SIMCA mCP
version 1: July 2023
ABOUT SIMCA mCP:
This repository contains data and Matlab scripts to facilitate the implementation of a DD-SIMCA model for the acceptance testing of purified m-cresol purple (mCP) for spectrophotometric pH measurements in seawater. The model was trained on measurements of UV-visible absorbance spectra of purified mCP and tested on independent datasets consisting of purified and unpurified mCP samples. The repository contains demo scripts that will demonstrate the training and optimization of the DD-SIMCA model and reproduce the figures in the associated publication (see citation below). A function is provided for users to classify new mCP samples with the model.
You may cite the use of the data as follows:
Fong, Michael, Takeshita, Yuichiro, Easley, Regina, Waters, Jason (2023), UV-visible absorbance spectra of purified and unpurified m-cresol purple samples in sodium hydroxide and sodium chloride solutions at pH 12, Version 1.0.0, National Institute of Standards and Technology, https://doi.org/10.18434/mds2-3055 (Accessed: [give download date])
You may cite the publication as follows:
Fong, M.B., Takeshita, Y., Easley, R., Waters, J. (in prep) Detection of impurities in m-cresol purple with SIMCA for the quality control of spectrophotometric pH measurements in seawater. Marine Chemistry.
Project Status: Maintenance only
Testing Summary
The demo scripts have been tested to ensure that they can be run in Matlab R2019b and Matlab R2022a with only the provided functions, data files, and PLS Toolbox version 8.9.2 (Eigenvector Research) and that they reproduce the figures in the associated manuscript.
Getting Started
Download the repository SIMCA mCP and add the main repository as well as all subfolders to MATLAB's search path.
Run either of the m-files in the \demo folder to verify that the scripts work.
Demo_OptimizeSIMCAModel.m will illustrate the training and optimization of the DD-SIMCA Model and generate Fig. 1 and Fig. 4 from the manuscript.
Demo_ClassifyNewmCPSamples provides an example of classifying new mCP samples with the optimized model and generates Fig. 5 from the manuscript. This demo requires PLS Toolbox to perform the instrument standardization.
The demo scripts use the data contained in the folder \data.
New samples measured by the user can be classified with the SIMCA model by calling:
NewClass = ClassifymCPSamples(class_labels, A_samples)
The inputs are class_labels, a cell array of sample names, and A_samples, a matrix containing the spectra of the samples. This function loads the optimized SIMCA model saved in mCP SIMCA Model\NIST_mCP_SIMCAModel.mat and calls DDSTask to classify the new samples
Prerequisites
Requires MATLAB and PLS Toolbox.
Author
Michael Fong
Copyright
This software was developed by employees of the National Institute of Standards and Technology (NIST), an agency of the Federal Government and is being made available as a public service. Pursuant to title 17 United States Code Section 105, works of NIST employees are not subject to copyright protection in the United States. This software may be subject to foreign copyright. Permission in the United States and in foreign countries, to the extent that NIST may hold copyright, to use, copy, modify, create derivative works, and distribute this software and its documentation without fee is hereby granted on a non-exclusive basis, provided that this notice and disclaimer of warranty appears in all copies.
To see the latest statement, please visit: https://www.nist.gov/director/copyright-fair-use-and-licensing-statements-srd-data-and-software
Also see the licenses:
SIMCA mCP\LICENSE.md
SIMCA mCP\functions\dd-simca-master\LICENSE.md
SIMCA mCP\functions\suplabel\LICENSE.txt
Acknowledgments
suplabel.m was written by Ben Barrowes (barrowes@alum.mit.edu)
normv2.m was written by Roma Tauler and Anna de Juan,
Chemometrics and Solution Equilibria Group, University of Barcelona
This repository includes the DD-SIMCA tool developed by Zontov et al. (2017).
Y.V. Zontov, O.Ye. Rodionova, S.V. Kucheryavskiy, A.L. Pomerantsev,
DD-SIMCA – A MATLAB GUI tool for data driven SIMCA approach, Chemometrics and Intelligent Laboratory Systems, Volume 167, 2017,
Pages 23-28, ISSN 0169-7439, DOI:[10.1016/j.chemolab.2017.05.010](https://doi.org/10.1016/j.chemolab.2017.05.010).
Contact
For questions, comments, or reporting any bugs, please contact Michael Fong (michael.fong@nist.gov).