2D-COS Wiki - User contributions [en]

Options of the 2D-COS Main Figure

2025-04-10T08:09:55Z

Laschp: /* Menu bar options */

[[File:2dcos-gui.jpg|400px|thumb|mat2dcorr: screenshot of the main figure]]
[[File:2dcos-x-trace.jpg|400px|thumb|mat2dcorr: screenshot of a 1D correlation slice produced by mat2dcorr]]
[[File:xy-feature-plot.jpg|400px|thumb|mat2dcorr: screenshot of a [x,y] feature plot window]]

== Menu bar options ==

'''File'''
: '''''save 2D spectrum''''' - stores the 2D spectrum and corresponding metadata, for details see section [[Format_of_a_2D-COS_Result_File|Format of a 2D-COS result file (Matlab)]]
: '''''clear''''' - data are deleted from memory and figures are set back to their initial state
: '''''quit''''' - exit the program

'''Load data'''
: '''''Matlab imaging forma'''''t: see [[Matlab_Imaging_Format|Import data in the imaging format (Matlab)]]
: '''''Matlab trace format''''': see [[Matlab_Trace_Format|Import data in the trace format (Matlab)]]
: '''''Excel data format''''': see [[Excel_Trace_Format|Import data in the MS Excel data format]]

'''Action'''
: '''''plot 2D spectrum''''': computes the 2D correlation spectrum using the actual settings of the [[Options_of_the_2D-COS_Control_Window| 2D-COS control window]]
: '''''extrema of 2D fcn (function)''''': obtains the minimum and maximum intensities of the current 2D correlation spectrum
: '''''plot x-slice''''': plots a 1D correlation (x) slices at a fixed y-position using the setting from the edit field ''[x,y] slice coordinates'' (y) of the [[Options_of_the_2D-COS_Control_Window| 2D-COS control window]], see screenshot to the right
: '''''plot y-slice''''': plots a 1D correlation (y) slices at a fixed x-position using the setting from the edit field ''[x,y] slice coordinates'' (x) of the [[Options_of_the_2D-COS_Control_Window| 2D-COS control window]], see screenshot to the right
: '''''plot [x,y] spectral features''''': creates an [x,y] feature plot using the settings from the edit fields ''[x,y] slice coordinates'' of the [[Options_of_the_2D-COS_Control_Window| 2D-COS control window]], see screenshot to the right
: '''''invert colormap''''': inverts the colormap
: '''''save 2D function''''': the current color representation of the 2D correlation spectrum can be stored in one of the following image formats: bmp, tif, png, and jpg
: '''''save gui to file''''': the current main user interface, including the 2D correlation function and the x- and y-mean spectra, are allowed to store in one of the following formats: pdf, eps, (vector graphic formats) tif, png, jpg, (image formats) and send to clipboard (no data are stored)
: '''''display 2D-COS intensity (on/off)''''': chose ''on'' to enables the ''MouseOver'' functionality; the option is helpful to obtain the intensity of the 2D correlation spectrum at defined [x,y] positions   

== Context Menu ==

: '''''plot x-slice''''': plots a 1D correlation (x) slices at a fixed y-position using the setting from the edit field ''[x,y] slice coordinates'' (y) of the [[Options_of_the_2D-COS_Control_Window| 2D-COS control window]], see screenshot below
: '''''plot y-slice''''': plots a 1D correlation (y) slices at a fixed x-position using the setting from the edit field ''[x,y] slice coordinates'' (x) of the [[Options_of_the_2D-COS_Control_Window| 2D-COS control window]], see screenshot below
: '''''plot [x,y] spectral features''''': creates an [x,y] feature plot using the settings from the edit fields ''[x,y] slice coordinates'' of the [[Options_of_the_2D-COS_Control_Window| 2D-COS control window]], see screenshot below

Mat2dcorr - A Matlab Toolbox for 2D-COS

2025-04-10T08:07:26Z

Laschp:

__FORCETOC__

[[File:GraphAbstr.gif|400px|thumb|mat2dcorr: Illustration of heterospectral 2D-COS (FTIR vs. Raman)]]
[[File:2D-COS.jpg|400px|thumb|mat2dcorr: Screenshot of the [[Options_of_the_2D-COS_Control_Window| ''2D control window'']] (left) and the window [[Options_of_the_2D-COS_Main_Figure| '2D correlation analysis ... ']] (right)]]

mat2dcorr - a free toolbox for performing two-dimensional correlation (2D-COS) analysis with [https://www.mathworks.com Matlab]. 

== Introduction ==

Since its introduction in 1989, two-dimensional correlation spectroscopy (2D-COS), sometimes referred to as two-dimensional correlation analysis, has become an invaluable analytical tool for spectroscopic characterization in various fields of application, including protein science, pharmacy, biomedical applications, and polymer or nanomaterial research. The technique of 2D-COS analysis was originally developed to study dynamic processes in which a spectroscopic probe is used to monitor the effect of an external perturbation on a given system. The system responds to the perturbation with characteristic changes that are monitored spectroscopically, e.g. by continuous acquisition of a spectral time series. The main purpose of 2D-COS investigations is to explore correlations that may exist between perturbation-induced spectral responses. In some situations, the 2D-COS analysis technique is useful not only to detect and visualize such correlations, but also to decipher the sequence of these spectral changes. Sources of perturbation may include changes in temperature, pH, pressure, concentration, or spatial coordinates when examining spatially heterogeneous samples. The 2D correlation concept has proven to be extremely useful in a wide variety of molecular spectroscopy applications based on IR, NIR, Raman, NMR, fluorescence and UV-visible spectroscopy.

An introduction into the history, mathematics and basic principles of 2D-COS can be found here: https://en.wikipedia.org/wiki/Two-dimensional_correlation_analysis

== Getting Started ==
* [[Computer_Specification|Specification of computer configuration]]
* [[Screenshot_of_mat2dcorr|Screenshot of the mat2dcorr gui]]
* Downloading mat2dcorr - [[How_to_obtain_the_mat2dcorr_toolbox|How to obtain the mat2dcorr toolbox?]]
* [[Installation_of_the_mat2dcorr_toolbox|Installation of the mat2dcorr toolbox]]
* [[Disclaimer_and_License_Conditions |Disclaimer and license conditions]]
* [[mat2dcorr_-_Relevant Publications|Acknowledgement, relevant publications]]
* [[Frequently_Asked_Questions_(FAQ)|Frequently asked questions (FAQ)]]

== Data Import & File Formats ==

* [[Matlab_Imaging_Format|Import data in the CytoSpec imaging format (CytoSpec/Matlab)]]
* [[Matlab_Trace_Format|Import data in the trace format (Matlab)]]
* [[Excel_Trace_Format|Import data in the MS Excel data format]]
* [[Format_of_a_2D-COS_Result_File|Format of a 2D-COS result file (Matlab)]]

== Options of the mat2dcorr Toolbox ==

* [[Options_of_the_2D-COS_Main_Figure| Options of the 2D-COS main figure]]
* [[Options_of_the_2D-COS_Control_Window| Options of the 2D-COS control window]]

== Related Publications and Web Links ==

* [https://en.wikipedia.org/wiki/Two-dimensional_correlation_analysis Two-dimensional correlation analysis] (Wikipedia)

* I. Noda. [https://doi.org/10.1366/0003702904087398 Two-Dimensional Infrared (2D IR) Spectroscopy: Theory and Applications], '''1990''' ''Appl. Spectrosc''. 44(4): 550-561

* I. Noda. [https://doi.org/10.1366/0003702934067694 Generalized Two- Dimensional Correlation Method Applicable to Infrared, Raman, and other Types of Spectroscopy], '''1993''' ''Appl. Spectrosc''. 47(9): 1329-1336

* I. Noda. [https://doi.org/10.1366/0003702001950472 Determination of Two-Dimensional Correlation Spectra Using the Hilbert Transform], '''2000''' ''Appl. Spectrosc''. 54(7): 994-999

* P. Lasch & I. Noda. [https://doi.org/10.1021/acs.analchem.7b00332 Two-Dimensional Correlation Spectroscopy for Multimodal Analysis of FT-IR, Raman, and MALDI-TOF MS Hyperspectral Images with Hamster Brain Tissue]. '''2017''' ''Anal. Chem''. 89, 9, 5008–5016

* P. Lasch & I. Noda. [https://doi.org/10.1177/0003702818819880 Two-Dimensional Correlation Spectroscopy (2D-COS) for Analysis of Spatially Resolved Vibrational Spectra]. '''2019''' ''Appl. Spectrosc''. 73(4): 359-379

   

Apr 09, 2025: more details of the ''mat2dcorr'' Matlab toolbox will follow

Mat2dcorr - A Matlab Toolbox for 2D-COS

2025-04-10T08:05:00Z

Laschp:

__FORCETOC__

[[File:GraphAbstr.gif|400px|thumb|mat2dcorr: Illustration of heterospectral 2D-COS (FTIR vs. Raman)]]
[[File:2D-COS.jpg|400px|thumb|Mat2Dcorr: Screenshot of the [[Options_of_the_2D-COS_Control_Window| ''2D control window'']] (left) and the window [[Options_of_the_2D-COS_Main_Figure| '2D correlation analysis ... ']] (right)]]

mat2dcorr - a free toolbox for performing two-dimensional correlation (2D-COS) analysis with [https://www.mathworks.com Matlab]. 

== Introduction ==

Since its introduction in 1989, two-dimensional correlation spectroscopy (2D-COS), sometimes referred to as two-dimensional correlation analysis, has become an invaluable analytical tool for spectroscopic characterization in various fields of application, including protein science, pharmacy, biomedical applications, and polymer or nanomaterial research. The technique of 2D-COS analysis was originally developed to study dynamic processes in which a spectroscopic probe is used to monitor the effect of an external perturbation on a given system. The system responds to the perturbation with characteristic changes that are monitored spectroscopically, e.g. by continuous acquisition of a spectral time series. The main purpose of 2D-COS investigations is to explore correlations that may exist between perturbation-induced spectral responses. In some situations, the 2D-COS analysis technique is useful not only to detect and visualize such correlations, but also to decipher the sequence of these spectral changes. Sources of perturbation may include changes in temperature, pH, pressure, concentration, or spatial coordinates when examining spatially heterogeneous samples. The 2D correlation concept has proven to be extremely useful in a wide variety of molecular spectroscopy applications based on IR, NIR, Raman, NMR, fluorescence and UV-visible spectroscopy.

An introduction into the history, mathematics and basic principles of 2D-COS can be found here: https://en.wikipedia.org/wiki/Two-dimensional_correlation_analysis

== Getting Started ==
* [[Computer_Specification|Specification of computer configuration]]
* [[Screenshot_of_mat2dcorr|Screenshot of the mat2dcorr gui]]
* Downloading mat2dcorr - [[How_to_obtain_the_mat2dcorr_toolbox|How to obtain the mat2dcorr toolbox?]]
* [[Installation_of_the_mat2dcorr_toolbox|Installation of the mat2dcorr toolbox]]
* [[Disclaimer_and_License_Conditions |Disclaimer and license conditions]]
* [[mat2dcorr_-_Relevant Publications|Acknowledgement, relevant publications]]
* [[Frequently_Asked_Questions_(FAQ)|Frequently asked questions (FAQ)]]

== Data Import & File Formats ==

* [[Matlab_Imaging_Format|Import data in the CytoSpec imaging format (CytoSpec/Matlab)]]
* [[Matlab_Trace_Format|Import data in the trace format (Matlab)]]
* [[Excel_Trace_Format|Import data in the MS Excel data format]]
* [[Format_of_a_2D-COS_Result_File|Format of a 2D-COS result file (Matlab)]]

== Options of the mat2dcorr Toolbox ==

* [[Options_of_the_2D-COS_Main_Figure| Options of the 2D-COS main figure]]
* [[Options_of_the_2D-COS_Control_Window| Options of the 2D-COS control window]]

== Related Publications and Web Links ==

* [https://en.wikipedia.org/wiki/Two-dimensional_correlation_analysis Two-dimensional correlation analysis] (Wikipedia)

* I. Noda. [https://doi.org/10.1366/0003702904087398 Two-Dimensional Infrared (2D IR) Spectroscopy: Theory and Applications], '''1990''' ''Appl. Spectrosc''. 44(4): 550-561

* I. Noda. [https://doi.org/10.1366/0003702934067694 Generalized Two- Dimensional Correlation Method Applicable to Infrared, Raman, and other Types of Spectroscopy], '''1993''' ''Appl. Spectrosc''. 47(9): 1329-1336

* I. Noda. [https://doi.org/10.1366/0003702001950472 Determination of Two-Dimensional Correlation Spectra Using the Hilbert Transform], '''2000''' ''Appl. Spectrosc''. 54(7): 994-999

* P. Lasch & I. Noda. [https://doi.org/10.1021/acs.analchem.7b00332 Two-Dimensional Correlation Spectroscopy for Multimodal Analysis of FT-IR, Raman, and MALDI-TOF MS Hyperspectral Images with Hamster Brain Tissue]. '''2017''' ''Anal. Chem''. 89, 9, 5008–5016

* P. Lasch & I. Noda. [https://doi.org/10.1177/0003702818819880 Two-Dimensional Correlation Spectroscopy (2D-COS) for Analysis of Spatially Resolved Vibrational Spectra]. '''2019''' ''Appl. Spectrosc''. 73(4): 359-379

   

Apr 09, 2025: more details of the ''mat2dcorr'' Matlab toolbox will follow

Frequently Asked Questions (FAQ)

2025-04-10T07:26:11Z

Laschp: /* How to interpret intensity values of 2D correlation functions? */

__FORCETOC__

[[File:GraphAbstr.gif|400px|thumb|mat2dcorr: Illustration of heterospectral 2D-COS (FTIR vs. Raman)]]
== Can the mat2corr toolbox account for unevenly spaced sampling of spectra? ==

Unfortunately, the current version of the ''mat2dcorr'' toolbox (v. 1.05) does not consider non-equidistant vectors of the perturbing variable. However, this would be a good idea for future versions of the toolbox as an addition to its functionality.

In the literature, there are basically three different ways to deal with perturbations that do not fulfill the equidistance condition:
:1. Ignore the requirement for equidistant perturbation vectors and use the data as they are. This is what the current mat2dcorr toolbox version 1.05 does.
:2. Use modified correlation equations as described in [https://journals.sagepub.com/doi/10.1366/000370203322259039 Noda (2003)] to account for uneven sampling of the perturbation variable.
:3. Interpolate the perturbation values and the associated spectral data to get an equidistant distribution of the perturbation values.

Option 3 would be fairly easy to implement programmatically. It is already on the to-do list.

== I have loaded spectral data, but the buttons are still grayed out? ==

This is not a bug! It is important to understand that the mat2dcos toolbox was originally developed for heterospectral 2D correlation analysis (2D-COS). This means that spectral series from two different modalities are analyzed. For example, if IR and Raman data are to be analyzed by heterospectral 2D-COS, two different spectral series must be loaded into the toolbox.
For the probably more common case of autocorrelation 2D-COS, this means that the data set to be analyzed has to be loaded twice, once as an ''x-'' and a second time as a ''y-data'' set. Only then will the buttons be available for analysis.

== How to interpret intensity values of 2D correlation functions? ==

For example, the symmetric 2D function can be interpreted as a statistical value, namely the covariance between two variables. Unlike the correlation, the covariance is not normalized by the standard variation and can thus take very small or very large values. This depends, among other things, on the absolute values of the intensity changes in the spectra examined. For this reason, some researchers do not attach much importance to the absolute intensity values of the 2D-COS functions. It is more important that the maxima and minima of the 2D correlation functions are clearly above the noise, which can be checked by analyzing the correlation slices.

It is also noteworthy that the intensities of the 2D functions differ depending on the method used to calculate the 2D correlation functions (e.g. statistical or FFT based). This can be easily tested using the example data provided with the ''mat2dcorr'' toolbox. The different intensities obtained by the distinct 2D-COS functions are not due to programming errors and are not discussed in the scientific literature.

== Links to other non-commercial 2D-COS software solutions ==

* [https://sites.google.com/view/shigemorita/home/2dshige 2DShige], free software for two-dimensional correlation analysis developed by Prof. Shigeaki Morita (Osaka Electro-Communication University).
* [https://de.mathworks.com/matlabcentral/fileexchange/32384-midas-2010 MIDAS 2010], Matlab-based software tools developed in the Canadian Light Source for 2D spectroscopic analysis and data exploration of time resolved infrared spectra
* [https://arxiv.org/abs/1808.00685 corr2D (R)], - Implementation of two-dimensional correlation analysis in R, developed by Robert Geitner.

Frequently Asked Questions (FAQ)

2025-04-10T07:23:07Z

Laschp: /* Can the mat2corr toolbox account for unevenly spaced sampling of spectra? */

__FORCETOC__

[[File:GraphAbstr.gif|400px|thumb|mat2dcorr: Illustration of heterospectral 2D-COS (FTIR vs. Raman)]]
== Can the mat2corr toolbox account for unevenly spaced sampling of spectra? ==

Unfortunately, the current version of the ''mat2dcorr'' toolbox (v. 1.05) does not consider non-equidistant vectors of the perturbing variable. However, this would be a good idea for future versions of the toolbox as an addition to its functionality.

In the literature, there are basically three different ways to deal with perturbations that do not fulfill the equidistance condition:
:1. Ignore the requirement for equidistant perturbation vectors and use the data as they are. This is what the current mat2dcorr toolbox version 1.05 does.
:2. Use modified correlation equations as described in [https://journals.sagepub.com/doi/10.1366/000370203322259039 Noda (2003)] to account for uneven sampling of the perturbation variable.
:3. Interpolate the perturbation values and the associated spectral data to get an equidistant distribution of the perturbation values.

Option 3 would be fairly easy to implement programmatically. It is already on the to-do list.

== I have loaded spectral data, but the buttons are still grayed out? ==

This is not a bug! It is important to understand that the mat2dcos toolbox was originally developed for heterospectral 2D correlation analysis (2D-COS). This means that spectral series from two different modalities are analyzed. For example, if IR and Raman data are to be analyzed by heterospectral 2D-COS, two different spectral series must be loaded into the toolbox.
For the probably more common case of autocorrelation 2D-COS, this means that the data set to be analyzed has to be loaded twice, once as an ''x-'' and a second time as a ''y-data'' set. Only then will the buttons be available for analysis.

== How to interpret intensity values of 2D correlation functions? ==

For example, the symmetric 2D function can be interpreted as a statistical value, namely the covariance between two variables. Unlike the correlation, the covariance is not normalized by the standard variation and can thus take very small or very large values. This depends, among other things, on the absolute values of the intensity changes in the spectra examined. For this reason, some researchers do not attach much importance to the absolute intensity values of the 2D-COS functions. It is more important that the maxima and minima of the 2D correlation functions are clearly above the noise, which can be checked by analyzing the correlation slices.

It is also interesting to note that the intensities of the 2D functions differ depending on the method used to calculate the 2D correlation functions (statistical or FFT based). This can be easily tested using the example data provided with the ''mat2dcorr'' toolbox. The different intensities obtained by the distinct 2D-COS functions are not due to programming errors and are not discussed in the scientific literature.

== Links to other non-commercial 2D-COS software solutions ==

* [https://sites.google.com/view/shigemorita/home/2dshige 2DShige], free software for two-dimensional correlation analysis developed by Prof. Shigeaki Morita (Osaka Electro-Communication University).
* [https://de.mathworks.com/matlabcentral/fileexchange/32384-midas-2010 MIDAS 2010], Matlab-based software tools developed in the Canadian Light Source for 2D spectroscopic analysis and data exploration of time resolved infrared spectra
* [https://arxiv.org/abs/1808.00685 corr2D (R)], - Implementation of two-dimensional correlation analysis in R, developed by Robert Geitner.

Frequently Asked Questions (FAQ)

2025-04-09T14:05:59Z

Laschp:

__FORCETOC__

[[File:GraphAbstr.gif|400px|thumb|mat2dcorr: Illustration of heterospectral 2D-COS (FTIR vs. Raman)]]
== Can the mat2corr toolbox account for unevenly spaced sampling of spectra? ==

Unfortunately, the current version of the ''mat2dcorr'' toolbox (v. 1.05) does not consider non-equidistant vectors of the perturbing variable. However, this would be a good idea for future versions of the toolbox as an addition to its functionality.

In the literature, there are basically three different ways to deal with a perturbation that does not fulfil the equidistance condition:
:1. Ignore the requirement for equidistant perturbation values and use the data as they are. This is what the mat2dcorr toolbox v.1.05 does.
:2. Use modified correlation equations as described in [https://journals.sagepub.com/doi/10.1366/000370203322259039 Noda (2003)] to account for uneven sampling of the perturbation variable.
:3. Interpolate the perturbation values and the associated spectral data to get an equidistant distribution of the perturbation values.

Option 3 would be fairly easy to implement programmatically. It is already on the to-do list.

== I have loaded spectral data, but the buttons are still grayed out? ==

This is not a bug! It is important to understand that the mat2dcos toolbox was originally developed for heterospectral 2D correlation analysis (2D-COS). This means that spectral series from two different modalities are analyzed. For example, if IR and Raman data are to be analyzed by heterospectral 2D-COS, two different spectral series must be loaded into the toolbox.
For the probably more common case of autocorrelation 2D-COS, this means that the data set to be analyzed has to be loaded twice, once as an ''x-'' and a second time as a ''y-data'' set. Only then will the buttons be available for analysis.

== How to interpret intensity values of 2D correlation functions? ==

For example, the symmetric 2D function can be interpreted as a statistical value, namely the covariance between two variables. Unlike the correlation, the covariance is not normalized by the standard variation and can thus take very small or very large values. This depends, among other things, on the absolute values of the intensity changes in the spectra examined. For this reason, some researchers do not attach much importance to the absolute intensity values of the 2D-COS functions. It is more important that the maxima and minima of the 2D correlation functions are clearly above the noise, which can be checked by analyzing the correlation slices.

