PREDICTIVE PATH MODELLING FOR RIVER MONITORING source code and demonstration

Published: 21 July 2022| Version 3 | DOI: 10.17632/jcdxmvj76k.3
Contributors:
Maria Cairoli,
,
,
,
Andre' van den Doel,

Description

Source code for: 'Monitoring pollution pathways in river water by predictive path modelling using untargeted GC-MS measurements'. Demonstration of the Process PLS analysis on chemical concentrations previously extracted from GC-MS measurements performed in 9 sampling sites along the river Rhine. The raw GC-MS data were provided by RIWA-Rijn and Rijkswaterstaat. For the original Process PLS code and demonstration, please see: van Kollenburg, Geert; Offermans, Tim; Jansen, Jeroen; Bouman, Roel; Postma, Geert (2022), “Data, software and scripts related to the Process PLS methodology manuscript”, Mendeley Data, V6, doi: 10.17632/9x9h7fr4kn.6

Files

Steps to reproduce

For details, please see 'README.txt' in the ZIP-archive. All the analysis is reproduced in the Matlab file: processpls_river.m.

Institutions

Radboud Universiteit Institute for Molecules and Materials

Categories

Multivariate Analysis, Chemometrics, Gas Chromatography Mass Spectrometry, Chemometrics Software, River, Statistical Analysis

License