https://github.com/bamescience/hmfa-spectra

Indirect Hard Modeling (IHM) for Spectral Component Identification
https://github.com/bamescience/hmfa-spectra

Last synced: 29 days ago
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Indirect Hard Modeling (IHM) for Spectral Component Identification

• Host: GitHub
• URL: https://github.com/bamescience/hmfa-spectra
• Owner: BAMeScience
• Created: 2024-11-15T16:56:52.000Z (6 months ago)
• Default Branch: main
• Last Pushed: 2024-11-15T17:08:36.000Z (6 months ago)
• Last Synced: 2025-03-30T12:11:14.601Z (about 2 months ago)
• Language: Python
• Size: 9.77 KB
• Stars: 1
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.MD

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README

        
# Indirect Hard Modeling (IHM) for Spectral Component Identification

This repository implements the **Indirect Hard Modeling (IHM)** method as described in the paper *"Identification of Unknown Pure Component Spectra by Indirect Hard Modeling"* by Kriesten et al., 2008. The code extracts pure spectra from a given spectral mixture matrix using a combination of peak fitting, correlation analysis, and optimization techniques.

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## Overview of the Method

The IHM approach models a spectral mixture matrix by:

1. **Finding a representative spectrum** (x_input) from a subset of mixture spectra (Step 1).

2. **Fitting Voigt profiles** to the peaks in x_input, capturing nonlinear effects like peak shifts and variations (Step 2).

3. **Constructing a weight matrix** representing contributions of the fitted peaks across all mixture spectra (Step 3).

4. **Correlation-based analysis** to identify distinctive and shared peaks, allowing classification of peaks into pure components (Step 4).

5. **Delta matrix optimization** to allocate shared peaks to their corresponding components, ensuring a coherent representation of the spectral data (Step 5).

6. **Pure spectrum reconstruction** using the optimized delta matrix to obtain individual pure spectra (Step 6).

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## Code Structure and Workflow

### Step 1: Input Data

- **Spectral Matrix**: Provide your mixed spectral data in `spectal_matrix`. The matrix should have dimensions corresponding to the number of spectra and wavelength points.

- **Wavelength Vector**: Ensure `wl` matches the wavelength points of your spectral matrix.

### Step 2: Finding x_input

- The code identifies a representative spectrum by averaging a subset of spectra with the lowest correlation errors.

### Step 3: Voigt Peak Fitting

- Fits Voigt profiles to the representative spectrum (x_input) to extract peak parameters.

- Outputs:

  - **Voigt Parameters**: Saved to `Voig_parameters_X_input`.

  - **Reconstructed Spectrum**: Visualized alongside the mean spectrum.

### Step 4: Weight Matrix Calculation

- Constructs the weight matrix W by fitting the Voigt parameters to all spectra in the dataset.

- Outputs:

  - **Weight Matrix**: Saved to `WeightsMatrix`.

### Step 5: Correlation Analysis

- Constructs a peak-to-peak correlation matrix to classify peaks as:

  - **Distinctive Peaks**: Unique to individual components.

  - **Shared Peaks**: Common across multiple components.

- Determines the number of components (K) and identifies correlated groups of peaks.

### Step 6: Delta Matrix Optimization

- Optimizes the delta matrix to allocate shared peaks to pure components using constrained optimization.

### Step 7: Pure Spectrum Reconstruction

- Reconstructs the pure spectra using the optimized delta matrix.

- Outputs:

  - **Reconstructed Spectra**: Visualized alongside the original mean spectrum.

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## Usage Instructions

1. **Prepare Input Data**:

   - Replace `spectal_matrix` with your spectral data.

   - Provide the corresponding wavelength vector `wl`.

2. **Run the Code**:

   - Set `run_fitting = True` to perform new peak fitting. If previously run, set it to `False` to reuse saved results.

   - Set `Only_SharedPeaks = True` to optimize shared peaks only, if required.

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## References

- **Kriesten, E., et al. (2008)**: "Identification of Unknown Pure Component Spectra by Indirect Hard Modeling".  

  [DOI:10.1016/j.chemolab.2008.05.002](https://doi.org/10.1016/j.chemolab.2008.05.002)

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