It is also interesting to note that the intensities of the 2D functions differ depending on the method used to calculate the 2D correlation functions (statistical or FFT based). This can be easily tested using the example data provided with the ''mat2dcorr'' toolbox. The different intensities obtained by the distinct 2D-COS functions are not due to programming errors and are not discussed in the scientific literature.

== Links to other non-commercial 2D-COS software solutions ==

* [https://sites.google.com/view/shigemorita/home/2dshige 2DShige], free software for two-dimensional correlation analysis developed by Prof. Shigeaki Morita (Osaka Electro-Communication University).
* [https://de.mathworks.com/matlabcentral/fileexchange/32384-midas-2010 MIDAS 2010], Matlab-based software tools developed in the Canadian Light Source for 2D spectroscopic analysis and data exploration of time resolved infrared spectra
* [https://arxiv.org/abs/1808.00685 corr2D (R)], - Implementation of two-dimensional correlation analysis in R, developed by Robert Geitner.

Frequently Asked Questions (FAQ)

2025-04-09T14:05:14Z

Laschp:

[[File:GraphAbstr.gif|400px|thumb|mat2dcorr: Illustration of heterospectral 2D-COS (FTIR vs. Raman)]]

__FORCETOC__

== Can the mat2corr toolbox account for unevenly spaced sampling of spectra? ==

Unfortunately, the current version of the ''mat2dcorr'' toolbox (v. 1.05) does not consider non-equidistant vectors of the perturbing variable. However, this would be a good idea for future versions of the toolbox as an addition to its functionality.

In the literature, there are basically three different ways to deal with a perturbation that does not fulfil the equidistance condition:
:1. Ignore the requirement for equidistant perturbation values and use the data as they are. This is what the mat2dcorr toolbox v.1.05 does.
:2. Use modified correlation equations as described in [https://journals.sagepub.com/doi/10.1366/000370203322259039 Noda (2003)] to account for uneven sampling of the perturbation variable.
:3. Interpolate the perturbation values and the associated spectral data to get an equidistant distribution of the perturbation values.

Option 3 would be fairly easy to implement programmatically. It is already on the to-do list.

== I have loaded spectral data, but the buttons are still grayed out? ==

This is not a bug! It is important to understand that the mat2dcos toolbox was originally developed for heterospectral 2D correlation analysis (2D-COS). This means that spectral series from two different modalities are analyzed. For example, if IR and Raman data are to be analyzed by heterospectral 2D-COS, two different spectral series must be loaded into the toolbox.
For the probably more common case of autocorrelation 2D-COS, this means that the data set to be analyzed has to be loaded twice, once as an ''x-'' and a second time as a ''y-data'' set. Only then will the buttons be available for analysis.

== How to interpret intensity values of 2D correlation functions? ==

For example, the symmetric 2D function can be interpreted as a statistical value, namely the covariance between two variables. Unlike the correlation, the covariance is not normalized by the standard variation and can thus take very small or very large values. This depends, among other things, on the absolute values of the intensity changes in the spectra examined. For this reason, some researchers do not attach much importance to the absolute intensity values of the 2D-COS functions. It is more important that the maxima and minima of the 2D correlation functions are clearly above the noise, which can be checked by analyzing the correlation slices.

It is also interesting to note that the intensities of the 2D functions differ depending on the method used to calculate the 2D correlation functions (statistical or FFT based). This can be easily tested using the example data provided with the ''mat2dcorr'' toolbox. The different intensities obtained by the distinct 2D-COS functions are not due to programming errors and are not discussed in the scientific literature.

== Links to other non-commercial 2D-COS software solutions ==

* [https://sites.google.com/view/shigemorita/home/2dshige 2DShige], free software for two-dimensional correlation analysis developed by Prof. Shigeaki Morita (Osaka Electro-Communication University).
* [https://de.mathworks.com/matlabcentral/fileexchange/32384-midas-2010 MIDAS 2010], Matlab-based software tools developed in the Canadian Light Source for 2D spectroscopic analysis and data exploration of time resolved infrared spectra
* [https://arxiv.org/abs/1808.00685 corr2D (R)], - Implementation of two-dimensional correlation analysis in R, developed by Robert Geitner.

Mat2dcorr - Relevant Publications

2025-04-09T14:04:38Z

Laschp:

[[File:GraphAbstr.gif|400px|thumb|mat2dcorr: Illustration of heterospectral 2D-COS (FTIR vs. Raman)]]

Two-dimensional correlation spectroscopy (2D-COS), or two-dimensional correlation analysis is known as a set of mathematical techniques useful to study changes in dynamic spectra. Dynamic spectra are often represented by spectra series obtained from a sample that was subjected to an external perturbation.   
The 2D-COS analysis technique has been initially developed by [https://en.wikipedia.org/wiki/Isao_Noda Isao Noda] in the 1980s.

Wikipedia link: [https://en.wikipedia.org/wiki/Two-dimensional_correlation_analysis Two-dimensional correlation analysis]

__FORCETOC__
== Relevant Publications ==

'''Main concepts of two-dimensional correlation analysis''' 
Basic principles of generalized 2D correlation spectroscopy are outlined in the following series of scientific publications: 
<ul>
<li>Noda, I.. 
[https://doi.org/10.1366/0003702904087398 Two-Dimensional Infrared (2D IR) Spectroscopy: Theory and Applications], 
'''1990''' ''Appl Spectrosc''. 44(4): 550-561.</li>
<li>Noda, I. 
[https://doi.org/10.1366/0003702934067694 Generalized Two-Dimensional Correlation Method Applicable to Infrared, Raman, and other Types of Spectroscopy], 
'''1993''' ''Appl Spectrosc''. 47(9): 1329-1336.</li>
<li>Noda, I. 
[https://doi.org/10.1366/0003702001950472 Determination of Two-Dimensional Correlation Spectra Using the Hilbert Transform], 
'''2000''' ''Appl Spectrosc''. 54(7): 994-999.</li>
</ul>

== Publications in which the mat2dcorr toolbox has been used or mentioned ==
 
<ol>

<li>Sonoiki, D.S., K. Kwarkye, K.M. Sorensen, S.B. Engelsen, et al., 2024 
[https://www.ncbi.nlm.nih.gov/pubmed/39686592 Single-Path Supercontinuum Near- to Mid-Infrared Correlation Spectroscopy of Aqueous Samples.] 
''Applied Spectroscopy'', p. 37028241302352.</li>

<li>Dabrowska, A., A. Schwaighofer, and B. Lendl. '''2024''' 
[https://www.ncbi.nlm.nih.gov/pubmed/38881172 Mid-Infrared Dispersion Spectroscopy as a Tool for Monitoring Time-Resolved Chemical Reactions on the Examples of Enzyme Kinetics and Mutarotation of Sugars] 
''Applied Spectroscopy''. 37028241258109.</li>

<li> Parpal, M., El Sachat, A., Sotomayor Torres, C.M., et al., '''2024''' 
[https://doi.org/10.1016/j.diamond.2023.110541 In situ Raman analysis of reduced-graphene oxide-based electroactive nanofluids.] 
''Diamond and Related Materials'',. 141: p. 110541.</li>

<li>Mite-Guzmán, N., M. Lazo, J. Triguero, A. Damián, et al., '''2023''' 
[https://doi.org/10.1016/j.cscee.2023.100359 Two-dimensional infrared for monitoring the structural variations of UV-aged recycled polypropylene straps used in the Ecuadorian banana industry.] 
''Case Studies in Chemical and Environmental Engineering'',. 7: p. 100359.</li>

<li>Chavez-Angel, E., R.C. Ng, S. Sandell, J. He, et al.,'''2023''' 
[https://www.ncbi.nlm.nih.gov/pubmed/36771835 Application of Synchrotron Radiation-Based Fourier-Transform Infrared Microspectroscopy for Thermal Imaging of Polymer Thin Films.] 
''Polymers (Basel)'', . 15(3).</li>

<li>Singh, R., V. Yadav, and S. Siddhanta,'''2023''' 
[https://www.ncbi.nlm.nih.gov/pubmed/36779479 Probing plasmon-induced surface reactions using two-dimensional correlation vibrational spectroscopy.] 
''Phys Chem Chem Phys'', . 25(8): p. 6032-6043.</li>

<li>Peng, S., F. Wang, D. Wei, C. Wang, et al.,'''2023''' 
[https://dx.doi.org/10.1016/j.jes.2023.10.004 Application of FTIR two-dimensional correlation spectroscopy (2D-COS) analysis in characterizing environmental behaviors of microplastics: A systematic review.] 
''Journal of Environmental Sciences''</li>

<li>Singh, R., Yadav, V., & Siddhanta, S. '''2023''' 
[https://doi.org/10.1039/D2CP05705K Probing plasmon-induced surface reactions using two-dimensional correlation vibrational spectroscopy]. 
''Physical Chemistry Chemical Physics'', 25(8), 6032-6043.</li>

<li>Park, Y., Jin, S., Noda, I., & Jung, Y. M. '''2022''' 
[https://doi.org/10.1016/j.saa.2022.121750 Continuing progress in the field of two-dimensional correlation spectroscopy (2D-COS), part II. Recent noteworthy developments]. 
''Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy'', 121750.</li>

<li>Chavez-Angel, E., Puertas, B., Kreuzer, M., Soliva Fortuny, R., Ng, R. C., Castro-Alvarez, A., & Sotomayor Torres, C. M. '''2022''' 
[https://doi.org/10.3390/foods11091304 Spectroscopic and thermal characterization of extra virgin olive oil adulterated with edible oils]. 
''Foods'', 11(9), 1304.</li>

<li>Lan, Z., Zhang, Y., Chen, X., Li, S., Cao, H., Wang, S., & Meng, J. '''2022''' 
[https://doi.org/10.1007/s12161-022-02245-y Efficient Detection of Limonoid From Citrus Seeds by Handheld NIR: Compared with Benchtop NIR]. 
''Food Analytical Methods'', 15(7), 1909-1921.</li>

<li>Amato, J., Iaccarino, N., D'Aria, F., D'Amico, F., Randazzo, A., Giancola, C., ... & Pagano, B. '''2022'''. 
[https://doi.org/10.1039/D2CP00058J Conformational plasticity of DNA secondary structures: Probing the conversion between i-motif and hairpin species by circular dichroism and ultraviolet resonance Raman spectroscopies]. 
''Physical Chemistry Chemical Physics'', 24(11), 7028-7044.</li>

<li>Sun, Y., Wang, X., Xia, S., & Zhao, J. '''2021''' 
[https://doi.org/10.1016/j.cej.2021.129085 New insights into oxytetracycline (OTC) adsorption behavior on polylactic acid microplastics undergoing microbial adhesion and degradation] 
C''hemical Engineering Journal'', 416, 129085. 
</li>

<li>Park, Y., Jin, S., Noda, I., & Jung, Y. M. '''2020''' 
[https://doi.org/10.1016/j.molstruc.2020.128405 Emerging developments in two-dimensional correlation spectroscopy (2D-COS)]. 
''Journal of Molecular Structure'', 1217, 128405.</li>

<li>Pin, J. M., Anstey, A., Park, C. B., & Lee, P. C. '''2020''' 
[https://pubs.acs.org/doi/10.1021/acs.macromol.0c01819 Exploration of Polymer Calorimetric Glass Transition Phenomenology by Two-Dimensional Correlation Analysis]. 
''Macromolecules'', 54(1), 473-487.</li>
<li>Lasch, P. and I. Noda '''2019''' 
[https://doi.org/10.1177/0003702818819880 Two-Dimensional Correlation Spectroscopy (2D-COS) for Analysis of Spatially Resolved Vibrational Spectra]. 
''Appl Spectrosc''. 73(4): 359-379.</li>

<li>Lasch, P. and I. Noda '''2017''' 
[https://doi.org/10.1021/acs.analchem.7b00332 Two-Dimensional Correlation Spectroscopy for Multimodal Analysis of FT-IR, Raman, and MALDI-TOF MS Hyperspectral Images with Hamster Brain Tissue]. 
''Anal Chem''. 89(9): 5008-5016.</li>
</ol>

== Acknowledgements ==

mat2dcorr is an open source software project which has been initiated and is maintained by [http://www.peter-lasch.de Peter Lasch] at the [https://www.rki.de/EN/Institute/Organisation/Departments/ZBS/ZBS6/zbs6-proteomics-and-spectroscopy-node.html ''Proteomics and Spectroscopy''] unit at the [https://www.rki.de ''Robert Koch-Institute''] (Berlin/Germany). The Matlab-based mat2dcorr toolbox is distributed under the Creative Commons CC BY-NC-SA 4.0 license for non-commercial use. Please send references to any publications, presentations, or successful funding applications that make use of the mat2Dcorr toolbox ([mailto:lasch@peter-lasch.de e-mail]).

In addition, I kindly ask to acknowledge utilization of the mat2dcorr toolbox by citing the following paper:   

<ul><ul>
{| class="wikitable" width=800
|-
| [http://doi.org/10.1177/0003702818819880 Two-Dimensional Correlation Spectroscopy (2D-COS) for Analysis of Spatially Resolved Vibrational Spectra]. Lasch, P. and Noda, I. ''Appl Spectrosc''. '''2019'''. 73(4): 359-379. doi:10.1177/0003702818819880
|}
  
</ul></ul>
Bug reports are welcome! ([mailto:lasch@peter-lasch.de e-mail])

Mat2dcorr - Relevant Publications

2025-04-09T14:03:48Z

Laschp:

[[File:GraphAbstr.gif|400px|thumb|mat2dcorr: Illustration of heterospectral 2D-COS (FTIR vs. Raman)]]

Two-dimensional correlation spectroscopy (2D-COS), or two-dimensional correlation analysis is known as a set of mathematical techniques useful to study changes in dynamic spectra. Dynamic spectra are often represented by spectra series obtained from a sample that was subjected to an external perturbation.   
The 2D-COS analysis technique has been initially developed by [https://en.wikipedia.org/wiki/Isao_Noda Isao Noda] in the 1980s.

<ul><ul>
{| class="wikitable" width=800
|-
| Wikipedia link: [https://en.wikipedia.org/wiki/Two-dimensional_correlation_analysis Two-dimensional correlation analysis]
|}
  
</ul></ul>
__FORCETOC__
== Relevant Publications ==

'''Main concepts of two-dimensional correlation analysis''' 
Basic principles of generalized 2D correlation spectroscopy are outlined in the following series of scientific publications: 
<ul>
<li>Noda, I.. 
[https://doi.org/10.1366/0003702904087398 Two-Dimensional Infrared (2D IR) Spectroscopy: Theory and Applications], 
'''1990''' ''Appl Spectrosc''. 44(4): 550-561.</li>
<li>Noda, I. 
[https://doi.org/10.1366/0003702934067694 Generalized Two-Dimensional Correlation Method Applicable to Infrared, Raman, and other Types of Spectroscopy], 
'''1993''' ''Appl Spectrosc''. 47(9): 1329-1336.</li>
<li>Noda, I. 
[https://doi.org/10.1366/0003702001950472 Determination of Two-Dimensional Correlation Spectra Using the Hilbert Transform], 
'''2000''' ''Appl Spectrosc''. 54(7): 994-999.</li>
</ul>

== Publications in which the mat2dcorr toolbox has been used or mentioned ==
 
<ol>

<li>Sonoiki, D.S., K. Kwarkye, K.M. Sorensen, S.B. Engelsen, et al., 2024 
[https://www.ncbi.nlm.nih.gov/pubmed/39686592 Single-Path Supercontinuum Near- to Mid-Infrared Correlation Spectroscopy of Aqueous Samples.] 
''Applied Spectroscopy'', p. 37028241302352.</li>

<li>Dabrowska, A., A. Schwaighofer, and B. Lendl. '''2024''' 
[https://www.ncbi.nlm.nih.gov/pubmed/38881172 Mid-Infrared Dispersion Spectroscopy as a Tool for Monitoring Time-Resolved Chemical Reactions on the Examples of Enzyme Kinetics and Mutarotation of Sugars] 
''Applied Spectroscopy''. 37028241258109.</li>

<li> Parpal, M., El Sachat, A., Sotomayor Torres, C.M., et al., '''2024''' 
[https://doi.org/10.1016/j.diamond.2023.110541 In situ Raman analysis of reduced-graphene oxide-based electroactive nanofluids.] 
''Diamond and Related Materials'',. 141: p. 110541.</li>

<li>Mite-Guzmán, N., M. Lazo, J. Triguero, A. Damián, et al., '''2023''' 
[https://doi.org/10.1016/j.cscee.2023.100359 Two-dimensional infrared for monitoring the structural variations of UV-aged recycled polypropylene straps used in the Ecuadorian banana industry.] 
''Case Studies in Chemical and Environmental Engineering'',. 7: p. 100359.</li>

<li>Chavez-Angel, E., R.C. Ng, S. Sandell, J. He, et al.,'''2023''' 
[https://www.ncbi.nlm.nih.gov/pubmed/36771835 Application of Synchrotron Radiation-Based Fourier-Transform Infrared Microspectroscopy for Thermal Imaging of Polymer Thin Films.] 
''Polymers (Basel)'', . 15(3).</li>

<li>Singh, R., V. Yadav, and S. Siddhanta,'''2023''' 
[https://www.ncbi.nlm.nih.gov/pubmed/36779479 Probing plasmon-induced surface reactions using two-dimensional correlation vibrational spectroscopy.] 
''Phys Chem Chem Phys'', . 25(8): p. 6032-6043.</li>

<li>Peng, S., F. Wang, D. Wei, C. Wang, et al.,'''2023''' 
[https://dx.doi.org/10.1016/j.jes.2023.10.004 Application of FTIR two-dimensional correlation spectroscopy (2D-COS) analysis in characterizing environmental behaviors of microplastics: A systematic review.] 
''Journal of Environmental Sciences''</li>

<li>Singh, R., Yadav, V., & Siddhanta, S. '''2023''' 
[https://doi.org/10.1039/D2CP05705K Probing plasmon-induced surface reactions using two-dimensional correlation vibrational spectroscopy]. 
''Physical Chemistry Chemical Physics'', 25(8), 6032-6043.</li>

<li>Park, Y., Jin, S., Noda, I., & Jung, Y. M. '''2022''' 
[https://doi.org/10.1016/j.saa.2022.121750 Continuing progress in the field of two-dimensional correlation spectroscopy (2D-COS), part II. Recent noteworthy developments]. 
''Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy'', 121750.</li>

<li>Chavez-Angel, E., Puertas, B., Kreuzer, M., Soliva Fortuny, R., Ng, R. C., Castro-Alvarez, A., & Sotomayor Torres, C. M. '''2022''' 
[https://doi.org/10.3390/foods11091304 Spectroscopic and thermal characterization of extra virgin olive oil adulterated with edible oils]. 
''Foods'', 11(9), 1304.</li>

<li>Lan, Z., Zhang, Y., Chen, X., Li, S., Cao, H., Wang, S., & Meng, J. '''2022''' 
[https://doi.org/10.1007/s12161-022-02245-y Efficient Detection of Limonoid From Citrus Seeds by Handheld NIR: Compared with Benchtop NIR]. 
''Food Analytical Methods'', 15(7), 1909-1921.</li>

<li>Amato, J., Iaccarino, N., D'Aria, F., D'Amico, F., Randazzo, A., Giancola, C., ... & Pagano, B. '''2022'''. 
[https://doi.org/10.1039/D2CP00058J Conformational plasticity of DNA secondary structures: Probing the conversion between i-motif and hairpin species by circular dichroism and ultraviolet resonance Raman spectroscopies]. 
''Physical Chemistry Chemical Physics'', 24(11), 7028-7044.</li>

<li>Sun, Y., Wang, X., Xia, S., & Zhao, J. '''2021''' 
[https://doi.org/10.1016/j.cej.2021.129085 New insights into oxytetracycline (OTC) adsorption behavior on polylactic acid microplastics undergoing microbial adhesion and degradation] 
C''hemical Engineering Journal'', 416, 129085. 
</li>

<li>Park, Y., Jin, S., Noda, I., & Jung, Y. M. '''2020''' 
[https://doi.org/10.1016/j.molstruc.2020.128405 Emerging developments in two-dimensional correlation spectroscopy (2D-COS)]. 
''Journal of Molecular Structure'', 1217, 128405.</li>

<li>Pin, J. M., Anstey, A., Park, C. B., & Lee, P. C. '''2020''' 
[https://pubs.acs.org/doi/10.1021/acs.macromol.0c01819 Exploration of Polymer Calorimetric Glass Transition Phenomenology by Two-Dimensional Correlation Analysis]. 
''Macromolecules'', 54(1), 473-487.</li>
<li>Lasch, P. and I. Noda '''2019''' 
[https://doi.org/10.1177/0003702818819880 Two-Dimensional Correlation Spectroscopy (2D-COS) for Analysis of Spatially Resolved Vibrational Spectra]. 
''Appl Spectrosc''. 73(4): 359-379.</li>

<li>Lasch, P. and I. Noda '''2017''' 
[https://doi.org/10.1021/acs.analchem.7b00332 Two-Dimensional Correlation Spectroscopy for Multimodal Analysis of FT-IR, Raman, and MALDI-TOF MS Hyperspectral Images with Hamster Brain Tissue]. 
''Anal Chem''. 89(9): 5008-5016.</li>
</ol>

== Acknowledgements ==

mat2dcorr is an open source software project which has been initiated and is maintained by [http://www.peter-lasch.de Peter Lasch] at the [https://www.rki.de/EN/Institute/Organisation/Departments/ZBS/ZBS6/zbs6-proteomics-and-spectroscopy-node.html ''Proteomics and Spectroscopy''] unit at the [https://www.rki.de ''Robert Koch-Institute''] (Berlin/Germany). The Matlab-based mat2dcorr toolbox is distributed under the Creative Commons CC BY-NC-SA 4.0 license for non-commercial use. Please send references to any publications, presentations, or successful funding applications that make use of the mat2Dcorr toolbox ([mailto:lasch@peter-lasch.de e-mail]).

