Correlations of Near-Infrared Spectra to Bulk Properties in Polyolefins, using Principal Component Analysis

This repository supports the following peer-reviewed manuscript:

Bradley P. Sutliff, Shailja Goyal, Tyler B. Martin, Peter A. Beaucage, Debra J. Audus, and Sara V. Orski, "Correlating Near-Infrared Spectra to Bulk Properties in Polyolefins" Macromolecules 2024, 57. 5. 2329-2338 DOI: https://doi.org/10.1021/acs.macromol.3c02290

which uses functional principal component analysis to connect near-visible infrared (NIR) spectra of polyolefins with physical properties to enable better sorting of these materials.

The repository is intended for the following use cases:

• Illustrate key ideas from the submitted manuscript including using functional principal component analysis and sliced inverse regression for correlating NIR with physical properties.
• Reproduce figures from our submitted manuscript
• Allow for full reproducibility of the data in the submitted manuscript

Running the notebook

The included notebook nir-corr-po.ipynb allows the user to reproduce all of the figures in the corresponding submitted manuscript, using the corresponding data. The notebook is setup to walk the user through each of the preprocessing and analyzing steps, generating corresponding figures along the way. All code is written in Python and requires Python == 3.9. It can be used on any operating system. Other requirements are listed in requirements.txt.

The code can either be run locally or in Google Colab.

Run notebook locally (option 1)

Clone the code

First clone the code via

git clone https://github.com/usnistgov/nir_corr_po.git

and navigate to the directory where the repository lives

cd nir_corr_po

Next, one needs to create a virtual environment. This can be done using Python virtual environments or with Anaconda. Both options are listed below.

Create a Python virtual environment (option 1)

First, make sure you are using Python 3.9.

python3 -m venv env

where env is the location of the virtual environment

Activate the virtual environment

source env/bin/activate

Install dependencies

python3 -m pip install -r requirements.txt

Create a virtual environment with Anaconda (option 2)

First, install conda. Then run the following in a conda-enabled terminal.

conda env create -f environment.yml

This yml file will automatically name the new environment ncp.

If you are using conda>=4.6, activate the virtual environment via

conda activate ncp

Otherwise, see the conda docs.

Run the notebook

The Jupyter notebook can be run by using the command

jupyter notebook

Running notebook in Google Colab (option 2)

If you are interested in running the notebook in Google Colab, first click on the relevant link below. Note that these links were generated by navigating to the notebook of interest on the nir_corr_po GitHub page, for example, https://github.com/usnistgov/nir_corr_po/nir-corr-po.ipynb and then replace github.com with githubtocolab.com.

• nir-corr-po

This should open the notebook in Google Colab.

Contact

Bradley P. Sutliff, PhD
Materials Science and Engineering Division
Material Measurement Laboratory
National Institute of Standards and Technology

Email: [email protected]
GitHubID: @bpsut
Staff website: https://www.nist.gov/people/bradley-sutliff

How to cite

If you use the code, please cite our submitted manuscript:

Bradley P. Sutliff, Shailja Goyal, Tyler B. Martin, Peter A. Beaucage, Debra J. Audus, and Sara V. Orski, "Correlating Near-Infrared Spectra to Bulk Properties in Polyolefins" Macromolecules 2024, 57. 5. 2329-2338 DOI: https://doi.org/10.1021/acs.macromol.3c02290

If you use the data, please cite:

Sutliff, Bradley; Goyal, Shailja; Martin, Tyler; Beaucage, Peter; Audus, Debra; Orski, Sara. Correlations of Near-Infrared Spectra to Bulk Properties in Polyolefins, using Principal Component Analysis, National Institute of Standards and Technology (2023), https://doi.org/10.18434/mds2-3022 (Accessed YYYY-MM-DD)

Future updates and maintenance

There is no intent to update/maintain this repository once it is released to the public, given that it is intended to reproduce published figures and analysis that should not change over time.