Deconvolution of fluorescence spectra

In summary: UTF-8In summary, the original spectra is a yellow graph, and the other two graphs are Cauchy distributions. The program I am trying to use to deconvolve the spectra does not work, and I need the component curves to be able to analyse the spectra correctly.
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Kristoffer Linder
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I am trying to make a deconvolution of fluorescence spectra in Matlab. The original spectra is the yellow graph in the figure below. The other two
graphs are Cauchy distributions, x and y, that I have manually added to the plot. I would like to write a program that could do this automatically for me, I have already tried deconv() but I do not get it to work, I have also try to estimate Gaussian distributions but it seems to me that the Cauchy distribution is more likely. I have tried to use conv(x,y,'same') to check if the manually found curved could be a used as an estimation, but it seems to me that after convolution I do not really get the desired answer, since I want an asymmetric curve, just as in the original graph. I need the component curves to be able to analyse the spectra correctly, since using the maxima of the original graph does not correspond very well with references. My Matlab code for the manual finding is
Matlab:
close all
mu1 = 550;
mu2 = 640;
sigma1 = 10;
sigma2 = 50;
pd1 = makedist('tLocationScale','mu',mu1,'sigma',sigma1,'nu',9)
pd2 = makedist('tLocationScale','mu',mu2,'sigma',sigma2,'nu',5)
y1 = pdf(pd1,wavelength(100:1300,36));
y2 = pdf(pd2,wavelength(100:1300,36));
y1 = .6*y1/max(y1);
y2 = .5*y2/max(y2);
figure, plot(wavelength(100:1300,36),y1,'LineWidth',1)
hold on, plot(wavelength(100:1300,36),y2,'LineWidth',1)
hold on, plot(wavelength(100:1300,36),intensity(100:1300,36)/max(intensity(100:1300,36)))
Anyone knows a good way to find the components of the original graph, and maybe what distribution to use? Also when using conv() in Matlab I get a problem with the scaling, since the output has much larger amplitude then the input.

deconvolution2.png
 
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1. What is deconvolution of fluorescence spectra?

Deconvolution of fluorescence spectra is a mathematical process used to separate the overlapping fluorescence signals from multiple fluorophores in a mixed sample. It is used to identify and quantify the individual components of a complex fluorescence spectrum.

2. What is the purpose of deconvolution of fluorescence spectra?

The purpose of deconvolution of fluorescence spectra is to improve the accuracy and resolution of fluorescence measurements. By separating the overlapping signals, it allows for better identification and quantification of the individual fluorophores present in a sample.

3. What are the common methods used for deconvolution of fluorescence spectra?

The two main methods of deconvolution of fluorescence spectra are curve fitting and spectral unmixing. Curve fitting involves fitting a mathematical model to the experimental data, while spectral unmixing uses a reference library of fluorescence spectra to identify and quantify the components in a mixed sample.

4. What are the factors that can affect the accuracy of deconvolution of fluorescence spectra?

The accuracy of deconvolution of fluorescence spectra can be affected by a number of factors, including the quality of the experimental data, the choice of deconvolution method, and the complexity of the fluorescence spectrum. Additionally, the presence of background noise and overlapping signals can also impact the accuracy of the deconvolution process.

5. What are the applications of deconvolution of fluorescence spectra?

Deconvolution of fluorescence spectra is widely used in various scientific fields, including biochemistry, biophysics, and medical research. It is commonly used to study the interactions between biomolecules, analyze protein structures, and identify and quantify fluorescent biomarkers in biological samples. It also has applications in drug discovery and development, environmental monitoring, and forensic science.

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