In addition, I kindly ask to acknowledge utilization of the mat2dcorr toolbox by citing the following paper:   

<ul><ul>
{| class="wikitable" width=800
|-
| [http://doi.org/10.1177/0003702818819880 Two-Dimensional Correlation Spectroscopy (2D-COS) for Analysis of Spatially Resolved Vibrational Spectra]. Lasch, P. and Noda, I. ''Appl Spectrosc''. '''2019'''. 73(4): 359-379. doi:10.1177/0003702818819880
|}
  
</ul></ul>
Bug reports are welcome! ([mailto:lasch@peter-lasch.de e-mail])

Excel Trace Format

2025-04-09T14:02:45Z

Laschp:

[[File:excel-format.jpg|400px|thumb|Screenshot of the Excel spreadsheet ''linescandata.xlsx'']]

With version 1.04 of the ''mat2dcorr'' toolbox, spectra data from MS Excel spreadsheets can now be loaded in addition to hyperspectral imaging data in the CytoSpec data format and Matlab trace files. MS Excel spreadsheets should contain spectra series in a 2D data format, with the spectra as columns and the wavenumber vectors as rows. It is also important that the first row contains the vector with the data entries of the perturbing variable (temperature, pressure, etc.). The first column should contain the y-vector (wavenumber, frequencies, Raman shift vector) starting from row no. 2. 

To use your own data with the MS Excel import function, it is recommended to analyze the structure of the example file ''linescandata.xlsx'' and replace the spectral data contained therein with your own data. 

To load MS Excel trace files select ''Load Data'' → ''Excel data format'' → ''x-data'', or ''y-data'' from the ''Load Data'' menu bar.   

== Download test file ==

Download MS Excel test file ''linescandata.xlsx'': [http://www.peter-lasch.de/2dcorr/linescandata.xlsx https://wiki2dcos.microbe-ms.com/linescandata.xlsx] 

== Related links ==

* [[Matlab_Imaging_Format|Import data in the CytoSpec imaging format (CytoSpec/Matlab)]]
* [[Matlab_Trace_Format|Import data in the trace format (Matlab)]]
* [[Format_of_a_2D-COS_Result_File|Format of a 2D-COS result file (Matlab)]]

Options of the 2D-COS Control Window

2025-04-09T13:55:32Z

Laschp:

[[File:2dcos-ctrl-gui.jpg|thumb|mat2dcorr: screenshot of the 2D-COS control window]]

'''Type of 2D spectrum''': defines the type of the 2D-COS analysis. Valid options are: 

* Pearson scaling - the synchronous 2D spectrum with Pearson, or unit variance scaling. Pearson scaling is also used in statistical total correlation spectroscopy [STOCSY] and statistical heterospectroscopy [SHY])
* Pareto scale 0.75 - the synchronous 2D spectrum with Pareto scaling. The parameter α equals 0.75
* Pareto scale 0.50 - the synchronous 2D spectrum with Pareto scaling. The parameter α equals 0.50 (Pareto scaling in the strict sense)
* Pareto scale 0.25 - the synchronous 2D spectrum with Pareto scaling. The parameter α equals 0.25
* Stat synchronous - the classical synchronous 2D (covariance) spectrum
* Stat asynchronous - the classical asynchronous 2D spectrum
* disrelation - allows to calculate the absolute of the 2D disrelation spectrum
* FFT synchronous - alternative implementation to obtain the synchronous 2D correlation spectrum by means of the fast Fourier-transformation approach
* FFT asynchronous - alternative implementation to obtain the asynchronous 2D correlation spectrum by means of the fast Fourier-transformation approach

'''Spectral regions for 2D-COS analysis''': allows to define the [x,y] spectral ranges for 2D-COS analysis

'''Type of reference spectrum''': defines the type of reference spectrum to obtain the dynamic spectrum. Valid options are ''no reference'' (spectrum), ''average spectrum'' (default), ''first spectrum'' and ''last spectrum''.

'''Plot contour map''': plots a contour map instead of an interpolated surface map where the color is proportional to the 2D-COS functional values.

'''Fill contour''': creates a filled contour map

'''Number of isolines''': defines the number of isolines in contour / filled contour maps

'''Select colormap''': the type of color maps used to plot surface maps, or to plot the isolines in contour maps

'''Scaling''': permits to modify manually the color map by entering the minimal and maximal z-values into the appropriate edit boxes

'''Apply scaling''': color map scaling values are immediately applied to the 2D correlation spectrum

'''[x,y] slice coordinates''': settings required to plot 1D correlation slices, or to create [x,y] feature plots

Options of the 2D-COS Control Window

2025-04-09T13:53:49Z

Laschp:

[[File:2dcos-ctrl-gui.jpg|thumb|mat2dcorr: screenshot of the 2D-COS control window]]

'''Type of 2D spectrum''': defines the type of the 2D-COS analysis. Valid options are: 

*Pearson scaling - the synchronous 2D spectrum with Pearson, or unit variance scaling. Pearson scaling is also used in statistical total correlation spectroscopy [STOCSY] and statistical heterospectroscopy [SHY])
*Pareto scale 0.75 - the synchronous 2D spectrum with Pareto scaling. The parameter α equals 0.75
*Pareto scale 0.50 - the synchronous 2D spectrum with Pareto scaling. The parameter α equals 0.50 (Pareto scaling in the strict sense)
*Pareto scale 0.25 - the synchronous 2D spectrum with Pareto scaling. The parameter α equals 0.25
*stat synchronous - the classical synchronous 2D (covariance) spectrum
*stat asynchronous - the classical asynchronous 2D spectrum
*disrelation - allows to calculate the absolute of the 2D disrelation spectrum
*fft synchronous - alternative implementation to obtain the synchronous 2D correlation spectrum by means of the fast Fourier-transformation approach
*fft asynchronous - alternative implementation to obtain the asynchronous 2D correlation spectrum by means of the fast Fourier-transformation approach

'''Spectral regions for 2D-COS analysis''': allows to define the [x,y] spectral ranges for 2D-COS analysis

'''Type of reference spectrum''': defines the type of reference spectrum to obtain the dynamic spectrum. Valid options are ''no reference'' (spectrum), ''average spectrum'' (default), ''first spectrum'' and ''last spectrum''.

'''Plot contour map''': plots a contour map instead of an interpolated surface map where the color is proportional to the 2D-COS functional values.

'''Fill contour''': creates a filled contour map

'''Number of isolines''': defines the number of isolines in contour / filled contour maps

'''Select colormap''': the type of color maps used to plot surface maps, or to plot the isolines in contour maps

'''Scaling''': permits to modify manually the color map by entering the minimal and maximal z-values into the appropriate edit boxes

'''Apply scaling''': color map scaling values are immediately applied to the 2D correlation spectrum

'''[x,y] slice coordinates''': settings required to plot 1D correlation slices, or to create [x,y] feature plots

Options of the 2D-COS Control Window

2025-04-09T13:53:06Z

Laschp:

[[File:2dcos-ctrl-gui.jpg|thumb|mat2dcorr: screenshot of the 2D-COS control window]]

'''Type of 2D spectrum''': defines the type of the 2D-COS analysis. Valid options are: 

* Pearson scaling - the synchronous 2D spectrum with Pearson, or unit variance scaling. Pearson scaling is also used in statistical total correlation spectroscopy [STOCSY] and statistical heterospectroscopy [SHY])
* Pareto scale 0.75 - the synchronous 2D spectrum with Pareto scaling. The parameter α equals 0.75
* Pareto scale 0.50 - the synchronous 2D spectrum with Pareto scaling. The parameter α equals 0.50 (Pareto scaling in the strict sense)
* Pareto scale 0.25 - the synchronous 2D spectrum with Pareto scaling. The parameter α equals 0.25
* stat synchronous - the classical synchronous 2D (covariance) spectrum
* stat asynchronous - the classical asynchronous 2D spectrum
* disrelation - allows to calculate the absolute of the 2D disrelation spectrum
* fft synchronous - alternative implementation to obtain the synchronous 2D correlation spectrum by means of the fast Fourier-transformation approach
* fft asynchronous - alternative implementation to obtain the asynchronous 2D correlation spectrum by means of the fast Fourier-transformation approach

'''Spectral regions for 2D-COS analysis''': allows to define the [x,y] spectral ranges for 2D-COS analysis

'''Type of reference spectrum''': defines the type of reference spectrum to obtain the dynamic spectrum. Valid options are ''no reference'' (spectrum), ''average spectrum'' (default), ''first spectrum'' and ''last spectrum''.

'''Plot contour map''': plots a contour map instead of an interpolated surface map where the color is proportional to the 2D-COS functional values.

'''Fill contour''': creates a filled contour map

'''Number of isolines''': defines the number of isolines in contour / filled contour maps

'''Select colormap''': the type of color maps used to plot surface maps, or to plot the isolines in contour maps

'''Scaling''': permits to modify manually the color map by entering the minimal and maximal z-values into the appropriate edit boxes

'''Apply scaling''': color map scaling values are immediately applied to the 2D correlation spectrum

'''[x,y] slice coordinates''': settings required to plot 1D correlation slices, or to create [x,y] feature plots

Computer Specification

2025-04-09T13:50:44Z

Laschp:

* Processor: 32-bit or 64-bit CPU from Intel or AMD
* Operating systems: Microsoft Windows 10 or Windows11 (64-bit version preferred), or LINUX (tested on Debian Bullseye)
* Matlab R2014a (The Mathworks), or newer
* Memory: >4 GB recommended (2048 MB minimum)
* the mat2dcorr toolbox will be installed as a Matlab toolbox. Matlab m-code (source files) can be downloaded from this website.

Mat2dcorr - Relevant Publications

2025-04-09T13:49:09Z

Laschp: /* Acknowledgement */

Two-dimensional correlation spectroscopy (2D-COS), or two-dimensional correlation analysis is known as a set of mathematical techniques useful to study changes in dynamic spectra. Dynamic spectra are often represented by spectra series obtained from a sample that was subjected to an external perturbation.   
The 2D-COS analysis technique has been initially developed by [https://en.wikipedia.org/wiki/Isao_Noda Isao Noda] in the 1980s.

<ul><ul>
{| class="wikitable" width=800
|-
| Wikipedia link: [https://en.wikipedia.org/wiki/Two-dimensional_correlation_analysis Two-dimensional correlation analysis]
|}
  
</ul></ul>
__FORCETOC__
== Relevant Publications ==

'''Main concepts of two-dimensional correlation analysis''' 
Basic principles of generalized 2D correlation spectroscopy are outlined in the following series of scientific publications: 
<ul>
<li>Noda, I.. 
[https://doi.org/10.1366/0003702904087398 Two-Dimensional Infrared (2D IR) Spectroscopy: Theory and Applications], 
'''1990''' ''Appl Spectrosc''. 44(4): 550-561.</li>
<li>Noda, I. 
[https://doi.org/10.1366/0003702934067694 Generalized Two-Dimensional Correlation Method Applicable to Infrared, Raman, and other Types of Spectroscopy], 
'''1993''' ''Appl Spectrosc''. 47(9): 1329-1336.</li>
<li>Noda, I. 
[https://doi.org/10.1366/0003702001950472 Determination of Two-Dimensional Correlation Spectra Using the Hilbert Transform], 
'''2000''' ''Appl Spectrosc''. 54(7): 994-999.</li>
</ul>

== Publications in which the mat2dcorr toolbox has been used or mentioned ==
 
<ol>

<li>Sonoiki, D.S., K. Kwarkye, K.M. Sorensen, S.B. Engelsen, et al., 2024 
[https://www.ncbi.nlm.nih.gov/pubmed/39686592 Single-Path Supercontinuum Near- to Mid-Infrared Correlation Spectroscopy of Aqueous Samples.] 
''Applied Spectroscopy'', p. 37028241302352.</li>

<li>Dabrowska, A., A. Schwaighofer, and B. Lendl. '''2024''' 
[https://www.ncbi.nlm.nih.gov/pubmed/38881172 Mid-Infrared Dispersion Spectroscopy as a Tool for Monitoring Time-Resolved Chemical Reactions on the Examples of Enzyme Kinetics and Mutarotation of Sugars] 
''Applied Spectroscopy''. 37028241258109.</li>

<li> Parpal, M., El Sachat, A., Sotomayor Torres, C.M., et al., '''2024''' 
[https://doi.org/10.1016/j.diamond.2023.110541 In situ Raman analysis of reduced-graphene oxide-based electroactive nanofluids.] 
''Diamond and Related Materials'',. 141: p. 110541.</li>

<li>Mite-Guzmán, N., M. Lazo, J. Triguero, A. Damián, et al., '''2023''' 
[https://doi.org/10.1016/j.cscee.2023.100359 Two-dimensional infrared for monitoring the structural variations of UV-aged recycled polypropylene straps used in the Ecuadorian banana industry.] 
''Case Studies in Chemical and Environmental Engineering'',. 7: p. 100359.</li>

<li>Chavez-Angel, E., R.C. Ng, S. Sandell, J. He, et al.,'''2023''' 
[https://www.ncbi.nlm.nih.gov/pubmed/36771835 Application of Synchrotron Radiation-Based Fourier-Transform Infrared Microspectroscopy for Thermal Imaging of Polymer Thin Films.] 
''Polymers (Basel)'', . 15(3).</li>

<li>Singh, R., V. Yadav, and S. Siddhanta,'''2023''' 
[https://www.ncbi.nlm.nih.gov/pubmed/36779479 Probing plasmon-induced surface reactions using two-dimensional correlation vibrational spectroscopy.] 
''Phys Chem Chem Phys'', . 25(8): p. 6032-6043.</li>

<li>Peng, S., F. Wang, D. Wei, C. Wang, et al.,'''2023''' 
[https://dx.doi.org/10.1016/j.jes.2023.10.004 Application of FTIR two-dimensional correlation spectroscopy (2D-COS) analysis in characterizing environmental behaviors of microplastics: A systematic review.] 
''Journal of Environmental Sciences''</li>

<li>Singh, R., Yadav, V., & Siddhanta, S. '''2023''' 
[https://doi.org/10.1039/D2CP05705K Probing plasmon-induced surface reactions using two-dimensional correlation vibrational spectroscopy]. 
''Physical Chemistry Chemical Physics'', 25(8), 6032-6043.</li>

<li>Park, Y., Jin, S., Noda, I., & Jung, Y. M. '''2022''' 
[https://doi.org/10.1016/j.saa.2022.121750 Continuing progress in the field of two-dimensional correlation spectroscopy (2D-COS), part II. Recent noteworthy developments]. 
''Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy'', 121750.</li>

<li>Chavez-Angel, E., Puertas, B., Kreuzer, M., Soliva Fortuny, R., Ng, R. C., Castro-Alvarez, A., & Sotomayor Torres, C. M. '''2022''' 
[https://doi.org/10.3390/foods11091304 Spectroscopic and thermal characterization of extra virgin olive oil adulterated with edible oils]. 
''Foods'', 11(9), 1304.</li>

<li>Lan, Z., Zhang, Y., Chen, X., Li, S., Cao, H., Wang, S., & Meng, J. '''2022''' 
[https://doi.org/10.1007/s12161-022-02245-y Efficient Detection of Limonoid From Citrus Seeds by Handheld NIR: Compared with Benchtop NIR]. 
''Food Analytical Methods'', 15(7), 1909-1921.</li>

<li>Amato, J., Iaccarino, N., D'Aria, F., D'Amico, F., Randazzo, A., Giancola, C., ... & Pagano, B. '''2022'''. 
[https://doi.org/10.1039/D2CP00058J Conformational plasticity of DNA secondary structures: Probing the conversion between i-motif and hairpin species by circular dichroism and ultraviolet resonance Raman spectroscopies]. 
''Physical Chemistry Chemical Physics'', 24(11), 7028-7044.</li>

<li>Sun, Y., Wang, X., Xia, S., & Zhao, J. '''2021''' 
[https://doi.org/10.1016/j.cej.2021.129085 New insights into oxytetracycline (OTC) adsorption behavior on polylactic acid microplastics undergoing microbial adhesion and degradation] 
C''hemical Engineering Journal'', 416, 129085. 
</li>

<li>Park, Y., Jin, S., Noda, I., & Jung, Y. M. '''2020''' 
[https://doi.org/10.1016/j.molstruc.2020.128405 Emerging developments in two-dimensional correlation spectroscopy (2D-COS)]. 
''Journal of Molecular Structure'', 1217, 128405.</li>

<li>Pin, J. M., Anstey, A., Park, C. B., & Lee, P. C. '''2020''' 
[https://pubs.acs.org/doi/10.1021/acs.macromol.0c01819 Exploration of Polymer Calorimetric Glass Transition Phenomenology by Two-Dimensional Correlation Analysis]. 
''Macromolecules'', 54(1), 473-487.</li>
<li>Lasch, P. and I. Noda '''2019''' 
[https://doi.org/10.1177/0003702818819880 Two-Dimensional Correlation Spectroscopy (2D-COS) for Analysis of Spatially Resolved Vibrational Spectra]. 
''Appl Spectrosc''. 73(4): 359-379.</li>

<li>Lasch, P. and I. Noda '''2017''' 
[https://doi.org/10.1021/acs.analchem.7b00332 Two-Dimensional Correlation Spectroscopy for Multimodal Analysis of FT-IR, Raman, and MALDI-TOF MS Hyperspectral Images with Hamster Brain Tissue]. 
''Anal Chem''. 89(9): 5008-5016.</li>
</ol>

== Acknowledgements ==

mat2dcorr is an open source software project which has been initiated and is maintained by [http://www.peter-lasch.de Peter Lasch] at the [https://www.rki.de/EN/Institute/Organisation/Departments/ZBS/ZBS6/zbs6-proteomics-and-spectroscopy-node.html ''Proteomics and Spectroscopy''] unit at the [https://www.rki.de ''Robert Koch-Institute''] (Berlin/Germany). The Matlab-based mat2dcorr toolbox is distributed under the Creative Commons CC BY-NC-SA 4.0 license for non-commercial use. Please send references to any publications, presentations, or successful funding applications that make use of the mat2Dcorr toolbox ([mailto:lasch@peter-lasch.de e-mail]).

In addition, I kindly ask to acknowledge utilization of the mat2dcorr toolbox by citing the following paper:   

<ul><ul>
{| class="wikitable" width=800
|-
| [http://doi.org/10.1177/0003702818819880 Two-Dimensional Correlation Spectroscopy (2D-COS) for Analysis of Spatially Resolved Vibrational Spectra]. Lasch, P. and Noda, I. ''Appl Spectrosc''. '''2019'''. 73(4): 359-379. doi:10.1177/0003702818819880
|}
  
</ul></ul>
Bug reports are welcome! ([mailto:lasch@peter-lasch.de e-mail])

Mat2dcorr - Relevant Publications

2025-04-09T13:48:44Z

Laschp: /* Acknowledgement */

Two-dimensional correlation spectroscopy (2D-COS), or two-dimensional correlation analysis is known as a set of mathematical techniques useful to study changes in dynamic spectra. Dynamic spectra are often represented by spectra series obtained from a sample that was subjected to an external perturbation.   
The 2D-COS analysis technique has been initially developed by [https://en.wikipedia.org/wiki/Isao_Noda Isao Noda] in the 1980s.

