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

Chris Aldrich, Leanne Smith, David Verrelli, Warren Bruckard, Melissa Kistner, Lidia Auret

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

2 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationProceedings of the XXVII International Mineral Processing Congress – IMPC 2014
EditorsJuan Yianatos, Alex Doll, Cesar Gomez, Romke Kuyvenhoven
Place of PublicationChile
Number of pages9
Publication statusPublished - Oct 2014
Externally publishedYes
Event27th International Mineral Processing Congress, IMPC - 2014 - Santiago, Chile
Duration: 20 Oct 201424 Oct 2014


Other27th International Mineral Processing Congress, IMPC - 2014


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