spectra from the original mass spectra, i.e. the original spectra are not modified by this function.<br>
	The spectral pre-processing procedure of auto-calibration is based on the use of analyte mass peaks, i.e. it requires the knowledge of the precise peak positions of at least two different sample peaks per mass spectrum. To perform auto-calibration, it is necessary to generate peak tables from appropriately pre-processed mass spectra. After peak detection, select the spectra to be calibrated and choose ''autocalibrate'' from the ''Preprocessing'' pulldown menu.		The spectral pre-processing procedure of auto-calibration is based on the use of analyte mass peaks, i.e. it requires the knowledge of the precise peak positions of at least two different sample peaks per mass spectrum. To perform auto-calibration, it is necessary to generate peak tables from appropriately pre-processed mass spectra. After peak detection, select the spectra to be calibrated and choose ''autocalibrate'' from the ''Preprocessing'' pulldown menu.

Laschp: /* Autocalibration */

2025-03-27T10:51:33Z

Autocalibration

← Older revision		Revision as of 12:51, 27 March 2025
Line 10:		Line 10:
	:[[File:autocalibrate.jpg\|right\|frame\|Screenshot of the dialog box ''autocalibration'']]		:[[File:autocalibrate.jpg\|right\|frame\|Screenshot of the dialog box ''autocalibration'']]

	Performs a linear recalibration of the mass spectra. Note that non-linear methods are not available. Auto-calibration overwrites existing pre-processed spectra and modifies also existing peak tables. If no pre-processed spectra are available auto-calibration will create pre-processed spectra from the original mass spectra, i.e. the original spectra are not modified by this function.<br>		Performs a linear recalibration of the mass spectra by utilizing internal calibration peaks for which the m/z positions are known. Note that non-linear methods are not available. Auto-calibration overwrites existing pre-processed spectra and modifies also existing peak tables. If no pre-processed spectra are available auto-calibration will create pre-processed spectra from the original mass spectra, i.e. the original spectra are not modified by this function.<br>
	The spectral pre-processing procedure of auto-calibration is based on the use of analyte mass peaks, i.e. it requires the knowledge of the precise peak positions of at least two different sample peaks per mass spectrum. To perform auto-calibration, it is necessary to generate peak tables from appropriately pre-processed mass spectra. After peak detection, select the spectra to be calibrated and choose ''autocalibrate'' from the ''Preprocessing'' pulldown menu.		The spectral pre-processing procedure of auto-calibration is based on the use of analyte mass peaks, i.e. it requires the knowledge of the precise peak positions of at least two different sample peaks per mass spectrum. To perform auto-calibration, it is necessary to generate peak tables from appropriately pre-processed mass spectra. After peak detection, select the spectra to be calibrated and choose ''autocalibrate'' from the ''Preprocessing'' pulldown menu.