Multivariate image analysis of a realgar-orpiment batch froth flotation system

In this investigation, different mixtures of realgar and orpiment particles were floated in a laboratory batch flotation cell and multivariate image analysis was used to estimate the arsenic content in the froths. The analysis was based on the froth colour, as well as extraction of three groups of t...

Full description

Bibliographic Details
Main Authors: Aldrich, Chris, Smith, L., Verrelli, D., Bruckard, W., Kistner, M., Auret, L.
Format: Conference Paper
Published: Gecamin 2014
Online Access:http://hdl.handle.net/20.500.11937/29885
_version_ 1848752929373159424
author Aldrich, Chris
Smith, L.
Verrelli, D.
Bruckard, W.
Kistner, M.
Auret, L.
author_facet Aldrich, Chris
Smith, L.
Verrelli, D.
Bruckard, W.
Kistner, M.
Auret, L.
author_sort Aldrich, Chris
building Curtin Institutional Repository
collection Online Access
description In this investigation, different mixtures of realgar and orpiment particles were floated in a laboratory batch flotation cell and multivariate image analysis was used to estimate the arsenic content in the froths. The analysis was based on the froth colour, as well as extraction of three groups of textural features, namely those based on grey level co-occurrence matrices (GLCMs), wavelets and local binary patterns (LBPs). Collectively, these features provided better information on the arsenic content of the froths than any one of the individual groups of features. Partial least squares models could explain approximately 78% of the variance in the arsenic by using all the features combined. The colour content, particularly the green component in the red-green-blue (RGB) features, provided almost as much information as each of the three sets of texture-base features individually.
first_indexed 2025-11-14T08:16:26Z
format Conference Paper
id curtin-20.500.11937-29885
institution Curtin University Malaysia
institution_category Local University
last_indexed 2025-11-14T08:16:26Z
publishDate 2014
publisher Gecamin
recordtype eprints
repository_type Digital Repository
spelling curtin-20.500.11937-298852017-01-30T13:15:58Z Multivariate image analysis of a realgar-orpiment batch froth flotation system Aldrich, Chris Smith, L. Verrelli, D. Bruckard, W. Kistner, M. Auret, L. In this investigation, different mixtures of realgar and orpiment particles were floated in a laboratory batch flotation cell and multivariate image analysis was used to estimate the arsenic content in the froths. The analysis was based on the froth colour, as well as extraction of three groups of textural features, namely those based on grey level co-occurrence matrices (GLCMs), wavelets and local binary patterns (LBPs). Collectively, these features provided better information on the arsenic content of the froths than any one of the individual groups of features. Partial least squares models could explain approximately 78% of the variance in the arsenic by using all the features combined. The colour content, particularly the green component in the red-green-blue (RGB) features, provided almost as much information as each of the three sets of texture-base features individually. 2014 Conference Paper http://hdl.handle.net/20.500.11937/29885 Gecamin restricted
spellingShingle Aldrich, Chris
Smith, L.
Verrelli, D.
Bruckard, W.
Kistner, M.
Auret, L.
Multivariate image analysis of a realgar-orpiment batch froth flotation system
title Multivariate image analysis of a realgar-orpiment batch froth flotation system
title_full Multivariate image analysis of a realgar-orpiment batch froth flotation system
title_fullStr Multivariate image analysis of a realgar-orpiment batch froth flotation system
title_full_unstemmed Multivariate image analysis of a realgar-orpiment batch froth flotation system
title_short Multivariate image analysis of a realgar-orpiment batch froth flotation system
title_sort multivariate image analysis of a realgar-orpiment batch froth flotation system
url http://hdl.handle.net/20.500.11937/29885