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pnnl/Chemometric_Toolbox

RESOURCE

Publicly Accessible Repository
https://github.com/pnnl/Chemometric_Toolbox
https://doi.org/10.11578/dc.20221003.1

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Abstract

The PNNL Chemometric Toolbox is a software collection of common MATLAB scripts that implement core chemometric algorithms for regression analysis. The three core regression techniques within this toolbox are classical least squares (CLS), principal component regression (PCR), and partial least squares (PLS). This is demonstrated using a supplied dataset of infrared (FTIR) spectral data with their corresponding concentrations
Developers:
Smith, Ian [1]
  1. Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
Release Date:
2022-09-13
Project Type:
Open Source, Publicly Available Repository
Software Type:
Scientific
Licenses:
BSD 2-clause "Simplified" License
Sponsoring Org.:
Code ID:
92291
Site Accession Number:
Battelle IPID 32555-E
Research Org.:
Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
Country of Origin:
United States

RESOURCE

Publicly Accessible Repository
https://github.com/pnnl/Chemometric_Toolbox
https://doi.org/10.11578/dc.20221003.1

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Citation Formats

Smith, Ian. pnnl/Chemometric_Toolbox. Computer Software. https://github.com/pnnl/Chemometric_Toolbox. USDOE. 13 Sep. 2022. Web. doi:10.11578/dc.20221003.1.
Smith, Ian. (2022, September 13). pnnl/Chemometric_Toolbox. [Computer software]. https://github.com/pnnl/Chemometric_Toolbox. https://doi.org/10.11578/dc.20221003.1.
Smith, Ian. "pnnl/Chemometric_Toolbox." Computer software. September 13, 2022. https://github.com/pnnl/Chemometric_Toolbox. https://doi.org/10.11578/dc.20221003.1.
@misc{ doecode_92291,
title = {pnnl/Chemometric_Toolbox},
author = {Smith, Ian},
abstractNote = {The PNNL Chemometric Toolbox is a software collection of common MATLAB scripts that implement core chemometric algorithms for regression analysis. The three core regression techniques within this toolbox are classical least squares (CLS), principal component regression (PCR), and partial least squares (PLS). This is demonstrated using a supplied dataset of infrared (FTIR) spectral data with their corresponding concentrations},
doi = {10.11578/dc.20221003.1},
url = {https://doi.org/10.11578/dc.20221003.1},
howpublished = {[Computer Software] \url{https://doi.org/10.11578/dc.20221003.1}},
year = {2022},
month = {sep}
}