https://github.com/marta-barea/machine-learning_visnirs-waxes-type-mixture-regression

Spectroscopic data processing approaches for petroleum wax blends quantification
https://github.com/marta-barea/machine-learning_visnirs-waxes-type-mixture-regression

chemistry chemoinformatics chemometrics machine-learning petroleum r

Last synced: about 2 months ago
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Spectroscopic data processing approaches for petroleum wax blends quantification

• Host: GitHub
• URL: https://github.com/marta-barea/machine-learning_visnirs-waxes-type-mixture-regression
• Owner: Marta-Barea
• License: gpl-3.0
• Created: 2022-04-28T13:24:17.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2024-03-28T21:34:57.000Z (9 months ago)
• Last Synced: 2024-03-28T22:31:59.390Z (9 months ago)
• Topics: chemistry, chemoinformatics, chemometrics, machine-learning, petroleum, r
• Language: R
• Homepage:
• Size: 2.53 MB
• Stars: 0
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Machine learning-based approaches to Vis-NIR data for the automated characterization of petroleum wax blends

## Description

This repository contains the source code for all data processing and the application of machine learning algorithms used in the article "Machine learning-based approaches to Vis-NIR data for the automated characterization of petroleum wax blends".

## Contents

- `spectra/`: Folder containing the spectra data.

- `ffeature selection plot: Source code for visualizing variables selected by the Boruta Algorithm and Genetic Algorithm.

- `supervised algorithms/`: Source code for all the supervised machine learning models and experiments.

- `unsupervised algorithms/`: Source code related to unsupervised learning techniques and clustering.

- `App/`: A Shiny application to demonstrate and visualize the findings.

## Requirements

All data analysis was performed with **R (version 4.1.2)**. The software and packages used include:

- **prospectr (version 0.2.3)**: Implemented for spectral data processing using the SG algorithm.

- **Boruta (version 7.0.0)**: Employed for feature selection through the Boruta algorithm.

- **caret (version 6.0–90)**: Used for the application of the Genetic Algorithm (GA) and for the development of PLS, SVR, and RF models.

- **MLmetrics (version 1.1.1)**: Provided model evaluation metrics.

- **graphics (version 4.1.2)** and **ggplot2 (version 3.3.5)**: Facilitated data and model output visualization.

- **shiny (version 1.7.1)**: Enabled the development of an interactive web application.