<ul><ul>
{| class="wikitable" width=800
|-
| Wikipedia link: [https://en.wikipedia.org/wiki/Two-dimensional_correlation_analysis Two-dimensional correlation analysis]
|}
  
</ul></ul>
__FORCETOC__
== Relevant Publications ==

'''Main concepts of two-dimensional correlation analysis''' 
Basic principles of generalized 2D correlation spectroscopy are outlined in the following series of scientific publications: 
<ul>
<li>Noda, I.. 
[https://doi.org/10.1366/0003702904087398 Two-Dimensional Infrared (2D IR) Spectroscopy: Theory and Applications], 
'''1990''' ''Appl Spectrosc''. 44(4): 550-561.</li>
<li>Noda, I. 
[https://doi.org/10.1366/0003702934067694 Generalized Two-Dimensional Correlation Method Applicable to Infrared, Raman, and other Types of Spectroscopy], 
'''1993''' ''Appl Spectrosc''. 47(9): 1329-1336.</li>
<li>Noda, I. 
[https://doi.org/10.1366/0003702001950472 Determination of Two-Dimensional Correlation Spectra Using the Hilbert Transform], 
'''2000''' ''Appl Spectrosc''. 54(7): 994-999.</li>
</ul>

== Publications in which the mat2dcorr toolbox has been used or mentioned ==
 
<ol>

<li>Sonoiki, D.S., K. Kwarkye, K.M. Sorensen, S.B. Engelsen, et al., 2024 
[https://www.ncbi.nlm.nih.gov/pubmed/39686592 Single-Path Supercontinuum Near- to Mid-Infrared Correlation Spectroscopy of Aqueous Samples.] 
''Applied Spectroscopy'', p. 37028241302352.</li>

<li>Dabrowska, A., A. Schwaighofer, and B. Lendl. '''2024''' 
[https://www.ncbi.nlm.nih.gov/pubmed/38881172 Mid-Infrared Dispersion Spectroscopy as a Tool for Monitoring Time-Resolved Chemical Reactions on the Examples of Enzyme Kinetics and Mutarotation of Sugars] 
''Applied Spectroscopy''. 37028241258109.</li>

<li> Parpal, M., El Sachat, A., Sotomayor Torres, C.M., et al., '''2024''' 
[https://doi.org/10.1016/j.diamond.2023.110541 In situ Raman analysis of reduced-graphene oxide-based electroactive nanofluids.] 
''Diamond and Related Materials'',. 141: p. 110541.</li>

<li>Mite-Guzmán, N., M. Lazo, J. Triguero, A. Damián, et al., '''2023''' 
[https://doi.org/10.1016/j.cscee.2023.100359 Two-dimensional infrared for monitoring the structural variations of UV-aged recycled polypropylene straps used in the Ecuadorian banana industry.] 
''Case Studies in Chemical and Environmental Engineering'',. 7: p. 100359.</li>

<li>Chavez-Angel, E., R.C. Ng, S. Sandell, J. He, et al.,'''2023''' 
[https://www.ncbi.nlm.nih.gov/pubmed/36771835 Application of Synchrotron Radiation-Based Fourier-Transform Infrared Microspectroscopy for Thermal Imaging of Polymer Thin Films.] 
''Polymers (Basel)'', . 15(3).</li>

<li>Singh, R., V. Yadav, and S. Siddhanta,'''2023''' 
[https://www.ncbi.nlm.nih.gov/pubmed/36779479 Probing plasmon-induced surface reactions using two-dimensional correlation vibrational spectroscopy.] 
''Phys Chem Chem Phys'', . 25(8): p. 6032-6043.</li>

<li>Peng, S., F. Wang, D. Wei, C. Wang, et al.,'''2023''' 
[https://dx.doi.org/10.1016/j.jes.2023.10.004 Application of FTIR two-dimensional correlation spectroscopy (2D-COS) analysis in characterizing environmental behaviors of microplastics: A systematic review.] 
''Journal of Environmental Sciences''</li>

<li>Singh, R., Yadav, V., & Siddhanta, S. '''2023''' 
[https://doi.org/10.1039/D2CP05705K Probing plasmon-induced surface reactions using two-dimensional correlation vibrational spectroscopy]. 
''Physical Chemistry Chemical Physics'', 25(8), 6032-6043.</li>

<li>Park, Y., Jin, S., Noda, I., & Jung, Y. M. '''2022''' 
[https://doi.org/10.1016/j.saa.2022.121750 Continuing progress in the field of two-dimensional correlation spectroscopy (2D-COS), part II. Recent noteworthy developments]. 
''Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy'', 121750.</li>

<li>Chavez-Angel, E., Puertas, B., Kreuzer, M., Soliva Fortuny, R., Ng, R. C., Castro-Alvarez, A., & Sotomayor Torres, C. M. '''2022''' 
[https://doi.org/10.3390/foods11091304 Spectroscopic and thermal characterization of extra virgin olive oil adulterated with edible oils]. 
''Foods'', 11(9), 1304.</li>

<li>Lan, Z., Zhang, Y., Chen, X., Li, S., Cao, H., Wang, S., & Meng, J. '''2022''' 
[https://doi.org/10.1007/s12161-022-02245-y Efficient Detection of Limonoid From Citrus Seeds by Handheld NIR: Compared with Benchtop NIR]. 
''Food Analytical Methods'', 15(7), 1909-1921.</li>

<li>Amato, J., Iaccarino, N., D'Aria, F., D'Amico, F., Randazzo, A., Giancola, C., ... & Pagano, B. '''2022'''. 
[https://doi.org/10.1039/D2CP00058J Conformational plasticity of DNA secondary structures: Probing the conversion between i-motif and hairpin species by circular dichroism and ultraviolet resonance Raman spectroscopies]. 
''Physical Chemistry Chemical Physics'', 24(11), 7028-7044.</li>

<li>Sun, Y., Wang, X., Xia, S., & Zhao, J. '''2021''' 
[https://doi.org/10.1016/j.cej.2021.129085 New insights into oxytetracycline (OTC) adsorption behavior on polylactic acid microplastics undergoing microbial adhesion and degradation] 
C''hemical Engineering Journal'', 416, 129085. 
</li>

<li>Park, Y., Jin, S., Noda, I., & Jung, Y. M. '''2020''' 
[https://doi.org/10.1016/j.molstruc.2020.128405 Emerging developments in two-dimensional correlation spectroscopy (2D-COS)]. 
''Journal of Molecular Structure'', 1217, 128405.</li>

<li>Pin, J. M., Anstey, A., Park, C. B., & Lee, P. C. '''2020''' 
[https://pubs.acs.org/doi/10.1021/acs.macromol.0c01819 Exploration of Polymer Calorimetric Glass Transition Phenomenology by Two-Dimensional Correlation Analysis]. 
''Macromolecules'', 54(1), 473-487.</li>
<li>Lasch, P. and I. Noda '''2019''' 
[https://doi.org/10.1177/0003702818819880 Two-Dimensional Correlation Spectroscopy (2D-COS) for Analysis of Spatially Resolved Vibrational Spectra]. 
''Appl Spectrosc''. 73(4): 359-379.</li>

<li>Lasch, P. and I. Noda '''2017''' 
[https://doi.org/10.1021/acs.analchem.7b00332 Two-Dimensional Correlation Spectroscopy for Multimodal Analysis of FT-IR, Raman, and MALDI-TOF MS Hyperspectral Images with Hamster Brain Tissue]. 
''Anal Chem''. 89(9): 5008-5016.</li>
</ol>

== Acknowledgement ==

mat2dcorr is an open source software project which has been initiated and is maintained by [http://www.peter-lasch.de Peter Lasch] at the [https://www.rki.de/EN/Institute/Organisation/Departments/ZBS/ZBS6/zbs6-proteomics-and-spectroscopy-node.html ''Proteomics and Spectroscopy''] unit at the [https://www.rki.de ''Robert Koch-Institute''] (Berlin/Germany). The Matlab-based mat2dcorr toolbox is distributed under the Creative Commons CC BY-NC-SA 4.0 license for non-commercial use. Please send references to any publications, presentations, or successful funding applications that make use of the mat2Dcorr toolbox ([mailto:lasch@peter-lasch.de e-mail]).

In addition, I kindly ask to acknowledge utilization of the mat2dcorr toolbox by citing the following paper:   

<ul><ul>
{| class="wikitable" width=800
|-
| [http://doi.org/10.1177/0003702818819880 Two-Dimensional Correlation Spectroscopy (2D-COS) for Analysis of Spatially Resolved Vibrational Spectra]. Lasch, P. and Noda, I. ''Appl Spectrosc''. '''2019'''. 73(4): 359-379. doi:10.1177/0003702818819880
|}
  
</ul></ul>
Bug reports are welcome! ([mailto:lasch@peter-lasch.de e-mail])

Mat2dcorr - A Matlab Toolbox for 2D-COS

2025-04-09T13:44:34Z

Laschp:

__FORCETOC__

[[File:GraphAbstr.gif|400px|thumb|mat2dcorr: Illustration of heterospectral 2D-COS (FTIR vs. Raman)]]
[[File:2D-COS.jpg|400px|thumb|Mat2Dcorr: Screenshot of the 2D control window (left) and the window '2D correlation analysis ... ' (right)]]

mat2dcorr - a free toolbox for performing two-dimensional correlation (2D-COS) analysis with [https://www.mathworks.com Matlab]. 

== Introduction ==

Since its introduction in 1989, two-dimensional correlation spectroscopy (2D-COS), sometimes referred to as two-dimensional correlation analysis, has become an invaluable analytical tool for spectroscopic characterization in various fields of application, including protein science, pharmacy, biomedical applications, and polymer or nanomaterial research. The technique of 2D-COS analysis was originally developed to study dynamic processes in which a spectroscopic probe is used to monitor the effect of an external perturbation on a given system. The system responds to the perturbation with characteristic changes that are monitored spectroscopically, e.g. by continuous acquisition of a spectral time series. The main purpose of 2D-COS investigations is to explore correlations that may exist between perturbation-induced spectral responses. In some situations, the 2D-COS analysis technique is useful not only to detect and visualize such correlations, but also to decipher the sequence of these spectral changes. Sources of perturbation may include changes in temperature, pH, pressure, concentration, or spatial coordinates when examining spatially heterogeneous samples. The 2D correlation concept has proven to be extremely useful in a wide variety of molecular spectroscopy applications based on IR, NIR, Raman, NMR, fluorescence and UV-visible spectroscopy.

An introduction into the history, mathematics and basic principles of 2D-COS can be found here: https://en.wikipedia.org/wiki/Two-dimensional_correlation_analysis

== Getting Started ==
* [[Computer_Specification|Specification of computer configuration]]
* [[Screenshot_of_mat2dcorr|Screenshot of the mat2dcorr gui]]
* Downloading mat2dcorr - [[How_to_obtain_the_mat2dcorr_toolbox|How to obtain the mat2dcorr toolbox?]]
* [[Installation_of_the_mat2dcorr_toolbox|Installation of the mat2dcorr toolbox]]
* [[Disclaimer_and_License_Conditions |Disclaimer and license conditions]]
* [[mat2dcorr_-_Relevant Publications|Acknowledgement, relevant publications]]
* [[Frequently_Asked_Questions_(FAQ)|Frequently asked questions (FAQ)]]

== Data Import & File Formats ==

* [[Matlab_Imaging_Format|Import data in the CytoSpec imaging format (CytoSpec/Matlab)]]
* [[Matlab_Trace_Format|Import data in the trace format (Matlab)]]
* [[Excel_Trace_Format|Import data in the MS Excel data format]]
* [[Format_of_a_2D-COS_Result_File|Format of a 2D-COS result file (Matlab)]]

== Options of the mat2dcorr Toolbox ==

* [[Options_of_the_2D-COS_Main_Figure| Options of the 2D-COS main figure]]
* [[Options_of_the_2D-COS_Control_Window| Options of the 2D-COS control window]]

== Related Publications and Web Links ==

* [https://en.wikipedia.org/wiki/Two-dimensional_correlation_analysis Two-dimensional correlation analysis] (Wikipedia)

* I. Noda. [https://doi.org/10.1366/0003702904087398 Two-Dimensional Infrared (2D IR) Spectroscopy: Theory and Applications], '''1990''' ''Appl. Spectrosc''. 44(4): 550-561

* I. Noda. [https://doi.org/10.1366/0003702934067694 Generalized Two- Dimensional Correlation Method Applicable to Infrared, Raman, and other Types of Spectroscopy], '''1993''' ''Appl. Spectrosc''. 47(9): 1329-1336

* I. Noda. [https://doi.org/10.1366/0003702001950472 Determination of Two-Dimensional Correlation Spectra Using the Hilbert Transform], '''2000''' ''Appl. Spectrosc''. 54(7): 994-999

* P. Lasch & I. Noda. [https://doi.org/10.1021/acs.analchem.7b00332 Two-Dimensional Correlation Spectroscopy for Multimodal Analysis of FT-IR, Raman, and MALDI-TOF MS Hyperspectral Images with Hamster Brain Tissue]. '''2017''' ''Anal. Chem''. 89, 9, 5008–5016

* P. Lasch & I. Noda. [https://doi.org/10.1177/0003702818819880 Two-Dimensional Correlation Spectroscopy (2D-COS) for Analysis of Spatially Resolved Vibrational Spectra]. '''2019''' ''Appl. Spectrosc''. 73(4): 359-379

   

Apr 09, 2025: more details of the ''mat2dcorr'' Matlab toolbox will follow

File:2D-COS.jpg

2025-04-09T13:41:43Z

Laschp: Laschp uploaded a new version of File:2D-COS.jpg

Mat2dcorr - A Matlab Toolbox for 2D-COS

2025-04-09T13:32:00Z

Laschp: /* Related Publications and Web Links */

__FORCETOC__

[[File:GraphAbstr.gif|400px|thumb|mat2dcorr: Illustration of heterospectral 2D-COS (FTIR vs. Raman)]]
[[File:2D-COS.jpg|400px|thumb|Mat2Dcorr: Screenshot of the 2D control window (left) and the window '2D correlation analysis ... ' (right)]]

mat2dcorr - a free toolbox for performing two-dimensional correlation (2D-COS) analysis with [https://www.mathworks.com Matlab]. 

== Introduction ==

Since its introduction in 1989, two-dimensional correlation spectroscopy (2D-COS), sometimes referred to as two-dimensional correlation analysis, has become an invaluable analytical tool for spectroscopic characterization in various fields of application, including protein science, pharmacy, biomedical applications, and polymer or nanomaterial research. The technique of 2D-COS analysis was originally developed to study dynamic processes in which a spectroscopic probe is used to monitor the effect of an external perturbation on a given system. The system responds to the perturbation with characteristic changes that are monitored spectroscopically, e.g. by continuous acquisition of a spectral time series. The main purpose of 2D-COS investigations is to explore correlations that may exist between perturbation-induced spectral responses. In some situations, the 2D-COS analysis technique is useful not only to detect and visualize such correlations, but also to decipher the sequence of these spectral changes. Sources of perturbation may include changes in temperature, pH, pressure, concentration, or spatial coordinates when examining spatially heterogeneous samples. The 2D correlation concept has proven to be extremely useful in a wide variety of molecular spectroscopy applications based on IR, NIR, Raman, NMR, fluorescence and UV-visible spectroscopy.

An introduction into the history, mathematics and basic principles of 2D-COS can be found here: https://en.wikipedia.org/wiki/Two-dimensional_correlation_analysis

== Getting Started ==
* [[Computer_Specification|Specification of computer configuration]]
* [[Screenshot_of_mat2dcorr|Screenshot of the mat2dcorr gui]]
* Downloading mat2dcorr - [[How_to_obtain_the_mat2dcorr_toolbox|How to obtain the mat2dcorr toolbox?]]
* [[Installation_of_the_mat2dcorr_toolbox|Installation of the mat2dcorr toolbox]]
* [[Disclaimer_and_License_Conditions |Disclaimer and license conditions]]
* [[mat2dcorr_-_Relevant Publications|Acknowledgement, relevant publications]]
* [[Frequently_Asked_Questions_(FAQ)|Frequently asked questions (FAQ)]]

== Data Import & File Formats ==

* [[Matlab_Imaging_Format|Import data in the CytoSpec imaging format (CytoSpec/Matlab)]]
* [[Matlab_Trace_Format|Import data in the trace format (Matlab)]]
* [[Excel_Trace_Format|Import data in the MS Excel data format]]
* [[Format_of_a_2D-COS_Result_File|Format of a 2D-COS result file (Matlab)]]

== Options of the mat2dcorr Toolbox ==

* [[Options_of_the_2D-COS_Main_Figure| Options of the 2D-COS main figure]]
* [[Options_of_the_2D-COS_Control_Window| Options of the 2D-COS control window]]

== Related Publications and Web Links ==

* [https://en.wikipedia.org/wiki/Two-dimensional_correlation_analysis Two-dimensional correlation analysis] (Wikipedia)

* I. Noda. [https://doi.org/10.1366/0003702904087398 Two-Dimensional Infrared (2D IR) Spectroscopy: Theory and Applications], '''1990''' ''Appl. Spectrosc''. 44(4): 550-561

* I. Noda. [https://doi.org/10.1366/0003702934067694 Generalized Two- Dimensional Correlation Method Applicable to Infrared, Raman, and other Types of Spectroscopy], '''1993''' ''Appl. Spectrosc''. 47(9): 1329-1336

* I. Noda. [https://doi.org/10.1366/0003702001950472 Determination of Two-Dimensional Correlation Spectra Using the Hilbert Transform], '''2000''' ''Appl. Spectrosc''. 54(7): 994-999

* P. Lasch & I. Noda. [https://doi.org/10.1021/acs.analchem.7b00332 Two-Dimensional Correlation Spectroscopy for Multimodal Analysis of FT-IR, Raman, and MALDI-TOF MS Hyperspectral Images with Hamster Brain Tissue]. '''2017''' ''Anal. Chem''., 89, 9, 5008–5016

* P. Lasch & I. Noda. [https://doi.org/10.1177/0003702818819880 Two-Dimensional Correlation Spectroscopy (2D-COS) for Analysis of Spatially Resolved Vibrational Spectra]. '''2019''' ''Appl. Spectrosc''. 73(4): 359-379

   

Apr 09, 2025: more details of the ''mat2dcorr'' Matlab toolbox will follow

Frequently Asked Questions (FAQ)

2025-04-09T13:22:04Z

Laschp: /* Links to other non-commercial 2D-COS software solutions */

__FORCETOC__

== Can the mat2corr toolbox account for unevenly spaced sampling of spectra? ==

Unfortunately, the current version of the ''mat2dcorr'' toolbox (v. 1.05) does not consider non-equidistant vectors of the perturbing variable. However, this would be a good idea for future versions of the toolbox as an addition to its functionality.

In the literature, there are basically three different ways to deal with a perturbation that does not fulfil the equidistance condition:
:1. Ignore the requirement for equidistant perturbation values and use the data as they are. This is what the mat2dcorr toolbox v.1.05 does.
:2. Use modified correlation equations as described in [https://journals.sagepub.com/doi/10.1366/000370203322259039 Noda (2003)] to account for uneven sampling of the perturbation variable.
:3. Interpolate the perturbation values and the associated spectral data to get an equidistant distribution of the perturbation values.

Option 3 would be fairly easy to implement programmatically. It is already on the to-do list.

== I have loaded spectral data, but the buttons are still grayed out? ==

This is not a bug! It is important to understand that the mat2dcos toolbox was originally developed for heterospectral 2D correlation analysis (2D-COS). This means that spectral series from two different modalities are analyzed. For example, if IR and Raman data are to be analyzed by heterospectral 2D-COS, two different spectral series must be loaded into the toolbox.
For the probably more common case of autocorrelation 2D-COS, this means that the data set to be analyzed has to be loaded twice, once as an ''x-'' and a second time as a ''y-data'' set. Only then will the buttons be available for analysis.

== How to interpret intensity values of 2D correlation functions? ==

For example, the symmetric 2D function can be interpreted as a statistical value, namely the covariance between two variables. Unlike the correlation, the covariance is not normalized by the standard variation and can thus take very small or very large values. This depends, among other things, on the absolute values of the intensity changes in the spectra examined. For this reason, some researchers do not attach much importance to the absolute intensity values of the 2D-COS functions. It is more important that the maxima and minima of the 2D correlation functions are clearly above the noise, which can be checked by analyzing the correlation slices.

It is also interesting to note that the intensities of the 2D functions differ depending on the method used to calculate the 2D correlation functions (statistical or FFT based). This can be easily tested using the example data provided with the ''mat2dcorr'' toolbox. The different intensities obtained by the distinct 2D-COS functions are not due to programming errors and are not discussed in the scientific literature.

== Links to other non-commercial 2D-COS software solutions ==

* [https://sites.google.com/view/shigemorita/home/2dshige 2DShige], free software for two-dimensional correlation analysis developed by Prof. Shigeaki Morita (Osaka Electro-Communication University).
* [https://de.mathworks.com/matlabcentral/fileexchange/32384-midas-2010 MIDAS 2010], Matlab-based software tools developed in the Canadian Light Source for 2D spectroscopic analysis and data exploration of time resolved infrared spectra
* [https://arxiv.org/abs/1808.00685 corr2D (R)], - Implementation of two-dimensional correlation analysis in R, developed by Robert Geitner.

Frequently Asked Questions (FAQ)

2025-04-09T13:21:33Z

Laschp: /* How to interpret intensity values in 2D correlation functions? */

Frequently Asked Questions (FAQ)

2025-04-09T13:21:04Z

Laschp: /* I have loaded spectral data, but the buttons are still grayed out? */

__FORCETOC__

== Can the mat2corr toolbox account for unevenly spaced sampling of spectra? ==

Unfortunately, the current version of the ''mat2dcorr'' toolbox (v. 1.05) does not consider non-equidistant vectors of the perturbing variable. However, this would be a good idea for future versions of the toolbox as an addition to its functionality.

In the literature, there are basically three different ways to deal with a perturbation that does not fulfil the equidistance condition:
:1. Ignore the requirement for equidistant perturbation values and use the data as they are. This is what the mat2dcorr toolbox v.1.05 does.
:2. Use modified correlation equations as described in [https://journals.sagepub.com/doi/10.1366/000370203322259039 Noda (2003)] to account for uneven sampling of the perturbation variable.
:3. Interpolate the perturbation values and the associated spectral data to get an equidistant distribution of the perturbation values.

Option 3 would be fairly easy to implement programmatically. It is already on the to-do list.

== I have loaded spectral data, but the buttons are still grayed out? ==

This is not a bug! It is important to understand that the mat2dcos toolbox was originally developed for heterospectral 2D correlation analysis (2D-COS). This means that spectral series from two different modalities are analyzed. For example, if IR and Raman data are to be analyzed by heterospectral 2D-COS, two different spectral series must be loaded into the toolbox.
For the probably more common case of autocorrelation 2D-COS, this means that the data set to be analyzed has to be loaded twice, once as an ''x-'' and a second time as a ''y-data'' set. Only then will the buttons be available for analysis.

== How to interpret intensity values in 2D correlation functions? ==

For example, the symmetric 2D function can be interpreted as a statistical value, namely the covariance between two variables. Unlike the correlation, the covariance is not normalized by the standard variation and can thus take very small or very large values. This depends, among other things, on the absolute values of the intensity changes in the spectra examined. For this reason, some researchers do not attach much importance to the absolute intensity values of the 2D-COS functions. It is more important that the maxima and minima of the 2D correlation functions are clearly above the noise, which can be checked by analyzing the correlation slices.

It is also interesting to note that the intensities of the 2D functions differ depending on the method used to calculate the 2D correlation functions (statistical or FFT based). This can be easily tested using the example data provided with the ''mat2dcorr'' toolbox. The different intensities obtained by the distinct 2D-COS functions are not due to programming errors and are not discussed in the scientific literature.

