Toggle Main Menu Toggle Search

Open Access padlockePrints

A MATLAB toolbox for data pre-processing and multivariate statistical process control

Lookup NU author(s): Emeritus Professor Julian Morris

Downloads


Licence

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND).


Abstract

A Multivariate Statistical Data Pre-screening/Data Pre-processing Toolbox (Pre-Screen) has been designed and developed for use by practising process engineers and researchers who wish to pre-process process data prior to multivariate data analysis, process data modelling or building predictive and inferential models. Many commercial data analysis packages do not fully address the initial data cleaning and data conditioning tasks which can consume up to 80% of the modelling time. The software toolkit has been developed specifically with the aim of focusing on the industrial needs for the initial data pre-screening of large industrial data sets. The core feature of Pre-Screen is that it has been specifically developed to make the analysis of large data sets as fast and visual as possible, and accessible for both process and control engineers, analytical scientists and academic R&D without taking away the need for engineering science understanding. The toolbox builds on top of the MATLAB numerical computing environment, with powerful user interface procedures providing user friendly, mouse/menu driven software. The toolbox has been complied to allow use by those whom do not have access to MATLAB.


Publication metadata

Author(s): Yi G, Herdsman C, Morris J

Publication type: Article

Publication status: Published

Journal: Chemometrics and Intelligent Laboratory Systems

Year: 2019

Volume: 194

Print publication date: 15/11/2019

Online publication date: 04/10/2019

Acceptance date: 25/09/2019

Date deposited: 09/01/2020

ISSN (print): 0169-7439

ISSN (electronic): 1873-3239

Publisher: Elsevier BV

URL: https://doi.org/10.1016/j.chemolab.2019.103863

DOI: 10.1016/j.chemolab.2019.103863


Altmetrics

Altmetrics provided by Altmetric


Share