Machine Learning Analysis of Raman Spectra to Quantify the Organic Constituents in Complex Organic-Mineral Mixtures

Welcome to the Raman analysis repository! This repository contains code, data, and documentation related to the analysis of memetic organic-mineral soil composition using Raman spectroscopy. The project aims to overcome challenges posed by complex organic/mineral compositions and fluorescence interference in soil analysis. Here we aim to provide the best practices on how to boost the quantitative power of Raman spectroscopy as a probe of chemical composition in complex mixtures.

Table of Contents

• Codes
• Data
• Predictions
• Helper Functions
• License
• Abstract
• Code Details

Codes

Explore the following Jupyter notebooks for data analysis and model development:

• Amino_Acids_AA.ipynb
• Amino_Acids_Fluorescence_AAF.ipynb
• Amino_Acids_Minerals_AAM.ipynb
• Supporting_Information_Extra.ipynb

Data

Access datasets for various soil compositions and ground truth data:

• AAF_Data
• AAM_Data
• AA_Data
• AA_GroundTruth
• AA_Pure

Predictions

Find predicted results from the models in the Predictions directory.

Helper Functions

Explore helpful Python scripts for data loading, preprocessing, and model creation:

• load_data.py