Machine Learning for VIS-NIR Wax Mixture Regression

The visnirs-wax-mixture-regression repository includes data processing methods and machine learning approaches applied to the analysis and characterization of petroleum wax blends. The focus is on classifying and predicting the composition of wax mixtures using VIS-NIR spectroscopy data.

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🛠️ System Requirements

Software

• R version 4.1.2 (or higher)
• RStudio (optional but recommended)

1. Feature Selection:

   • Boruta Algorithm using the Boruta package (v7.0.0).
   • Genetic Algorithm implemented via the caret package (v6.0-90).

2. Supervised Learning Models:

   • Developed with the caret package (v6.0-90).
   • Includes Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), and Random Forest Regression (RF).
   • Evaluation metrics calculated using the MLmetrics package (v1.1.1).

3. Data Preprocessing:

   • Spectral smoothing and normalization using the prospectr package (v0.2.3).

4. Visualization:

   • Results visualized with the ggplot2 package (v3.3.5) and base R graphics.

5. Interactive Web Application:

   • Built with the shiny package (v1.7.1).

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📂 Repository Structure

• spectra/: Contains scripts for raw and preprocessed VIS-NIR spectral data visualization.
• feature selection plot/: Scripts for visualizing feature importance from Boruta and Genetic Algorithm.
• supervised algorithms/: Scripts for supervised learning experiments.
• unsupervised algorithms/: Scripts for unsupervised learning and clustering analysis.
• App/: Shiny application for interactive exploration of the results.
• LICENSE: Licensing information.
• README.md: This file.

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⚙️ How to Use This Repository

Clone the Repository

git clone https://github.com/Marta-Barea/visnirs-wax-mixture-regression
cd visnirs-wax-mixture-regression

Running the Shiny Application

1. Place app.R, svm.rds, svr.rdsand test_data.xlsx in the same folder.
2. In your R console, run:

shiny::runApp("app.R")

3. Use the web interface to:

• 📁 Upload .csv or .xlsx data files.
• 🛠️ Preprocess data using advanced filtering techniques.
• 🤖 Predict wax blends with AI.

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📂 Example Dataset

A sample dataset (test_data.xlsx) is included for demonstration purposes. It contains Vis-NIR spectral readings and hydroprocessing grades for various wax samples.

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🤝 Contributors

• University of Cádiz (AGR-291 Research Group)
  • Specializing in hydrocarbon characterization and spectroscopy.

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📜 License

This project is licensed under the GNU GENERAL PUBLIC License. See LICENSE for details.