Parffin Wax Odor detection using HS-GC/MS data combined with ML

The machine-learning_HSGCMS_ParaffinWax_Odor repository contains data processing approaches and machine learning techniques applied to discriminate and quantify the odor of paraffin wax samples. The study focuses on the classification of paraffin wax based on hydroprocessing levels, which significantly affect their olfactory characteristics.

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🔍 Objective

The main goal of this project is to classify paraffin wax samples into five categories based on the degree of hydroprocessing:

• None: Non-hydrotreated paraffin.
• Low: 75% hydrotreated + 25% non-hydrotreated paraffin.
• Moderate: 50% hydrotreated + 50% non-hydrotreated paraffin.
• High: 25% hydrotreated + 75% non-hydrotreated paraffin.
• Full: Fully hydrotreated paraffin.

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🛠️ System Requirements

Software

• R version 4.2.0
• RStudio (optional but recommended)

1. Hierarchical Clustering Analysis (HCA):

   • Performed using the hclust function from the stats package (version 4.1.2).
   • The agglomerative coefficient was calculated using the agnes function from the cluster package (version 2.1.3).
   • Results were visualized with the fviz_dend function from the factoextra package (version 1.0.7).

2. Supervised Learning Models:

   • Developed using the trainControl and train functions from the caret package (version 6.0-93).
   • Regression metrics were calculated using the MLmetrics package (version 1.1.1).
   • Important variables for PLS and RF models were extracted with the varImp function from the caret package.

3. Data Visualization:

   • Plots generated using ggplot2 (version 3.3.6) and graphics (version 4.1.2).

4. Interactive Web Application:

   • Developed with the shiny package (version 1.7.2).

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📂 Repository Structure

• App/: Contains the interactive web application developed using Shiny.
• method development/: Scripts and notebooks for developing data processing methods.
• supervised algorithms/: Scripts for supervised machine learning models.
• unsupervised algorithms/: Scripts for unsupervised learning models, such as clustering.
• tis/: Analysis scripts for Total Ion Spectra of paraffin wax samples.
• .gitattributes and .gitignore: Repository configuration files.
• LICENSE: Project license.
• README.md: This file.

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⚙️ How to Use This Repository

Clone the Repository

git clone https://github.com/Marta-Barea/hsgcms-paraffinwax-odor-ml
cd hsgcms-paraffinwax-odor-ml

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 hydroprocessing grades 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.