== Links to other non-commercial 2D-COS software solutions ==

* [https://sites.google.com/view/shigemorita/home/2dshige 2DShige], free software for two-dimensional correlation analysis developed by Prof. Shigeaki Morita (Osaka Electro-Communication University).
* [https://de.mathworks.com/matlabcentral/fileexchange/32384-midas-2010 MIDAS 2010], Matlab-based software tools developed in the Canadian Light Source for 2D spectroscopic analysis and data exploration of time resolved infrared spectra
* [https://arxiv.org/abs/1808.00685 corr2D (R)], - Implementation of two-dimensional correlation analysis in R, developed by Robert Geitner.
 
(to be continued)

Frequently Asked Questions (FAQ)

2025-04-09T13:19:51Z

Laschp:

__FORCETOC__

== Can the mat2corr toolbox account for unevenly spaced sampling of spectra? ==

Unfortunately, the current version of the ''mat2dcorr'' toolbox (v. 1.05) does not consider non-equidistant vectors of the perturbing variable. However, this would be a good idea for future versions of the toolbox as an addition to its functionality.

In the literature, there are basically three different ways to deal with a perturbation that does not fulfil the equidistance condition:
:1. Ignore the requirement for equidistant perturbation values and use the data as they are. This is what the mat2dcorr toolbox v.1.05 does.
:2. Use modified correlation equations as described in [https://journals.sagepub.com/doi/10.1366/000370203322259039 Noda (2003)] to account for uneven sampling of the perturbation variable.
:3. Interpolate the perturbation values and the associated spectral data to get an equidistant distribution of the perturbation values.

Option 3 would be fairly easy to implement programmatically. It is already on the to-do list.

== I have loaded spectral data, but the buttons are still grayed out? ==

This is not a bug! It is important to understand that the mat2dcos toolbox was originally developed for heterospectral 2D correlation analysis (2D-COS). This means that spectral series from two different modalities are analyzed. For example, if IR and Raman data are to be analyzed by heterospectral 2D-COS, two different spectral series must be loaded into the toolbox.
For the probably more common case of autocorrelation 2D-COS, this means that the data set to be analyzed has to be loaded twice, once as x- and a second time as y-data set. Only then will the buttons be available for analysis.

== How to interpret intensity values in 2D correlation functions? ==

For example, the symmetric 2D function can be interpreted as a statistical value, namely the covariance between two variables. Unlike the correlation, the covariance is not normalized by the standard variation and can thus take very small or very large values. This depends, among other things, on the absolute values of the intensity changes in the spectra examined. For this reason, some researchers do not attach much importance to the absolute intensity values of the 2D-COS functions. It is more important that the maxima and minima of the 2D correlation functions are clearly above the noise, which can be checked by analyzing the correlation slices.

It is also interesting to note that the intensities of the 2D functions differ depending on the method used to calculate the 2D correlation functions (statistical or FFT based). This can be easily tested using the example data provided with the ''mat2dcorr'' toolbox. The different intensities obtained by the distinct 2D-COS functions are not due to programming errors and are not discussed in the scientific literature.

== Links to other non-commercial 2D-COS software solutions ==

* [https://sites.google.com/view/shigemorita/home/2dshige 2DShige], free software for two-dimensional correlation analysis developed by Prof. Shigeaki Morita (Osaka Electro-Communication University).
* [https://de.mathworks.com/matlabcentral/fileexchange/32384-midas-2010 MIDAS 2010], Matlab-based software tools developed in the Canadian Light Source for 2D spectroscopic analysis and data exploration of time resolved infrared spectra
* [https://arxiv.org/abs/1808.00685 corr2D (R)], - Implementation of two-dimensional correlation analysis in R, developed by Robert Geitner.
 
(to be continued)

Frequently Asked Questions (FAQ)

2025-04-09T13:02:16Z

Laschp:

Frequently Asked Questions (FAQ)

2025-04-09T13:00:36Z

Laschp:

Frequently Asked Questions (FAQ)

2025-04-09T12:53:34Z

Laschp:

__FORCETOC__

== Can the mat2corr toolbox account for the lack of equidistance of the perturbation variable? ==

Unfortunately, the current version of the ''mat2dcorr'' toolbox (v. 1.05) does not consider non-equidistant vectors of the perturbing variable. However, this would be a good idea for future versions of the toolbox as an addition to its functionality.
The literature mentions basically three different possibilities to deal with a perturbing variable that does not fulfill the condition of equidistance:
1. Ignore the requirement for equidistant perturbation values and use the data as it is. This is what the mat2dcorr toolbox does today
2. Use modified correlation equations as described in [https://journals.sagepub.com/doi/10.1366/000370203322259039 Noda (2003)] to account for the uneven sampling of the perturbation variable.
3. Interpolate the perturbation values and the associated spectral data to get an equidistant distribution of the perturbation values.

== I have loaded spectral data, but the buttons are still grayed out? ==

This is not a bug! It is important to understand that the mat2dcos toolbox was originally developed for heterospectral 2D correlation analysis (2D-COS). This means that spectral series from two different modalities are analyzed. For example, if IR and Raman data are to be analyzed by heterospectral 2D-COS, two different spectral series must be loaded into the toolbox.
For the probably more common case of autocorrelation 2D-COS, this means that the data set to be analyzed has to be loaded twice, once as x- and a second time as y-data set. Only then will the buttons be available for analysis.

== Links to other non-commercial 2D-COS software solutions ==

* [https://sites.google.com/view/shigemorita/home/2dshige 2DShige], free software for two-dimensional correlation analysis developed by Prof. Shigeaki Morita (Osaka Electro-Communication University).
* [https://de.mathworks.com/matlabcentral/fileexchange/32384-midas-2010 MIDAS 2010], Matlab-based software tools developed in the Canadian Light Source for 2D spectroscopic analysis and data exploration of time resolved infrared spectra
* [https://arxiv.org/abs/1808.00685 corr2D (R)], - Implementation of two-dimensional correlation analysis in R, developed by Robert Geitner.
 
(to be continued)

Frequently Asked Questions (FAQ)

2024-12-30T10:15:16Z

Laschp:

__FORCETOC__

== I have loaded spectral data, but the buttons are still grayed out? ==

This is not a bug! It is important to understand that the mat2dcos toolbox was originally developed for heterospectral 2D correlation analysis (2D-COS). This means that spectral series from two different modalities are analyzed. For example, if IR and Raman data are to be analyzed by heterospectral 2D-COS, two different spectral series must be loaded into the toolbox.
For the probably more common case of autocorrelation 2D-COS, this means that the data set to be analyzed has to be loaded twice, once as x- and a second time as y-data set. Only then will the buttons be available for analysis.

== Links to other non-commercial 2D-COS software solutions ==

* [https://sites.google.com/view/shigemorita/home/2dshige 2DShige], free software for two-dimensional correlation analysis developed by Prof. Shigeaki Morita (Osaka Electro-Communication University).
* [https://de.mathworks.com/matlabcentral/fileexchange/32384-midas-2010 MIDAS 2010], Matlab-based software tools developed in the Canadian Light Source for 2D spectroscopic analysis and data exploration of time resolved infrared spectra
* [https://arxiv.org/abs/1808.00685 corr2D (R)], - Implementation of two-dimensional correlation analysis in R, developed by Robert Geitner.
 
(to be continued)

Format of a 2D-COS Result File

2024-12-30T10:13:31Z

Laschp:

[[File:res-file-format.jpg|400px|thumb|Variables present in a result file stored by the mat2dcos toolbox]]

''So called'' 2D-COS result files can be stored by choosing the option ''save 2D spectrum'' from the ''File'' menu bar of the 2D-COS main window ([[Options_of_the_2D-COS_Main_Figure|2D-COS Main Figure]]). 

To open a 2D-COS result file ''NameOfResultFile.mat'' start Matlab, cd to the respective directory and type then 

>> load('NameOfResultFile');

at the command prompt. This will create a new structure array ''twoddata'' containing the following fields: 

<ul>
<li> ''data'' - the 2D correlation spectrum computed by utilizing the actual settings (spectral regions for 2D-COS analysis, type of reference spectrum, etc.). Of note, ''data'' is an array of float32 values of dimensions [nptsx, nptsy] with ''nptsx'' being the number of spectral data points in '''x''' and ''nptsy'' denoting the number of spectral data points in '''y''' </li>
<li> ''xwv'' - the spectral vector '''x''' (float32 values) with a length of ''nptsx'' </li>
<li> ''ywv'' - the spectral vector '''y''' (float32 values) with a length of ''nptsy'' </li>
<li> ''spcx'' - the 2D spectra array ('''x''') a matrix of float32 values with the dimensions [''nobs, nptsx''], ''nobs'' corresponds to the number of observations (i.e. of spectra) </li>
<li> ''spcx'' - the 2D spectra array ('''y''') a matrix of float32 values with the dimensions [''nobs, nptsy''], ''nobs'' corresponds to the number of observations (i.e. of spectra) </li>
<li> ''info'' - a char array denoting the type of 2D correlation spectrum (synchronous, asynchronous, disrelation, Pareto, etc.)</li>
<li> ''xpts'' - a set of two float32 values representing the first and last spectral data point of the x-vector used in the current 2D-COS analysis </li>
<li> ''ypts'' - a set of two float32 values representing the first and last spectral data point of the y-vector used in the current 2D-COS analysis </li>
</ul>

== Related links ==

* [[Matlab_Imaging_Format|Import data in the CytoSpec imaging format (CytoSpec/Matlab)]]
* [[Excel_Trace_Format|Import data in the MS Excel data format]]
* [[Matlab_Trace_Format|Import data in the trace format (Matlab)]]

Format of a 2D-COS Result File

2024-12-30T10:12:58Z

Laschp:

[[File:res-file-format.jpg|400px|thumb|Variables present in a result file stored by the mat2dcos toolbox]]

''So called'' 2D-COS result files can be stored by choosing the option ''save 2D spectrum'' from the ''File'' menu bar of the 2D-COS main window ([[Options_of_the_2D-COS_Main_Figure|2D-COS Main Figure]]). 

To open a 2D-COS result file start Matlab, cd to the respective directory and type then 

>> load('NameOfResultFile');

at the command prompt. This will create a new structure array ''twoddata'' containing the following fields: 

<ul>
<li> ''data'' - the 2D correlation spectrum computed by utilizing the actual settings (spectral regions for 2D-COS analysis, type of reference spectrum, etc.). Of note, ''data'' is an array of float32 values of dimensions [nptsx, nptsy] with ''nptsx'' being the number of spectral data points in '''x''' and ''nptsy'' denoting the number of spectral data points in '''y''' </li>
<li> ''xwv'' - the spectral vector '''x''' (float32 values) with a length of ''nptsx'' </li>
<li> ''ywv'' - the spectral vector '''y''' (float32 values) with a length of ''nptsy'' </li>
<li> ''spcx'' - the 2D spectra array ('''x''') a matrix of float32 values with the dimensions [''nobs, nptsx''], ''nobs'' corresponds to the number of observations (i.e. of spectra) </li>
<li> ''spcx'' - the 2D spectra array ('''y''') a matrix of float32 values with the dimensions [''nobs, nptsy''], ''nobs'' corresponds to the number of observations (i.e. of spectra) </li>
<li> ''info'' - a char array denoting the type of 2D correlation spectrum (synchronous, asynchronous, disrelation, Pareto, etc.)</li>
<li> ''xpts'' - a set of two float32 values representing the first and last spectral data point of the x-vector used in the current 2D-COS analysis </li>
<li> ''ypts'' - a set of two float32 values representing the first and last spectral data point of the y-vector used in the current 2D-COS analysis </li>
</ul>

== Related links ==

* [[Matlab_Imaging_Format|Import data in the CytoSpec imaging format (CytoSpec/Matlab)]]
* [[Excel_Trace_Format|Import data in the MS Excel data format]]
* [[Matlab_Trace_Format|Import data in the trace format (Matlab)]]

Mat2dcorr - A Matlab Toolbox for 2D-COS

2024-12-30T10:10:13Z

Laschp:

__FORCETOC__

[[File:GraphAbstr.gif|400px|thumb|mat2dcorr: Illustration of heterospectral 2D-COS (FTIR vs. Raman)]]
[[File:2D-COS.jpg|400px|thumb|Mat2Dcorr: Screenshot of the 2D control window (left) and the window '2D correlation analysis ... ' (right)]]

mat2dcorr - a free toolbox for performing two-dimensional correlation (2D-COS) analysis with [https://www.mathworks.com Matlab]. 

== Introduction ==

Since its introduction in 1989, two-dimensional correlation spectroscopy (2D-COS), sometimes referred to as two-dimensional correlation analysis, has become an invaluable analytical tool for spectroscopic characterization in various fields of application, including protein science, pharmacy, biomedical applications, and polymer or nanomaterial research. The technique of 2D-COS analysis was originally developed to study dynamic processes in which a spectroscopic probe is used to monitor the effect of an external perturbation on a given system. The system responds to the perturbation with characteristic changes that are monitored spectroscopically, e.g. by continuous acquisition of a spectral time series. The main purpose of 2D-COS investigations is to explore correlations that may exist between perturbation-induced spectral responses. In some situations, the 2D-COS analysis technique is useful not only to detect and visualize such correlations, but also to decipher the sequence of these spectral changes. Sources of perturbation may include changes in temperature, pH, pressure, concentration, or spatial coordinates when examining spatially heterogeneous samples. The 2D correlation concept has proven to be extremely useful in a wide variety of molecular spectroscopy applications based on IR, NIR, Raman, NMR, fluorescence and UV-visible spectroscopy.

An introduction into the history, mathematics and basic principles of 2D-COS can be found here: https://en.wikipedia.org/wiki/Two-dimensional_correlation_analysis

== Getting Started ==
* [[Computer_Specification|Specification of computer configuration]]
* [[Screenshot_of_mat2dcorr|Screenshot of the mat2dcorr gui]]
* Downloading mat2dcorr - [[How_to_obtain_the_mat2dcorr_toolbox|How to obtain the mat2dcorr toolbox?]]
* [[Installation_of_the_mat2dcorr_toolbox|Installation of the mat2dcorr toolbox]]
* [[Disclaimer_and_License_Conditions |Disclaimer and license conditions]]
* [[mat2dcorr_-_Relevant Publications|Acknowledgement, relevant publications]]
* [[Frequently_Asked_Questions_(FAQ)|Frequently asked questions (FAQ)]]

== Data Import & File Formats ==

* [[Matlab_Imaging_Format|Import data in the CytoSpec imaging format (CytoSpec/Matlab)]]
* [[Matlab_Trace_Format|Import data in the trace format (Matlab)]]
* [[Excel_Trace_Format|Import data in the MS Excel data format]]
* [[Format_of_a_2D-COS_Result_File|Format of a 2D-COS result file (Matlab)]]

== Options of the mat2dcorr Toolbox ==

* [[Options_of_the_2D-COS_Main_Figure| Options of the 2D-COS main figure]]
* [[Options_of_the_2D-COS_Control_Window| Options of the 2D-COS control window]]

== Related Publications and Web Links ==

* [https://en.wikipedia.org/wiki/Two-dimensional_correlation_analysis Two-dimensional correlation analysis] (Wikipedia)

* I. Noda. [https://doi.org/10.1366/0003702904087398 Two-Dimensional Infrared (2D IR) Spectroscopy: Theory and Applications], '''1990''' ''Appl. Spectrosc''. 44(4): 550-561

* I. Noda. [https://doi.org/10.1366/0003702934067694 Generalized Two- Dimensional Correlation Method Applicable to Infrared, Raman, and other Types of Spectroscopy], '''1993''' ''Appl. Spectrosc''. 47(9): 1329-1336

* I. Noda. [https://doi.org/10.1366/0003702001950472 Determination of Two-Dimensional Correlation Spectra Using the Hilbert Transform], '''2000''' ''Appl. Spectrosc''. 54(7): 994-999

* P. Lasch & I. Noda. [https://doi.org/10.1021/acs.analchem.7b00332 Two-Dimensional Correlation Spectroscopy for Multimodal Analysis of FT-IR, Raman, and MALDI-TOF MS Hyperspectral Images with Hamster Brain Tissue]. '''2017''' ''Anal. Chem''., 89, 9, 5008–5016

* P. Lasch & I. Noda. [https://doi.org/10.1177/0003702818819880 Two-Dimensional Correlation Spectroscopy (2D-COS) for Analysis of Spatially Resolved Vibrational Spectra]. '''2019''' ''Appl. Spectrosc''. 73(4): 359-379

   

Dec 22, 2024: more details of the mat2dcorr Matlab toolbox will follow

Mat2dcorr - A Matlab Toolbox for 2D-COS

2024-12-30T10:09:46Z

Laschp:

__FORCETOC__

[[File:GraphAbstr.gif|500px|thumb|mat2dcorr: Illustration of heterospectral 2D-COS (FTIR vs. Raman)]]
[[File:2D-COS.jpg|500px|thumb|Mat2Dcorr: Screenshot of the 2D control window (left) and the window '2D correlation analysis ... ' (right)]]

mat2dcorr - a free toolbox for performing two-dimensional correlation (2D-COS) analysis with [https://www.mathworks.com Matlab]. 

== Introduction ==

Since its introduction in 1989, two-dimensional correlation spectroscopy (2D-COS), sometimes referred to as two-dimensional correlation analysis, has become an invaluable analytical tool for spectroscopic characterization in various fields of application, including protein science, pharmacy, biomedical applications, and polymer or nanomaterial research. The technique of 2D-COS analysis was originally developed to study dynamic processes in which a spectroscopic probe is used to monitor the effect of an external perturbation on a given system. The system responds to the perturbation with characteristic changes that are monitored spectroscopically, e.g. by continuous acquisition of a spectral time series. The main purpose of 2D-COS investigations is to explore correlations that may exist between perturbation-induced spectral responses. In some situations, the 2D-COS analysis technique is useful not only to detect and visualize such correlations, but also to decipher the sequence of these spectral changes. Sources of perturbation may include changes in temperature, pH, pressure, concentration, or spatial coordinates when examining spatially heterogeneous samples. The 2D correlation concept has proven to be extremely useful in a wide variety of molecular spectroscopy applications based on IR, NIR, Raman, NMR, fluorescence and UV-visible spectroscopy.

An introduction into the history, mathematics and basic principles of 2D-COS can be found here: https://en.wikipedia.org/wiki/Two-dimensional_correlation_analysis

== Getting Started ==
* [[Computer_Specification|Specification of computer configuration]]
* [[Screenshot_of_mat2dcorr|Screenshot of the mat2dcorr gui]]
* Downloading mat2dcorr - [[How_to_obtain_the_mat2dcorr_toolbox|How to obtain the mat2dcorr toolbox?]]
* [[Installation_of_the_mat2dcorr_toolbox|Installation of the mat2dcorr toolbox]]
* [[Disclaimer_and_License_Conditions |Disclaimer and license conditions]]
* [[mat2dcorr_-_Relevant Publications|Acknowledgement, relevant publications]]
* [[Frequently_Asked_Questions_(FAQ)|Frequently asked questions (FAQ)]]

== Data Import & File Formats ==

* [[Matlab_Imaging_Format|Import data in the CytoSpec imaging format (CytoSpec/Matlab)]]
* [[Matlab_Trace_Format|Import data in the trace format (Matlab)]]
* [[Excel_Trace_Format|Import data in the MS Excel data format]]
* [[Format_of_a_2D-COS_Result_File|Format of a 2D-COS result file (Matlab)]]

== Options of the mat2dcorr Toolbox ==

* [[Options_of_the_2D-COS_Main_Figure| Options of the 2D-COS main figure]]
* [[Options_of_the_2D-COS_Control_Window| Options of the 2D-COS control window]]

== Related Publications and Web Links ==

* [https://en.wikipedia.org/wiki/Two-dimensional_correlation_analysis Two-dimensional correlation analysis] (Wikipedia)

* I. Noda. [https://doi.org/10.1366/0003702904087398 Two-Dimensional Infrared (2D IR) Spectroscopy: Theory and Applications], '''1990''' ''Appl. Spectrosc''. 44(4): 550-561

* I. Noda. [https://doi.org/10.1366/0003702934067694 Generalized Two- Dimensional Correlation Method Applicable to Infrared, Raman, and other Types of Spectroscopy], '''1993''' ''Appl. Spectrosc''. 47(9): 1329-1336

* I. Noda. [https://doi.org/10.1366/0003702001950472 Determination of Two-Dimensional Correlation Spectra Using the Hilbert Transform], '''2000''' ''Appl. Spectrosc''. 54(7): 994-999

* P. Lasch & I. Noda. [https://doi.org/10.1021/acs.analchem.7b00332 Two-Dimensional Correlation Spectroscopy for Multimodal Analysis of FT-IR, Raman, and MALDI-TOF MS Hyperspectral Images with Hamster Brain Tissue]. '''2017''' ''Anal. Chem''., 89, 9, 5008–5016

* P. Lasch & I. Noda. [https://doi.org/10.1177/0003702818819880 Two-Dimensional Correlation Spectroscopy (2D-COS) for Analysis of Spatially Resolved Vibrational Spectra]. '''2019''' ''Appl. Spectrosc''. 73(4): 359-379

   

Dec 22, 2024: more details of the mat2dcorr Matlab toolbox will follow

Mat2dcorr - A Matlab Toolbox for 2D-COS

2024-12-30T10:09:10Z

Laschp: /* Related Publications and Web Links */

__FORCETOC__
mat2dcorr - a free toolbox for performing two-dimensional correlation (2D-COS) analysis with [https://www.mathworks.com Matlab]. 

