Matlab Trace Format and Mat2dcorr - A Matlab Toolbox for 2D-COS: Difference between pages

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Matlab trace format files contain spectra series in a 2D data format where the first dimension is the spectral dimension and the second dimension represents the perturbing variable, i.e. time, pressure, temperature, spatial dimension, etc. <br>
__FORCETOC__
mat2dcorr - a free toolbox for performing two-dimensionsonal correlation (2D-COS) analysis with [https://www.mathworks.com Matlab].<br>


To load Matlab trace files select ''Load Data'' &rarr; ''Matlab trace format'' &rarr; ''x-data'', or ''y-data'' from the ''Load Data'' menu bar.
== Introduction ==
<br> &nbsp; <br>
 
[[File:2D-COS.jpg|450px|thumb|Mat2Dcorr: Screenshot of the 2D control window (left) and the window '2D correlation
analysis ... ' (right)]]


== Format of a 2D-COS trace file ==
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, pharmaceutics, biomedical applications, and polymer or nanomaterial research, since its early introduction in 1989. 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 by 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 can 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.


[[File:trace-file-format.jpg|400px|thumb|Variables present in a Matlab trace file]]
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


Matlab trace files contain five different variables to store the spectral data as well as relevant metadata:<br>
== Getting Started ==
* ''spc'' – this variable contains the spectral data, a 2D array of double precision floating point values (float32). Columns indicate individual spectra of absorbance, intensity, transmittance, etc. values. The column length indicates the number of data points per spectrum. The number of columns is equal to the number of observations i.e. the number of spectra.
* [[Computer_Specification|Specification of computer configuration]]
* ''tos'' - A character vector of variable length which indicates the type of spectra. Examples are ''Transmission'', ''Fluorescence'', ''Raman'', etc.
* [[Screenshot_of_mat2dcorr|Screenshot of the mat2dcorr gui]]
* ''vst'' - A character vector of variable length indicating the type of the perturbing variable. Examples of vst: ''Temperature'', ''Time'', ''Voltage'', ''Pressure'', or ''Spatial variable''.
* <span style="color:red"> Downloading mat2dcorr</span> - [[How_to_obtain_the_mat2dcorr_toolbox|How to obtain the mat2dcorr toolbox?]]
* ''war'' - A vector of float32 values with the perturbing variable: ''temperature'', ''time'', ''voltage'', ''pressure'', etc. The length of ''war'' must be equal to size(spc,2).
* [[Installation_of_the_mat2dcorr_toolbox|Installation of the mat2dcorr toolbox]]
* ''wav'' - A vector of float32 values, the ''wavenumber'' vector, or more general the vector of y-values (frequencies, wavenumbers, Raman shift. wavelength, or alternative variables). The length of ''wav'' must equal the number of data points per spectrum, i.e. size(wav,1) == size(spc,1). Equidistancy of the 'wav' vector is not a requirement.
* [[Disclaimer_and_License_Conditions |Disclaimer and license conditions]]
* [[mat2dcorr_-_Relevant Publications|Acknowledgement, relevant publications]]
* [[Frequently_Asked_Questions_(FAQ)|Frequently asked questions (FAQ)]]


== Related links ==
== Data Import & File Formats ==


* [[Matlab_Imaging_Format|Import data in the CytoSpec imaging format (CytoSpec/Matlab)]]
* [[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]]
* [[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)]]
* [[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:GraphAbstr.gif|500px|thumb|Mat2Dcorr: Illustration of Heterspectral 2D-COS (FTIR vs. Raman)]]
* [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]. Anal. Chem. 2017, 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
<br> &nbsp; <br>
Mar 18, 2023: more details of the mat2dcorr Matlab toolbox will follow soon

Revision as of 17:03, 2 April 2023

mat2dcorr - a free toolbox for performing two-dimensionsonal correlation (2D-COS) analysis with Matlab.

Introduction

Mat2Dcorr: Screenshot of the 2D control window (left) and the window '2D correlation analysis ... ' (right)

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, pharmaceutics, biomedical applications, and polymer or nanomaterial research, since its early introduction in 1989. 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 by 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 can 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

Data Import & File Formats

Options of the mat2dcorr Toolbox

Related Publications and Web Links

Mat2Dcorr: Illustration of Heterspectral 2D-COS (FTIR vs. Raman)


 

Mar 18, 2023: more details of the mat2dcorr Matlab toolbox will follow soon

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