Vis-NIRS Wax Analysis Scripts

This repository contains a suite of R scripts and a Shiny web application for analyzing wax samples using Visible-Near Infrared (Vis-NIR) spectroscopy. These tools include preprocessing, classification, clustering, and dimensionality reduction methods tailored to study the effects of hydroprocessing on waxes.

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🔍 Overview of Scripts

1. Spectra_VisNIRS_Waxes_Type.R

   • Prepares Vis-NIR spectral data for analysis using smoothing, normalization, and scatter correction.

2. Gaussian_SVM_VisNIRS_Waxes_HT.R

   • Implements Gaussian Support Vector Machine (SVM) models to classify hydroprocessing grades.

3. RF_VisNIRS_Waxes_HT.R

   • Applies Random Forest classification to determine wax hydroprocessing levels.

4. HCA_VisNIRS_Waxes_HIFI.R

   • Performs Hierarchical Cluster Analysis (HCA) with dendrogram visualizations to group wax samples.

5. PCA_VisNIRS_Waxes_HIFI.R

   • Conducts Principal Component Analysis (PCA) and visualizes eigenvalues, scores, and loadings.

6. app.R

   • A Shiny application for uploading, preprocessing, visualizing, and classifying wax data.

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🛠️ System Requirements

Software

• R version 4.4.0 (2024-04-24, "Puppy Cup")
• RStudio (optional but recommended)

R Packages and Versions

Task	Package	Version
Spectral preprocessing	prospectr	0.2.7
Clustering (HCA)	stats	4.4.0
Dendrogram visualization	factoextra	1.0.7
PCA	stats	4.4.0
PCA visualization	factoextra	1.0.7
Machine learning (SVM, RF)	caret	6.0-94
Random Forest models	ranger	0.17.0
Data manipulation	dplyr, data.table, stringr	1.1.4, 1.16.2, 1.5.1
Radar charts	ggplot2, ggiraphExtra	3.5.1, 0.3.0
Visualization	ggplot2, viridis, egg	3.5.1, 0.6.5, 0.4.5
Web application	shiny	1.9.1
Web themes	shinythemes	1.2.0

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🚀 How to Use

Running Scripts

1. Open the R script in RStudio.
2. Update file paths and parameters as needed.
3. Run the script to analyze wax data.

Running the Shiny Application

1. Place app.R, weighted_rf.rds, and 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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✨ Features

• Preprocessing: Savitzky–Golay smoothing and scatter correction.
• Clustering: HCA with dendrogram visualization.
• Dimensionality Reduction: PCA with eigenvalues, score, and loading plots.
• Machine Learning: SVM and Random Forest models for hydroprocessing classification.
• Web Application: Intuitive Shiny interface for non-technical users.

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🤝 Contributors

• Nebux Cloud, S.L.
  • Experts in AI-driven data analysis solutions.
• 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.