== Introduction ==

[[File:GraphAbstr.gif|500px|thumb|mat2dcorr: Illustration of heterospectral 2D-COS (FTIR vs. Raman)]]
[[File:2D-COS.jpg|500px|thumb|Mat2Dcorr: Screenshot of the 2D control window (left) and the window '2D correlation analysis ... ' (right)]]

Since its introduction in 1989, two-dimensional correlation spectroscopy (2D-COS), sometimes referred to as two-dimensional correlation analysis, has become an invaluable analytical tool for spectroscopic characterization in various fields of application, including protein science, pharmacy, biomedical applications, and polymer or nanomaterial research. The technique of 2D-COS analysis was originally developed to study dynamic processes in which a spectroscopic probe is used to monitor the effect of an external perturbation on a given system. The system responds to the perturbation with characteristic changes that are monitored spectroscopically, e.g. by continuous acquisition of a spectral time series. The main purpose of 2D-COS investigations is to explore correlations that may exist between perturbation-induced spectral responses. In some situations, the 2D-COS analysis technique is useful not only to detect and visualize such correlations, but also to decipher the sequence of these spectral changes. Sources of perturbation may include changes in temperature, pH, pressure, concentration, or spatial coordinates when examining spatially heterogeneous samples. The 2D correlation concept has proven to be extremely useful in a wide variety of molecular spectroscopy applications based on IR, NIR, Raman, NMR, fluorescence and UV-visible spectroscopy.

An introduction into the history, mathematics and basic principles of 2D-COS can be found here: https://en.wikipedia.org/wiki/Two-dimensional_correlation_analysis

== Getting Started ==
* [[Computer_Specification|Specification of computer configuration]]
* [[Screenshot_of_mat2dcorr|Screenshot of the mat2dcorr gui]]
* Downloading mat2dcorr - [[How_to_obtain_the_mat2dcorr_toolbox|How to obtain the mat2dcorr toolbox?]]
* [[Installation_of_the_mat2dcorr_toolbox|Installation of the mat2dcorr toolbox]]
* [[Disclaimer_and_License_Conditions |Disclaimer and license conditions]]
* [[mat2dcorr_-_Relevant Publications|Acknowledgement, relevant publications]]
* [[Frequently_Asked_Questions_(FAQ)|Frequently asked questions (FAQ)]]

== Data Import & File Formats ==

* [[Matlab_Imaging_Format|Import data in the CytoSpec imaging format (CytoSpec/Matlab)]]
* [[Matlab_Trace_Format|Import data in the trace format (Matlab)]]
* [[Excel_Trace_Format|Import data in the MS Excel data format]]
* [[Format_of_a_2D-COS_Result_File|Format of a 2D-COS result file (Matlab)]]

== Options of the mat2dcorr Toolbox ==

* [[Options_of_the_2D-COS_Main_Figure| Options of the 2D-COS main figure]]
* [[Options_of_the_2D-COS_Control_Window| Options of the 2D-COS control window]]

== Related Publications and Web Links ==

* [https://en.wikipedia.org/wiki/Two-dimensional_correlation_analysis Two-dimensional correlation analysis] (Wikipedia)

* I. Noda. [https://doi.org/10.1366/0003702904087398 Two-Dimensional Infrared (2D IR) Spectroscopy: Theory and Applications], '''1990''' ''Appl. Spectrosc''. 44(4): 550-561

* I. Noda. [https://doi.org/10.1366/0003702934067694 Generalized Two- Dimensional Correlation Method Applicable to Infrared, Raman, and other Types of Spectroscopy], '''1993''' ''Appl. Spectrosc''. 47(9): 1329-1336

* I. Noda. [https://doi.org/10.1366/0003702001950472 Determination of Two-Dimensional Correlation Spectra Using the Hilbert Transform], '''2000''' ''Appl. Spectrosc''. 54(7): 994-999

* P. Lasch & I. Noda. [https://doi.org/10.1021/acs.analchem.7b00332 Two-Dimensional Correlation Spectroscopy for Multimodal Analysis of FT-IR, Raman, and MALDI-TOF MS Hyperspectral Images with Hamster Brain Tissue]. '''2017''' ''Anal. Chem''., 89, 9, 5008–5016

* P. Lasch & I. Noda. [https://doi.org/10.1177/0003702818819880 Two-Dimensional Correlation Spectroscopy (2D-COS) for Analysis of Spatially Resolved Vibrational Spectra]. '''2019''' ''Appl. Spectrosc''. 73(4): 359-379

   

Dec 22, 2024: more details of the mat2dcorr Matlab toolbox will follow

Mat2dcorr - A Matlab Toolbox for 2D-COS

2024-12-30T10:08:54Z

Laschp: /* Introduction */

__FORCETOC__
mat2dcorr - a free toolbox for performing two-dimensional correlation (2D-COS) analysis with [https://www.mathworks.com Matlab]. 

== Introduction ==

[[File:GraphAbstr.gif|500px|thumb|mat2dcorr: Illustration of heterospectral 2D-COS (FTIR vs. Raman)]]
[[File:2D-COS.jpg|500px|thumb|Mat2Dcorr: Screenshot of the 2D control window (left) and the window '2D correlation analysis ... ' (right)]]

Since its introduction in 1989, two-dimensional correlation spectroscopy (2D-COS), sometimes referred to as two-dimensional correlation analysis, has become an invaluable analytical tool for spectroscopic characterization in various fields of application, including protein science, pharmacy, biomedical applications, and polymer or nanomaterial research. The technique of 2D-COS analysis was originally developed to study dynamic processes in which a spectroscopic probe is used to monitor the effect of an external perturbation on a given system. The system responds to the perturbation with characteristic changes that are monitored spectroscopically, e.g. by continuous acquisition of a spectral time series. The main purpose of 2D-COS investigations is to explore correlations that may exist between perturbation-induced spectral responses. In some situations, the 2D-COS analysis technique is useful not only to detect and visualize such correlations, but also to decipher the sequence of these spectral changes. Sources of perturbation may include changes in temperature, pH, pressure, concentration, or spatial coordinates when examining spatially heterogeneous samples. The 2D correlation concept has proven to be extremely useful in a wide variety of molecular spectroscopy applications based on IR, NIR, Raman, NMR, fluorescence and UV-visible spectroscopy.

An introduction into the history, mathematics and basic principles of 2D-COS can be found here: https://en.wikipedia.org/wiki/Two-dimensional_correlation_analysis

== Getting Started ==
* [[Computer_Specification|Specification of computer configuration]]
* [[Screenshot_of_mat2dcorr|Screenshot of the mat2dcorr gui]]
* Downloading mat2dcorr - [[How_to_obtain_the_mat2dcorr_toolbox|How to obtain the mat2dcorr toolbox?]]
* [[Installation_of_the_mat2dcorr_toolbox|Installation of the mat2dcorr toolbox]]
* [[Disclaimer_and_License_Conditions |Disclaimer and license conditions]]
* [[mat2dcorr_-_Relevant Publications|Acknowledgement, relevant publications]]
* [[Frequently_Asked_Questions_(FAQ)|Frequently asked questions (FAQ)]]

== Data Import & File Formats ==

* [[Matlab_Imaging_Format|Import data in the CytoSpec imaging format (CytoSpec/Matlab)]]
* [[Matlab_Trace_Format|Import data in the trace format (Matlab)]]
* [[Excel_Trace_Format|Import data in the MS Excel data format]]
* [[Format_of_a_2D-COS_Result_File|Format of a 2D-COS result file (Matlab)]]

== Options of the mat2dcorr Toolbox ==

* [[Options_of_the_2D-COS_Main_Figure| Options of the 2D-COS main figure]]
* [[Options_of_the_2D-COS_Control_Window| Options of the 2D-COS control window]]

== Related Publications and Web Links ==

[[File:2D-COS.jpg|650px|thumb|Mat2dcorr: Screenshot of the 2D control window (left) and the window ''2D correlation
analysis ...'' (right)]]

* [https://en.wikipedia.org/wiki/Two-dimensional_correlation_analysis Two-dimensional correlation analysis] (Wikipedia)

* I. Noda. [https://doi.org/10.1366/0003702904087398 Two-Dimensional Infrared (2D IR) Spectroscopy: Theory and Applications], '''1990''' ''Appl. Spectrosc''. 44(4): 550-561

* I. Noda. [https://doi.org/10.1366/0003702934067694 Generalized Two- Dimensional Correlation Method Applicable to Infrared, Raman, and other Types of Spectroscopy], '''1993''' ''Appl. Spectrosc''. 47(9): 1329-1336

* I. Noda. [https://doi.org/10.1366/0003702001950472 Determination of Two-Dimensional Correlation Spectra Using the Hilbert Transform], '''2000''' ''Appl. Spectrosc''. 54(7): 994-999

* P. Lasch & I. Noda. [https://doi.org/10.1021/acs.analchem.7b00332 Two-Dimensional Correlation Spectroscopy for Multimodal Analysis of FT-IR, Raman, and MALDI-TOF MS Hyperspectral Images with Hamster Brain Tissue]. '''2017''' ''Anal. Chem''., 89, 9, 5008–5016

* P. Lasch & I. Noda. [https://doi.org/10.1177/0003702818819880 Two-Dimensional Correlation Spectroscopy (2D-COS) for Analysis of Spatially Resolved Vibrational Spectra]. '''2019''' ''Appl. Spectrosc''. 73(4): 359-379

   

Dec 22, 2024: more details of the mat2dcorr Matlab toolbox will follow

Installation of the mat2dcorr toolbox

2024-12-30T10:07:52Z

Laschp: /* Installation */

__FORCETOC__
To download go to [http://www.peter-lasch.de/2dcorr/ http://www.peter-lasch.de/2dcorr/]

== Required files / Software / OS ==

[[File:index-of-2dcorr.jpg|500px|thumb|mat2Dcorr: index of http://www.peter-lasch.de/2dcorr]]

[[File:2D-COS.jpg|500px|thumb|Mat2Dcorr: Screenshot of the 2D control window (left) and the window '2D correlation analysis ... ' (right)]]

'''Program Files''' of the mat2Dcorr toolbox:
<ol>
<li> mat2dcorr.m</li>
<li> updcorrplt.m</li>
<li> resiz2dfig.m</li>
<li> readme.txt</li>
</ol>

'''Software''':
<ul>
<li> [https://www.mathworks.com Matlab] R2014a or newer</li>
</ul>

'''Operating Systems''':
<ul>
<li> Windows 10</li>
<li> LINUX (Debian Buster/Bullseye/Bookworm)</li>
</ul>

== Extra files (spectral data files) ==

<ol start="5">
<li> linescandata.mat - example data file in the trace (2D) data format, see [[Matlab_Trace_Format| Matlab trace data format]] </li>
<li> linescandata.xlsx - example data file in the MS Excel format, see [[Excel_Trace_Format| MS Excel data format]]</li>
<li> IR-cerebellum-100x100.mat - example data file in the hyperspectral imaging (HSI) data format (FT-IR spectra from hamster cerebellum), see [[Matlab_Imaging_Format| Matlab imaging data format ]]</li>
<li> IR-cerebellum-50x50.mat - example data (50 x 50 spectra, interpolated), [[Matlab_Imaging_Format| Matlab imaging data format ]]</li>
<li> MALDI-cerebellum-100x100.mat - example data file (100 x 100 spectra) in the HSI data format (MALDI-TOF mass spectra from hamster cerebellum), [[Matlab_Imaging_Format| Matlab imaging data format ]]</li>
<li> MALDI-cerebellum-50x50.mat - example data file (50 x 50 spectra, interpolated), [[Matlab_Imaging_Format| Matlab imaging data format ]]</li>
<li> Raman-cerebellum-100x100.mat - example data file (100 x 100 spectra) in the HSI data format (MALDI-TOF mass spectra from hamster cerebellum), [[Matlab_Imaging_Format| Matlab imaging data format ]]</li>
<li> Raman-cerebellum-50x50.mat - example data file (50 x 50 spectra, interpolated), [[Matlab_Imaging_Format| Matlab imaging data format ]]</li>
<li> fftsyncro.mat - result file of 2D correlation analysis by the mat2dcorr toolbox using the FFT 2D-COS option, for details of the result file format refer also to the section [[Format_of_a_2D-COS_Result_File|format of a 2D-COS result file (Matlab)]]</li>
<li> statssyncro.mat - result file of 2D correlation analysis by the mat2dcorr toolbox using the statistical 2D-COS option, for details of the result file format refer also to the section [[Format_of_a_2D-COS_Result_File|format of a 2D-COS result file (Matlab)]]</li>
</ol>

== Installation ==

* Download files 1.-4. + some test data (see above) and and copy them into one single directory, for example into a directory ''mat2dcos''.
* Add then this directory to Matlab's search path. For this start Matlab and type 

>> addpath('PathToMat2dcorrToolbox')

at the Matlab command prompt, where ''PathToMat2dcorrToolbox'' denotes the full path where the the toolbox resides (i.e. ''mat2dcos''). 
Example of Mat2DcorrPath: ''C:\Users\Tim\Documents\Matlab\mat2dcos''

* To start mat2Dcorr type 

>> mat2dcorr

at the Matlab prompt. 

This will open two different windows:
# A window entitled '2D correlation analysis ... ' which shows the 2D correlation spectrum and mean spectra obtained from the x- and y-spectral data
# The 2D-COS control window, the user dialog box, see screenshot below

Installation of the mat2dcorr toolbox

2024-12-30T10:07:36Z

Laschp: /* Required files / Software / OS */

Installation of the mat2dcorr toolbox

2024-12-30T10:07:14Z

Laschp: /* Required files / Software / OS */

__FORCETOC__
To download go to [http://www.peter-lasch.de/2dcorr/ http://www.peter-lasch.de/2dcorr/]

== Required files / Software / OS ==

[[File:index-of-2dcorr.jpg|500px|thumb|mat2Dcorr: index of http://www.peter-lasch.de/2dcorr]]

[[File:2D-COS.jpg|500px|thumb|left|Mat2Dcorr: Screenshot of the 2D control window (left) and the window '2D correlation analysis ... ' (right)]]

'''Program Files''' of the mat2Dcorr toolbox:
<ol>
<li> mat2dcorr.m</li>
<li> updcorrplt.m</li>
<li> resiz2dfig.m</li>
<li> readme.txt</li>
</ol>

'''Software''':
<ul>
<li> [https://www.mathworks.com Matlab] R2014a or newer</li>
</ul>

'''Operating Systems''':
<ul>
<li> Windows 10</li>
<li> LINUX (Debian Buster/Bullseye/Bookworm)</li>
</ul>

== Extra files (spectral data files) ==

<ol start="5">
<li> linescandata.mat - example data file in the trace (2D) data format, see [[Matlab_Trace_Format| Matlab trace data format]] </li>
<li> linescandata.xlsx - example data file in the MS Excel format, see [[Excel_Trace_Format| MS Excel data format]]</li>
<li> IR-cerebellum-100x100.mat - example data file in the hyperspectral imaging (HSI) data format (FT-IR spectra from hamster cerebellum), see [[Matlab_Imaging_Format| Matlab imaging data format ]]</li>
<li> IR-cerebellum-50x50.mat - example data (50 x 50 spectra, interpolated), [[Matlab_Imaging_Format| Matlab imaging data format ]]</li>
<li> MALDI-cerebellum-100x100.mat - example data file (100 x 100 spectra) in the HSI data format (MALDI-TOF mass spectra from hamster cerebellum), [[Matlab_Imaging_Format| Matlab imaging data format ]]</li>
<li> MALDI-cerebellum-50x50.mat - example data file (50 x 50 spectra, interpolated), [[Matlab_Imaging_Format| Matlab imaging data format ]]</li>
<li> Raman-cerebellum-100x100.mat - example data file (100 x 100 spectra) in the HSI data format (MALDI-TOF mass spectra from hamster cerebellum), [[Matlab_Imaging_Format| Matlab imaging data format ]]</li>
<li> Raman-cerebellum-50x50.mat - example data file (50 x 50 spectra, interpolated), [[Matlab_Imaging_Format| Matlab imaging data format ]]</li>
<li> fftsyncro.mat - result file of 2D correlation analysis by the mat2dcorr toolbox using the FFT 2D-COS option, for details of the result file format refer also to the section [[Format_of_a_2D-COS_Result_File|format of a 2D-COS result file (Matlab)]]</li>
<li> statssyncro.mat - result file of 2D correlation analysis by the mat2dcorr toolbox using the statistical 2D-COS option, for details of the result file format refer also to the section [[Format_of_a_2D-COS_Result_File|format of a 2D-COS result file (Matlab)]]</li>
</ol>

== Installation ==

* Download files 1.-4. + some test data (see above) and and copy them into one single directory, for example into a directory ''mat2dcos''.
* Add then this directory to Matlab's search path. For this start Matlab and type 

>> addpath('PathToMat2dcorrToolbox')

at the Matlab command prompt, where ''PathToMat2dcorrToolbox'' denotes the full path where the the toolbox resides (i.e. ''mat2dcos''). 
Example of Mat2DcorrPath: ''C:\Users\Tim\Documents\Matlab\mat2dcos''

* To start mat2Dcorr type 

>> mat2dcorr

at the Matlab prompt. 

This will open two different windows:
# A window entitled '2D correlation analysis ... ' which shows the 2D correlation spectrum and mean spectra obtained from the x- and y-spectral data
# The 2D-COS control window, the user dialog box, see screenshot below

[[File:2D-COS.jpg|800px|thumb|left|Mat2Dcorr: Screenshot of the 2D control window (left) and the window '2D correlation analysis ... ' (right)]]

Installation of the mat2dcorr toolbox

2024-12-30T10:06:35Z

Laschp: /* Installation */

__FORCETOC__
To download go to [http://www.peter-lasch.de/2dcorr/ http://www.peter-lasch.de/2dcorr/]

== Required files / Software / OS ==

[[File:index-of-2dcorr.jpg|500px|thumb|mat2Dcorr: index of http://www.peter-lasch.de/2dcorr]]

'''Program Files''' of the mat2Dcorr toolbox:
<ol>
<li> mat2dcorr.m</li>
<li> updcorrplt.m</li>
<li> resiz2dfig.m</li>
<li> readme.txt</li>
</ol>

'''Software''':
<ul>
<li> [https://www.mathworks.com Matlab] R2014a or newer</li>
</ul>

'''Operating Systems''':
<ul>
<li> Windows 10</li>
<li> LINUX (Debian Buster/Bullseye/Bookworm)</li>
</ul>

== Extra files (spectral data files) ==

<ol start="5">
<li> linescandata.mat - example data file in the trace (2D) data format, see [[Matlab_Trace_Format| Matlab trace data format]] </li>
<li> linescandata.xlsx - example data file in the MS Excel format, see [[Excel_Trace_Format| MS Excel data format]]</li>
<li> IR-cerebellum-100x100.mat - example data file in the hyperspectral imaging (HSI) data format (FT-IR spectra from hamster cerebellum), see [[Matlab_Imaging_Format| Matlab imaging data format ]]</li>
<li> IR-cerebellum-50x50.mat - example data (50 x 50 spectra, interpolated), [[Matlab_Imaging_Format| Matlab imaging data format ]]</li>
<li> MALDI-cerebellum-100x100.mat - example data file (100 x 100 spectra) in the HSI data format (MALDI-TOF mass spectra from hamster cerebellum), [[Matlab_Imaging_Format| Matlab imaging data format ]]</li>
<li> MALDI-cerebellum-50x50.mat - example data file (50 x 50 spectra, interpolated), [[Matlab_Imaging_Format| Matlab imaging data format ]]</li>
<li> Raman-cerebellum-100x100.mat - example data file (100 x 100 spectra) in the HSI data format (MALDI-TOF mass spectra from hamster cerebellum), [[Matlab_Imaging_Format| Matlab imaging data format ]]</li>
<li> Raman-cerebellum-50x50.mat - example data file (50 x 50 spectra, interpolated), [[Matlab_Imaging_Format| Matlab imaging data format ]]</li>
<li> fftsyncro.mat - result file of 2D correlation analysis by the mat2dcorr toolbox using the FFT 2D-COS option, for details of the result file format refer also to the section [[Format_of_a_2D-COS_Result_File|format of a 2D-COS result file (Matlab)]]</li>
<li> statssyncro.mat - result file of 2D correlation analysis by the mat2dcorr toolbox using the statistical 2D-COS option, for details of the result file format refer also to the section [[Format_of_a_2D-COS_Result_File|format of a 2D-COS result file (Matlab)]]</li>
</ol>

== Installation ==

* Download files 1.-4. + some test data (see above) and and copy them into one single directory, for example into a directory ''mat2dcos''.
* Add then this directory to Matlab's search path. For this start Matlab and type 

>> addpath('PathToMat2dcorrToolbox')

at the Matlab command prompt, where ''PathToMat2dcorrToolbox'' denotes the full path where the the toolbox resides (i.e. ''mat2dcos''). 
Example of Mat2DcorrPath: ''C:\Users\Tim\Documents\Matlab\mat2dcos''

* To start mat2Dcorr type 

>> mat2dcorr

at the Matlab prompt. 

This will open two different windows:
# A window entitled '2D correlation analysis ... ' which shows the 2D correlation spectrum and mean spectra obtained from the x- and y-spectral data
# The 2D-COS control window, the user dialog box, see screenshot below

[[File:2D-COS.jpg|800px|thumb|left|Mat2Dcorr: Screenshot of the 2D control window (left) and the window '2D correlation analysis ... ' (right)]]

Screenshot of mat2dcorr

2024-12-22T16:22:01Z

Laschp:

[[File:Screenshot-mat2dcorr-gui.jpg|922px|thumb|left|Screenshot of the mat2dcorr gui (version Apr 02, 2023)]]

Options of the 2D-COS Main Figure

2024-12-22T16:09:09Z

Laschp: /* Menu bar options */

[[File:2dcos-gui.jpg|400px|thumb|mat2dcorr: screenshot of the main figure]]
[[File:2dcos-x-trace.jpg|400px|thumb|mat2dcorr: screenshot of a 1D correlation slice produced by mat2dcorr]]
[[File:xy-feature-plot.jpg|400px|thumb|mat2dcorr: screenshot of a [x,y] feature plot window]]

== Menu bar options ==

'''File'''
: '''''save 2D spectrum''''' - stores the 2D spectrum and corresponding metadata, for details see section [[Format_of_a_2D-COS_Result_File|Format of a 2D-COS result file (Matlab)]]
: '''''clear''''' - data are deleted from memory and figures are set back to their initial state
: '''''quit''''' - exit the program

'''Load data'''
: '''''Matlab imaging forma'''''t: see [[Matlab_Imaging_Format|Import data in the imaging format (Matlab)]]
: '''''Matlab trace format''''': see [[Matlab_Trace_Format|Import data in the trace format (Matlab)]]
: '''''Excel data format''''': see [[Excel_Trace_Format|Import data in the MS Excel data format]]

'''Action'''
: '''''plot 2D spectrum''''': computes the 2D correlation spectrum using the actual settings of the [[Options_of_the_2D-COS_Control_Window| 2D-COS control window]]
: '''''extrema of 2D fcn (function)''''': obtains the minimum and maximum intensities of the current 2D correlation spectrum
: '''''plot x-slice''''': plots a 1D correlation (x) slices at a fixed y-position using the setting from the edit field ''[x,y] slice coordinates'' (y) of the [[Options_of_the_2D-COS_Control_Window| 2D-COS control window]], see screenshot below
: '''''plot y-slice''''': plots a 1D correlation (y) slices at a fixed x-position using the setting from the edit field ''[x,y] slice coordinates'' (x) of the [[Options_of_the_2D-COS_Control_Window| 2D-COS control window]], see screenshot below
: '''''plot [x,y] spectral features''''': creates an [x,y] feature plot using the settings from the edit fields ''[x,y] slice coordinates'' of the [[Options_of_the_2D-COS_Control_Window| 2D-COS control window]], see screenshot below
: '''''invert colormap''''': inverts the colormap
: '''''save 2D function''''': the current color representation of the 2D correlation spectrum can be stored in one of the following image formats: bmp, tif, png, and jpg
: '''''save gui to file''''': the current main user interface, including the 2D correlation function and the x- and y-mean spectra, are allowed to store in one of the following formats: pdf, eps, (vector graphic formats) tif, png, jpg, (image formats) and send to clipboard (no data are stored)
: '''''display 2D-COS intensity (on/off)''''': chose ''on'' to enables the ''MouseOver'' functionality; the option is helpful to obtain the intensity of the 2D correlation spectrum at defined [x,y] positions   

== Context Menu ==

: '''''plot x-slice''''': plots a 1D correlation (x) slices at a fixed y-position using the setting from the edit field ''[x,y] slice coordinates'' (y) of the [[Options_of_the_2D-COS_Control_Window| 2D-COS control window]], see screenshot below
: '''''plot y-slice''''': plots a 1D correlation (y) slices at a fixed x-position using the setting from the edit field ''[x,y] slice coordinates'' (x) of the [[Options_of_the_2D-COS_Control_Window| 2D-COS control window]], see screenshot below
: '''''plot [x,y] spectral features''''': creates an [x,y] feature plot using the settings from the edit fields ''[x,y] slice coordinates'' of the [[Options_of_the_2D-COS_Control_Window| 2D-COS control window]], see screenshot below

Options of the 2D-COS Main Figure

2024-12-22T16:07:32Z

Laschp: /* Context Menu */

[[File:2dcos-gui.jpg|400px|thumb|mat2dcorr: screenshot of the main figure]]
[[File:2dcos-x-trace.jpg|400px|thumb|mat2dcorr: screenshot of a 1D correlation slice produced by mat2dcorr]]
[[File:xy-feature-plot.jpg|400px|thumb|mat2dcorr: screenshot of a [x,y] feature plot window]]

== Menu bar options ==

'''File'''
: '''''save 2D spectrum''''' - stores the 2D spectrum and corresponding metadata, for details see section [[Format_of_a_2D-COS_Result_File|Format of a 2D-COS result file (Matlab)]]
: '''''clear''''' - data are deleted from memory and figures are set back to their initial state
: '''''quit''''' - exit the program

'''Load data'''
: '''''Matlab imaging forma'''''t: see [[Matlab_Imaging_Format|Import data in the imaging format (Matlab)]]
: '''''Matlab trace format''''': see [[Matlab_Trace_Format|Import data in the trace format (Matlab)]]
: '''''Excel data format''''': see [[Excel_Trace_Format|Import data in the MS Excel data format]]

'''Action'''
: '''''plot 2D spectrum''''': computes the 2D correlation spectrum using the actual settings of the [[Options_of_the_2D-COS_Control_Window| 2D-COS control window]]
: '''''extrema of 2D fcn (function)''''': obtains the minimum and maximum intensities of the current 2D correlation spectrum
: '''''plot x-slice''''': plots a 1D correlation (x) slices at a fixed y-position using the setting from the edit field ''[x,y] slice coordinates'' (y) of the [[Options_of_the_2D-COS_Control_Window| 2D-COS control window]], see screenshot below
: '''''plot y-slice''''': plots a 1D correlation (y) slices at a fixed x-position using the setting from the edit field ''[x,y] slice coordinates'' (x) of the [[Options_of_the_2D-COS_Control_Window| 2D-COS control window]], see screenshot below
: '''''plot [x,y] spectral features''''': creates an [x,y] feature plot using the settings from the edit fields ''[x,y] slice coordinates'' of the [[Options_of_the_2D-COS_Control_Window| 2D-COS control window]], see screenshot below
: '''''invert colormap''''': inverts the colormap
: '''''save 2D function''''': the current color representation of the 2D correlation spectrum can be stored in one of the following image formats: bmp, tif, png, and jpg
: '''''save gui to file''''': the current main user interface, including the 2D correlation function and the x- and y-mean spectra are allowed to store in one of the following image formats: pdf, eps, tif, png, jpg, and send to clipboard
: '''''display 2D-COS intensity (on/off)''''': chose ''on'' to enables the ''MouseOver'' functionality; the option is helpful to obtain the intensity of the 2D correlation spectrum at defined [x,y] positions   

== Context Menu ==

: '''''plot x-slice''''': plots a 1D correlation (x) slices at a fixed y-position using the setting from the edit field ''[x,y] slice coordinates'' (y) of the [[Options_of_the_2D-COS_Control_Window| 2D-COS control window]], see screenshot below
: '''''plot y-slice''''': plots a 1D correlation (y) slices at a fixed x-position using the setting from the edit field ''[x,y] slice coordinates'' (x) of the [[Options_of_the_2D-COS_Control_Window| 2D-COS control window]], see screenshot below
: '''''plot [x,y] spectral features''''': creates an [x,y] feature plot using the settings from the edit fields ''[x,y] slice coordinates'' of the [[Options_of_the_2D-COS_Control_Window| 2D-COS control window]], see screenshot below

Options of the 2D-COS Main Figure

2024-12-22T16:07:03Z

Laschp: /* Menu bar options */

[[File:2dcos-gui.jpg|400px|thumb|mat2dcorr: screenshot of the main figure]]
[[File:2dcos-x-trace.jpg|400px|thumb|mat2dcorr: screenshot of a 1D correlation slice produced by mat2dcorr]]
[[File:xy-feature-plot.jpg|400px|thumb|mat2dcorr: screenshot of a [x,y] feature plot window]]

== Menu bar options ==

'''File'''
: '''''save 2D spectrum''''' - stores the 2D spectrum and corresponding metadata, for details see section [[Format_of_a_2D-COS_Result_File|Format of a 2D-COS result file (Matlab)]]
: '''''clear''''' - data are deleted from memory and figures are set back to their initial state
: '''''quit''''' - exit the program

'''Load data'''
: '''''Matlab imaging forma'''''t: see [[Matlab_Imaging_Format|Import data in the imaging format (Matlab)]]
: '''''Matlab trace format''''': see [[Matlab_Trace_Format|Import data in the trace format (Matlab)]]
: '''''Excel data format''''': see [[Excel_Trace_Format|Import data in the MS Excel data format]]

'''Action'''
: '''''plot 2D spectrum''''': computes the 2D correlation spectrum using the actual settings of the [[Options_of_the_2D-COS_Control_Window| 2D-COS control window]]
: '''''extrema of 2D fcn (function)''''': obtains the minimum and maximum intensities of the current 2D correlation spectrum
: '''''plot x-slice''''': plots a 1D correlation (x) slices at a fixed y-position using the setting from the edit field ''[x,y] slice coordinates'' (y) of the [[Options_of_the_2D-COS_Control_Window| 2D-COS control window]], see screenshot below
: '''''plot y-slice''''': plots a 1D correlation (y) slices at a fixed x-position using the setting from the edit field ''[x,y] slice coordinates'' (x) of the [[Options_of_the_2D-COS_Control_Window| 2D-COS control window]], see screenshot below
: '''''plot [x,y] spectral features''''': creates an [x,y] feature plot using the settings from the edit fields ''[x,y] slice coordinates'' of the [[Options_of_the_2D-COS_Control_Window| 2D-COS control window]], see screenshot below
: '''''invert colormap''''': inverts the colormap
: '''''save 2D function''''': the current color representation of the 2D correlation spectrum can be stored in one of the following image formats: bmp, tif, png, and jpg
: '''''save gui to file''''': the current main user interface, including the 2D correlation function and the x- and y-mean spectra are allowed to store in one of the following image formats: pdf, eps, tif, png, jpg, and send to clipboard
: '''''display 2D-COS intensity (on/off)''''': chose ''on'' to enables the ''MouseOver'' functionality; the option is helpful to obtain the intensity of the 2D correlation spectrum at defined [x,y] positions   

== Context Menu ==

: '''plot x-slice''': plots a 1D correlation (x) slices at a fixed y-position using the setting from the edit field ''[x,y] slice coordinates'' (y) of the [[Options_of_the_2D-COS_Control_Window| 2D-COS control window]], see screenshot below
: '''plot y-slice''': plots a 1D correlation (y) slices at a fixed x-position using the setting from the edit field ''[x,y] slice coordinates'' (x) of the [[Options_of_the_2D-COS_Control_Window| 2D-COS control window]], see screenshot below
: '''plot [x,y] spectral features''': creates an [x,y] feature plot using the settings from the edit fields ''[x,y] slice coordinates'' of the [[Options_of_the_2D-COS_Control_Window| 2D-COS control window]], see screenshot below

Options of the 2D-COS Main Figure

2024-12-22T16:06:42Z

Laschp: /* Menu bar options */

[[File:2dcos-gui.jpg|400px|thumb|mat2dcorr: screenshot of the main figure]]
[[File:2dcos-x-trace.jpg|400px|thumb|mat2dcorr: screenshot of a 1D correlation slice produced by mat2dcorr]]
[[File:xy-feature-plot.jpg|400px|thumb|mat2dcorr: screenshot of a [x,y] feature plot window]]

== Menu bar options ==

'''File'''
: '''''save 2D spectrum''''' - stores the 2D spectrum and corresponding metadata, for details see section [[Format_of_a_2D-COS_Result_File|Format of a 2D-COS result file (Matlab)]]
: '''''clear''''' - data are deleted from memory and figures are set back to their initial state
: '''''quit''''' - exit the program

'''Load data'''
: '''''Matlab imaging forma'''''t: see [[Matlab_Imaging_Format|Import data in the imaging format (Matlab)]]
: '''''Matlab trace format''''': see [[Matlab_Trace_Format|Import data in the trace format (Matlab)]]
: '''''Excel data format''''': see [[Excel_Trace_Format|Import data in the MS Excel data format]]

'''Action'''
: '''''plot 2D spectrum''''': computes the 2D correlation spectrum using the actual settings of the [[Options_of_the_2D-COS_Control_Window| 2D-COS control window]]
: '''''extrema of 2D fcn (function)''': obtains the minimum and maximum intensities of the current 2D correlation spectrum
: '''''plot x-slice''''': plots a 1D correlation (x) slices at a fixed y-position using the setting from the edit field ''[x,y] slice coordinates'' (y) of the [[Options_of_the_2D-COS_Control_Window| 2D-COS control window]], see screenshot below
: '''''plot y-slice''''': plots a 1D correlation (y) slices at a fixed x-position using the setting from the edit field ''[x,y] slice coordinates'' (x) of the [[Options_of_the_2D-COS_Control_Window| 2D-COS control window]], see screenshot below
: '''''plot [x,y] spectral features''''': creates an [x,y] feature plot using the settings from the edit fields ''[x,y] slice coordinates'' of the [[Options_of_the_2D-COS_Control_Window| 2D-COS control window]], see screenshot below
: '''''invert colormap''''': inverts the colormap
: '''''save 2D function''''': the current color representation of the 2D correlation spectrum can be stored in one of the following image formats: bmp, tif, png, and jpg
: '''''save gui to file''''': the current main user interface, including the 2D correlation function and the x- and y-mean spectra are allowed to store in one of the following image formats: pdf, eps, tif, png, jpg, and send to clipboard
: '''''display 2D-COS intensity (on/off)''''': chose ''on'' to enables the ''MouseOver'' functionality; the option is helpful to obtain the intensity of the 2D correlation spectrum at defined [x,y] positions   

== Context Menu ==

: '''plot x-slice''': plots a 1D correlation (x) slices at a fixed y-position using the setting from the edit field ''[x,y] slice coordinates'' (y) of the [[Options_of_the_2D-COS_Control_Window| 2D-COS control window]], see screenshot below
: '''plot y-slice''': plots a 1D correlation (y) slices at a fixed x-position using the setting from the edit field ''[x,y] slice coordinates'' (x) of the [[Options_of_the_2D-COS_Control_Window| 2D-COS control window]], see screenshot below
: '''plot [x,y] spectral features''': creates an [x,y] feature plot using the settings from the edit fields ''[x,y] slice coordinates'' of the [[Options_of_the_2D-COS_Control_Window| 2D-COS control window]], see screenshot below

Options of the 2D-COS Main Figure

2024-12-22T16:04:48Z

Laschp: /* Menu bar options */

[[File:2dcos-gui.jpg|400px|thumb|mat2dcorr: screenshot of the main figure]]
[[File:2dcos-x-trace.jpg|400px|thumb|mat2dcorr: screenshot of a 1D correlation slice produced by mat2dcorr]]
[[File:xy-feature-plot.jpg|400px|thumb|mat2dcorr: screenshot of a [x,y] feature plot window]]

== Menu bar options ==

File
: '''save 2D spectrum''' - stores the 2D spectrum and corresponding metadata, for details see section [[Format_of_a_2D-COS_Result_File|Format of a 2D-COS result file (Matlab)]]
: '''clear''' - data are deleted from memory and figures are set back to their initial state
: '''quit''' - exit the program

Load data
: '''Matlab imaging forma'''t: see [[Matlab_Imaging_Format|Import data in the imaging format (Matlab)]]
: '''Matlab trace format''': see [[Matlab_Trace_Format|Import data in the trace format (Matlab)]]
: '''Excel data format''': see [[Excel_Trace_Format|Import data in the MS Excel data format]]

Action
: '''plot 2D spectrum''': computes the 2D correlation spectrum using the actual settings of the [[Options_of_the_2D-COS_Control_Window| 2D-COS control window]]
: '''extrema of 2D fcn (function)''': obtains the minimum and maximum intensities of the current 2D correlation spectrum
: '''plot x-slice''': plots a 1D correlation (x) slices at a fixed y-position using the setting from the edit field ''[x,y] slice coordinates'' (y) of the [[Options_of_the_2D-COS_Control_Window| 2D-COS control window]], see screenshot below
: '''plot y-slice''': plots a 1D correlation (y) slices at a fixed x-position using the setting from the edit field ''[x,y] slice coordinates'' (x) of the [[Options_of_the_2D-COS_Control_Window| 2D-COS control window]], see screenshot below
: '''plot [x,y] spectral features''': creates an [x,y] feature plot using the settings from the edit fields ''[x,y] slice coordinates'' of the [[Options_of_the_2D-COS_Control_Window| 2D-COS control window]], see screenshot below
: '''invert colormap''': inverts the colormap
: '''save 2D function''': the current color representation of the 2D correlation spectrum can be stored in one of the following image formats: bmp, tif, png, and jpg
: '''save gui to file''': the current main user interface, including the 2D correlation function and the x- and y-mean spectra are allowed to store in one of the following image formats: pdf, eps, tif, png, jpg, and send to clipboard
: '''display 2D-COS intensity (on/off)''': chose ''on'' to enables the ''MouseOver'' functionality; the option is helpful to obtain the intensity of the 2D correlation spectrum at defined [x,y] positions   

== Context Menu ==

: '''plot x-slice''': plots a 1D correlation (x) slices at a fixed y-position using the setting from the edit field ''[x,y] slice coordinates'' (y) of the [[Options_of_the_2D-COS_Control_Window| 2D-COS control window]], see screenshot below
: '''plot y-slice''': plots a 1D correlation (y) slices at a fixed x-position using the setting from the edit field ''[x,y] slice coordinates'' (x) of the [[Options_of_the_2D-COS_Control_Window| 2D-COS control window]], see screenshot below
: '''plot [x,y] spectral features''': creates an [x,y] feature plot using the settings from the edit fields ''[x,y] slice coordinates'' of the [[Options_of_the_2D-COS_Control_Window| 2D-COS control window]], see screenshot below

Options of the 2D-COS Main Figure

2024-12-22T15:58:48Z

Laschp:

[[File:2dcos-gui.jpg|400px|thumb|mat2dcorr: screenshot of the main figure]]
[[File:2dcos-x-trace.jpg|400px|thumb|mat2dcorr: screenshot of a 1D correlation slice produced by mat2dcorr]]
[[File:xy-feature-plot.jpg|400px|thumb|mat2dcorr: screenshot of a [x,y] feature plot window]]

== Menu bar options ==

File
: '''save 2D spectrum''' - stores the 2D spectrum and corresponding metadata, for details see section [[Format_of_a_2D-COS_Result_File|Format of a 2D-COS result file (Matlab)]]
: '''clear''' - data are deleted from memory and figures are set back to their initial state
: '''quit''' - exit the program

Load data
: '''Matlab imaging forma'''t: see [[Matlab_Imaging_Format|Import data in the imaging format (Matlab)]]
: '''Matlab trace format''': see [[Matlab_Trace_Format|Import data in the trace format (Matlab)]]
: '''Excel data format''': see [[Excel_Trace_Format|Import data in the MS Excel data format]]

Action
: '''plot 2D spectrum''': computes the 2D correlation spectrum using the actual settings of the [[Options_of_the_2D-COS_Control_Window| 2D-COS control window]]
: '''extrema of 2D fcn (function)''': obtains the minimum and maximum intensities of the current 2D correlation spectrum
: '''plot x-slice''': plots a 1D correlation (x) slices at a fixed y-position using the setting from the edit field ''[x,y] slice coordinates'' (y) of the [[Options_of_the_2D-COS_Control_Window| 2D-COS control window]], see screenshot below
: '''plot y-slice''': plots a 1D correlation (y) slices at a fixed x-position using the setting from the edit field ''[x,y] slice coordinates'' (x) of the [[Options_of_the_2D-COS_Control_Window| 2D-COS control window]], see screenshot below
: '''plot [x,y] spectral features''': creates an [x,y] feature plot using the settings from the edit fields ''[x,y] slice coordinates'' of the [[Options_of_the_2D-COS_Control_Window| 2D-COS control window]], see screenshot below
: '''invert colormap''': inverts the colormap
: '''capture to bitmap''': the current color representation of the 2D correlation spectrum is stored in a standard bitmap format
: '''display 2D-COS intensity (on/off)''': chose ''on'' to enables the ''MouseOver'' functionality; the option is helpful to obtain the intensity of the 2D correlation spectrum at defined [x,y] positions   

== Context Menu ==

: '''plot x-slice''': plots a 1D correlation (x) slices at a fixed y-position using the setting from the edit field ''[x,y] slice coordinates'' (y) of the [[Options_of_the_2D-COS_Control_Window| 2D-COS control window]], see screenshot below
: '''plot y-slice''': plots a 1D correlation (y) slices at a fixed x-position using the setting from the edit field ''[x,y] slice coordinates'' (x) of the [[Options_of_the_2D-COS_Control_Window| 2D-COS control window]], see screenshot below
: '''plot [x,y] spectral features''': creates an [x,y] feature plot using the settings from the edit fields ''[x,y] slice coordinates'' of the [[Options_of_the_2D-COS_Control_Window| 2D-COS control window]], see screenshot below

Mat2dcorr - A Matlab Toolbox for 2D-COS

2024-12-22T15:57:16Z

Laschp: /* Related Publications and Web Links */

__FORCETOC__
mat2dcorr - a free toolbox for performing two-dimensional correlation (2D-COS) analysis with [https://www.mathworks.com Matlab]. 

== Introduction ==

[[File:GraphAbstr.gif|500px|thumb|mat2dcorr: Illustration of heterospectral 2D-COS (FTIR vs. Raman)]]

Since its introduction in 1989, two-dimensional correlation spectroscopy (2D-COS), sometimes referred to as two-dimensional correlation analysis, has become an invaluable analytical tool for spectroscopic characterization in various fields of application, including protein science, pharmacy, biomedical applications, and polymer or nanomaterial research. The technique of 2D-COS analysis was originally developed to study dynamic processes in which a spectroscopic probe is used to monitor the effect of an external perturbation on a given system. The system responds to the perturbation with characteristic changes that are monitored spectroscopically, e.g. by continuous acquisition of a spectral time series. The main purpose of 2D-COS investigations is to explore correlations that may exist between perturbation-induced spectral responses. In some situations, the 2D-COS analysis technique is useful not only to detect and visualize such correlations, but also to decipher the sequence of these spectral changes. Sources of perturbation may include changes in temperature, pH, pressure, concentration, or spatial coordinates when examining spatially heterogeneous samples. The 2D correlation concept has proven to be extremely useful in a wide variety of molecular spectroscopy applications based on IR, NIR, Raman, NMR, fluorescence and UV-visible spectroscopy.

An introduction into the history, mathematics and basic principles of 2D-COS can be found here: https://en.wikipedia.org/wiki/Two-dimensional_correlation_analysis

== Getting Started ==
* [[Computer_Specification|Specification of computer configuration]]
* [[Screenshot_of_mat2dcorr|Screenshot of the mat2dcorr gui]]
* Downloading mat2dcorr - [[How_to_obtain_the_mat2dcorr_toolbox|How to obtain the mat2dcorr toolbox?]]
* [[Installation_of_the_mat2dcorr_toolbox|Installation of the mat2dcorr toolbox]]
* [[Disclaimer_and_License_Conditions |Disclaimer and license conditions]]
* [[mat2dcorr_-_Relevant Publications|Acknowledgement, relevant publications]]
* [[Frequently_Asked_Questions_(FAQ)|Frequently asked questions (FAQ)]]

== Data Import & File Formats ==

* [[Matlab_Imaging_Format|Import data in the CytoSpec imaging format (CytoSpec/Matlab)]]
* [[Matlab_Trace_Format|Import data in the trace format (Matlab)]]
* [[Excel_Trace_Format|Import data in the MS Excel data format]]
* [[Format_of_a_2D-COS_Result_File|Format of a 2D-COS result file (Matlab)]]

== Options of the mat2dcorr Toolbox ==

* [[Options_of_the_2D-COS_Main_Figure| Options of the 2D-COS main figure]]
* [[Options_of_the_2D-COS_Control_Window| Options of the 2D-COS control window]]

== Related Publications and Web Links ==

[[File:2D-COS.jpg|650px|thumb|Mat2dcorr: Screenshot of the 2D control window (left) and the window ''2D correlation
analysis ...'' (right)]]

* [https://en.wikipedia.org/wiki/Two-dimensional_correlation_analysis Two-dimensional correlation analysis] (Wikipedia)

* I. Noda. [https://doi.org/10.1366/0003702904087398 Two-Dimensional Infrared (2D IR) Spectroscopy: Theory and Applications], '''1990''' ''Appl. Spectrosc''. 44(4): 550-561

* I. Noda. [https://doi.org/10.1366/0003702934067694 Generalized Two- Dimensional Correlation Method Applicable to Infrared, Raman, and other Types of Spectroscopy], '''1993''' ''Appl. Spectrosc''. 47(9): 1329-1336

* I. Noda. [https://doi.org/10.1366/0003702001950472 Determination of Two-Dimensional Correlation Spectra Using the Hilbert Transform], '''2000''' ''Appl. Spectrosc''. 54(7): 994-999

* P. Lasch & I. Noda. [https://doi.org/10.1021/acs.analchem.7b00332 Two-Dimensional Correlation Spectroscopy for Multimodal Analysis of FT-IR, Raman, and MALDI-TOF MS Hyperspectral Images with Hamster Brain Tissue]. '''2017''' ''Anal. Chem''., 89, 9, 5008–5016

* P. Lasch & I. Noda. [https://doi.org/10.1177/0003702818819880 Two-Dimensional Correlation Spectroscopy (2D-COS) for Analysis of Spatially Resolved Vibrational Spectra]. '''2019''' ''Appl. Spectrosc''. 73(4): 359-379

   

Dec 22, 2024: more details of the mat2dcorr Matlab toolbox will follow

Mat2dcorr - A Matlab Toolbox for 2D-COS

2024-12-22T15:56:53Z

Laschp: /* Introduction */

__FORCETOC__
mat2dcorr - a free toolbox for performing two-dimensional correlation (2D-COS) analysis with [https://www.mathworks.com Matlab]. 

== Introduction ==

[[File:GraphAbstr.gif|500px|thumb|mat2dcorr: Illustration of heterospectral 2D-COS (FTIR vs. Raman)]]

Since its introduction in 1989, two-dimensional correlation spectroscopy (2D-COS), sometimes referred to as two-dimensional correlation analysis, has become an invaluable analytical tool for spectroscopic characterization in various fields of application, including protein science, pharmacy, biomedical applications, and polymer or nanomaterial research. The technique of 2D-COS analysis was originally developed to study dynamic processes in which a spectroscopic probe is used to monitor the effect of an external perturbation on a given system. The system responds to the perturbation with characteristic changes that are monitored spectroscopically, e.g. by continuous acquisition of a spectral time series. The main purpose of 2D-COS investigations is to explore correlations that may exist between perturbation-induced spectral responses. In some situations, the 2D-COS analysis technique is useful not only to detect and visualize such correlations, but also to decipher the sequence of these spectral changes. Sources of perturbation may include changes in temperature, pH, pressure, concentration, or spatial coordinates when examining spatially heterogeneous samples. The 2D correlation concept has proven to be extremely useful in a wide variety of molecular spectroscopy applications based on IR, NIR, Raman, NMR, fluorescence and UV-visible spectroscopy.

An introduction into the history, mathematics and basic principles of 2D-COS can be found here: https://en.wikipedia.org/wiki/Two-dimensional_correlation_analysis

== Getting Started ==
* [[Computer_Specification|Specification of computer configuration]]
* [[Screenshot_of_mat2dcorr|Screenshot of the mat2dcorr gui]]
* Downloading mat2dcorr - [[How_to_obtain_the_mat2dcorr_toolbox|How to obtain the mat2dcorr toolbox?]]
* [[Installation_of_the_mat2dcorr_toolbox|Installation of the mat2dcorr toolbox]]
* [[Disclaimer_and_License_Conditions |Disclaimer and license conditions]]
* [[mat2dcorr_-_Relevant Publications|Acknowledgement, relevant publications]]
* [[Frequently_Asked_Questions_(FAQ)|Frequently asked questions (FAQ)]]

== Data Import & File Formats ==

* [[Matlab_Imaging_Format|Import data in the CytoSpec imaging format (CytoSpec/Matlab)]]
* [[Matlab_Trace_Format|Import data in the trace format (Matlab)]]
* [[Excel_Trace_Format|Import data in the MS Excel data format]]
* [[Format_of_a_2D-COS_Result_File|Format of a 2D-COS result file (Matlab)]]

== Options of the mat2dcorr Toolbox ==

* [[Options_of_the_2D-COS_Main_Figure| Options of the 2D-COS main figure]]
* [[Options_of_the_2D-COS_Control_Window| Options of the 2D-COS control window]]

== Related Publications and Web Links ==

[[File:2D-COS.jpg|650px|thumb|Mat2dcorr: Screenshot of the 2D control window (left) and the window ''2D correlation
analysis ...'' (right)]]

* [https://en.wikipedia.org/wiki/Two-dimensional_correlation_analysis Two-dimensional correlation analysis] (Wikipedia)

* I. Noda. [https://doi.org/10.1366/0003702904087398 Two-Dimensional Infrared (2D IR) Spectroscopy: Theory and Applications], '''1990''' ''Appl. Spectrosc''. 44(4): 550-561

* I. Noda. [https://doi.org/10.1366/0003702934067694 Generalized Two- Dimensional Correlation Method Applicable to Infrared, Raman, and other Types of Spectroscopy], '''1993''' ''Appl. Spectrosc''. 47(9): 1329-1336

* I. Noda. [https://doi.org/10.1366/0003702001950472 Determination of Two-Dimensional Correlation Spectra Using the Hilbert Transform], '''2000''' ''Appl. Spectrosc''. 54(7): 994-999

* P. Lasch & I. Noda. [https://doi.org/10.1021/acs.analchem.7b00332 Two-Dimensional Correlation Spectroscopy for Multimodal Analysis of FT-IR, Raman, and MALDI-TOF MS Hyperspectral Images with Hamster Brain Tissue]. '''2017''' ''Anal. Chem''., 89, 9, 5008–5016

* P. Lasch & I. Noda. [https://doi.org/10.1177/0003702818819880 Two-Dimensional Correlation Spectroscopy (2D-COS) for Analysis of Spatially Resolved Vibrational Spectra]. '''2019''' ''Appl. Spectrosc''. 73(4): 359-379

   

Oct 02, 2023: more details of the mat2dcorr Matlab toolbox will follow soon

Installation of the mat2dcorr toolbox

2024-12-22T15:52:49Z

Laschp: /* Installation */

__FORCETOC__
To download go to [http://www.peter-lasch.de/2dcorr/ http://www.peter-lasch.de/2dcorr/]

== Required files / Software / OS ==

[[File:index-of-2dcorr.jpg|500px|thumb|mat2Dcorr: index of http://www.peter-lasch.de/2dcorr]]

'''Program Files''' of the mat2Dcorr toolbox:
<ol>
<li> mat2dcorr.m</li>
<li> updcorrplt.m</li>
<li> resiz2dfig.m</li>
<li> readme.txt</li>
</ol>

'''Software''':
<ul>
<li> [https://www.mathworks.com Matlab] R2014a or newer</li>
</ul>

'''Operating Systems''':
<ul>
<li> Windows 10</li>
<li> LINUX (Debian Buster/Bullseye/Bookworm)</li>
</ul>

== Extra files (spectral data files) ==

<ol start="5">
<li> linescandata.mat - example data file in the trace (2D) data format, see [[Matlab_Trace_Format| Matlab trace data format]] </li>
<li> linescandata.xlsx - example data file in the MS Excel format, see [[Excel_Trace_Format| MS Excel data format]]</li>
<li> IR-cerebellum-100x100.mat - example data file in the hyperspectral imaging (HSI) data format (FT-IR spectra from hamster cerebellum), see [[Matlab_Imaging_Format| Matlab imaging data format ]]</li>
<li> IR-cerebellum-50x50.mat - example data (50 x 50 spectra, interpolated), [[Matlab_Imaging_Format| Matlab imaging data format ]]</li>
<li> MALDI-cerebellum-100x100.mat - example data file (100 x 100 spectra) in the HSI data format (MALDI-TOF mass spectra from hamster cerebellum), [[Matlab_Imaging_Format| Matlab imaging data format ]]</li>
<li> MALDI-cerebellum-50x50.mat - example data file (50 x 50 spectra, interpolated), [[Matlab_Imaging_Format| Matlab imaging data format ]]</li>
<li> Raman-cerebellum-100x100.mat - example data file (100 x 100 spectra) in the HSI data format (MALDI-TOF mass spectra from hamster cerebellum), [[Matlab_Imaging_Format| Matlab imaging data format ]]</li>
<li> Raman-cerebellum-50x50.mat - example data file (50 x 50 spectra, interpolated), [[Matlab_Imaging_Format| Matlab imaging data format ]]</li>
<li> fftsyncro.mat - result file of 2D correlation analysis by the mat2dcorr toolbox using the FFT 2D-COS option, for details of the result file format refer also to the section [[Format_of_a_2D-COS_Result_File|format of a 2D-COS result file (Matlab)]]</li>
<li> statssyncro.mat - result file of 2D correlation analysis by the mat2dcorr toolbox using the statistical 2D-COS option, for details of the result file format refer also to the section [[Format_of_a_2D-COS_Result_File|format of a 2D-COS result file (Matlab)]]</li>
</ol>

== Installation ==

* Download files 1.-4. + some test data (see above) and and copy them into one single directory, for example into a directory ''mat2dcorr''.
* Add then this directory to Matlab's search path. For this start Matlab and type 

>> addpath('Mat2DcorrPath')

at the Matlab command prompt, where ''Mat2DcorrPath'' denotes the full path where the the toolbox resides (i.e. ''mat2dcos''). 
Example of Mat2DcorrPath: ''C:\Users\Tim\Documents\Matlab\mat2dcorr''

* To start mat2Dcorr type 

>> mat2dcorr

at the Matlab prompt. 

This will open two different windows:
# A window entitled '2D correlation analysis ... ' which shows the 2D correlation spectrum and mean spectra obtained from the x- and y-spectral data
# The 2D-COS control window, the user dialog box, see screenshot below

[[File:2D-COS.jpg|800px|thumb|left|Mat2Dcorr: Screenshot of the 2D control window (left) and the window '2D correlation
analysis ... ' (right)]]

Installation of the mat2dcorr toolbox

2024-12-22T15:52:11Z

Laschp: /* Installation */

__FORCETOC__
To download go to [http://www.peter-lasch.de/2dcorr/ http://www.peter-lasch.de/2dcorr/]

== Required files / Software / OS ==

[[File:index-of-2dcorr.jpg|500px|thumb|mat2Dcorr: index of http://www.peter-lasch.de/2dcorr]]

'''Program Files''' of the mat2Dcorr toolbox:
<ol>
<li> mat2dcorr.m</li>
<li> updcorrplt.m</li>
<li> resiz2dfig.m</li>
<li> readme.txt</li>
</ol>

'''Software''':
<ul>
<li> [https://www.mathworks.com Matlab] R2014a or newer</li>
</ul>

'''Operating Systems''':
<ul>
<li> Windows 10</li>
<li> LINUX (Debian Buster/Bullseye/Bookworm)</li>
</ul>

== Extra files (spectral data files) ==

<ol start="5">
<li> linescandata.mat - example data file in the trace (2D) data format, see [[Matlab_Trace_Format| Matlab trace data format]] </li>
<li> linescandata.xlsx - example data file in the MS Excel format, see [[Excel_Trace_Format| MS Excel data format]]</li>
<li> IR-cerebellum-100x100.mat - example data file in the hyperspectral imaging (HSI) data format (FT-IR spectra from hamster cerebellum), see [[Matlab_Imaging_Format| Matlab imaging data format ]]</li>
<li> IR-cerebellum-50x50.mat - example data (50 x 50 spectra, interpolated), [[Matlab_Imaging_Format| Matlab imaging data format ]]</li>
<li> MALDI-cerebellum-100x100.mat - example data file (100 x 100 spectra) in the HSI data format (MALDI-TOF mass spectra from hamster cerebellum), [[Matlab_Imaging_Format| Matlab imaging data format ]]</li>
<li> MALDI-cerebellum-50x50.mat - example data file (50 x 50 spectra, interpolated), [[Matlab_Imaging_Format| Matlab imaging data format ]]</li>
<li> Raman-cerebellum-100x100.mat - example data file (100 x 100 spectra) in the HSI data format (MALDI-TOF mass spectra from hamster cerebellum), [[Matlab_Imaging_Format| Matlab imaging data format ]]</li>
<li> Raman-cerebellum-50x50.mat - example data file (50 x 50 spectra, interpolated), [[Matlab_Imaging_Format| Matlab imaging data format ]]</li>
<li> fftsyncro.mat - result file of 2D correlation analysis by the mat2dcorr toolbox using the FFT 2D-COS option, for details of the result file format refer also to the section [[Format_of_a_2D-COS_Result_File|format of a 2D-COS result file (Matlab)]]</li>
<li> statssyncro.mat - result file of 2D correlation analysis by the mat2dcorr toolbox using the statistical 2D-COS option, for details of the result file format refer also to the section [[Format_of_a_2D-COS_Result_File|format of a 2D-COS result file (Matlab)]]</li>
</ol>

== Installation ==

* Download files 1.-4. + some test data (see above) and and copy them into one single directory, for example into a directory ''mat2dcorr''.
* Add then this directory to Matlab's search path. For this start Matlab and type 

>> addpath('Mat2DcorrPath')

at the Matlab command prompt, where ''Mat2DcorrPath'' denotes the full path where the the toolbox resides (i.e. ''mat2dcos''). 
Example of Mat2DcorrPath: ''C:\Users\Tim\Documents\Matlab\mat2dcorr''

* To start mat2Dcorr type 

>> mat2dcorr

at the Matlab prompt. 

This will open two different windows:
# A window entitled '2D correlation analysis ... ' which shows the 2D correlation spectrum and mean spectra obtained from the x- and y-spectral data
# The 2D-COS control window, the user dialog box, see screenshot below

[[File:2D-COS.jpg|800px|thumb|Mat2Dcorr: Screenshot of the 2D control window (left) and the window '2D correlation
analysis ... ' (right)]]

Installation of the mat2dcorr toolbox

2024-12-22T15:51:31Z

Laschp: /* Required files / Software / OS */

__FORCETOC__
To download go to [http://www.peter-lasch.de/2dcorr/ http://www.peter-lasch.de/2dcorr/]

== Required files / Software / OS ==

[[File:index-of-2dcorr.jpg|500px|thumb|mat2Dcorr: index of http://www.peter-lasch.de/2dcorr]]

'''Program Files''' of the mat2Dcorr toolbox:
<ol>
<li> mat2dcorr.m</li>
<li> updcorrplt.m</li>
<li> resiz2dfig.m</li>
<li> readme.txt</li>
</ol>

'''Software''':
<ul>
<li> [https://www.mathworks.com Matlab] R2014a or newer</li>
</ul>

'''Operating Systems''':
<ul>
<li> Windows 10</li>
<li> LINUX (Debian Buster/Bullseye/Bookworm)</li>
</ul>

== Extra files (spectral data files) ==

<ol start="5">
<li> linescandata.mat - example data file in the trace (2D) data format, see [[Matlab_Trace_Format| Matlab trace data format]] </li>
<li> linescandata.xlsx - example data file in the MS Excel format, see [[Excel_Trace_Format| MS Excel data format]]</li>
<li> IR-cerebellum-100x100.mat - example data file in the hyperspectral imaging (HSI) data format (FT-IR spectra from hamster cerebellum), see [[Matlab_Imaging_Format| Matlab imaging data format ]]</li>
<li> IR-cerebellum-50x50.mat - example data (50 x 50 spectra, interpolated), [[Matlab_Imaging_Format| Matlab imaging data format ]]</li>
<li> MALDI-cerebellum-100x100.mat - example data file (100 x 100 spectra) in the HSI data format (MALDI-TOF mass spectra from hamster cerebellum), [[Matlab_Imaging_Format| Matlab imaging data format ]]</li>
<li> MALDI-cerebellum-50x50.mat - example data file (50 x 50 spectra, interpolated), [[Matlab_Imaging_Format| Matlab imaging data format ]]</li>
<li> Raman-cerebellum-100x100.mat - example data file (100 x 100 spectra) in the HSI data format (MALDI-TOF mass spectra from hamster cerebellum), [[Matlab_Imaging_Format| Matlab imaging data format ]]</li>
<li> Raman-cerebellum-50x50.mat - example data file (50 x 50 spectra, interpolated), [[Matlab_Imaging_Format| Matlab imaging data format ]]</li>
<li> fftsyncro.mat - result file of 2D correlation analysis by the mat2dcorr toolbox using the FFT 2D-COS option, for details of the result file format refer also to the section [[Format_of_a_2D-COS_Result_File|format of a 2D-COS result file (Matlab)]]</li>
<li> statssyncro.mat - result file of 2D correlation analysis by the mat2dcorr toolbox using the statistical 2D-COS option, for details of the result file format refer also to the section [[Format_of_a_2D-COS_Result_File|format of a 2D-COS result file (Matlab)]]</li>
</ol>

== Installation ==

* Download files 1.-4. + some test data (see above) and and copy them into one single directory, for example into a directory ''mat2dcos''.
* Add then this directory to Matlab's search path. For this start Matlab and type 

>> addpath('Mat2DcorrPath')

at the Matlab command prompt, where ''Mat2DcorrPath'' denotes the full path where the the toolbox resides (i.e. ''mat2dcos''). 
Example of Mat2DcorrPath: ''C:\Users\Tim\Documents\Matlab\mat2dcos''

* To start mat2Dcorr type 

>> mat2dcorr

at the Matlab prompt. 

This will open two different windows:
# A window entitled '2D correlation analysis ... ' which shows the 2D correlation spectrum and mean spectra obtained from the x- and y-spectral data
# The 2D-COS control window, the user dialog box, see screenshot below

[[File:2D-COS.jpg|800px|thumb|Mat2Dcorr: screenshot of the 2D control window (left) and the window '2D correlation
analysis ... ' (right)]]

How to obtain the mat2dcorr toolbox

2024-12-22T15:49:19Z

Laschp:

[[File:index-of-2dcorr.jpg|500px|thumb|mat2Dcorr: index of http://www.peter-lasch.de/2dcorr]]

'''Download''' - the latest version of the mat2dcorr toolbox (CC BY-NC-SA 4.0 license) is available from this website: 

[http://www.peter-lasch.de/2dcorr/ http://www.peter-lasch.de/2dcorr/] 

===== mat2dcorr Toolbox Versions =====

<ol>
<li>version 1.00 - Jan 2020</li>
<li>version 1.01 - May 2021 - minor bugfixes:
<ul>
<li>button for starting the calculation greys out after changing pulldown menu selections (type of 2D spectrum, type of reference spectrum, etc.)</li>
<li>bug of the ''Mouse over'' function of the main window fixed</li>
<li>standard size of the main window changed</li>
</ul>
</li>
<li>version 1.02 - May 2021:
<ul>
<li>fixed a critical error reported by Z. Xinchang - wrong treatment of spectra with non-equidistant point spacing (function import data in the Matlab trace format)</li>
</ul>
</li>
<li>version 1.03 - Aug 2021:
<ul>
<li>allows resizing the 2D-COS window (main window), minor bug fixes</li>
</ul>
</li>
<li>version 1.04 - Apr 2023:
<ul>
<li>import data from MS Excel files (*.xlsx)</li>
<li>minor bug fixes</li>
</ul>
</li>
<li>version 1.05 - Dec 2024:
<ul>
<li>code modifications to speed up graphics</li>
<li>new functionality to store 2DCOS function and gui (bmp, jpg, tif, png, eps, pdf, requires Matlab 2020 and later)</li>
<li>minor bug fixes</li>
</ul>
</li>
</ol>