Improving Textile Sustainability through Accurate Dye Recipe Prediction using the Mrango-Owens Modification of the Single Constant Kubelka-Munk Equation

Huw Owens, Satyadev Rosunee (Editor)

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


    ‘Right first time’ dye recipe prediction, where a computer based system can accurately calculate the mixture of dyes and auxiliaries needed to match a target fabric shade is the ultimate goal for dyers and colourists in the textile industry. If the dyer can produce an accurate reproduction of the target shade, both time and resources can be saved hence contributing to the sustainability of the textile industry and minimizing the impact on the environment. Computer based colour recipe prediction systems have been available since the 1960s and are based on approximations of the theory first proposed by Kubelka and Munk. The Kubelka-Munk approach is desirable as it provides a linear model that relates the amount of applied dye with the light absorption and scattering properties of the dyed fabric. Single constant Kubelka-Munk theory and other derivative models have been successfully applied over the last few decades in the textile dye recipe prediction area primarily due to the simplicity of the model. One limitation of single constant Kubelka-Munk theory is the poor linearity with applied dye concentration, particularly at higher levels of dye concentration, due to the fabric becoming saturated with dye. The linearisation of the single constant Kubelka-Munk equation by introducing modified K/S-values calculated using the Mrango-Owens equation shows an improved performance in both linearity and recipe prediction. The Mrango-Owens equation addresses the non-linearity of the original single-constant K-M equation and shows significant improvements in the accurate prediction of textile dye recipes when compared to the original single constant Kubelka-Munk and its derivatives (i.e. Pineo, Derbyshire-Marshall and McDonald equations). The Mrango-Owens equation accurately predicts the applied dye concentration for the cotton fabric samples dyed with reactive dyes from very lower shade (i.e. 0.5% o.w.f) to the heavier shade dyed samples (i.e. 10% o.w.f) whereby reduces the overall error on predicting the applied dye concentration by 70% compared to the original single constant Kubelka-Munk equation.
    Original languageEnglish
    Title of host publicationInternational Textile & Apparel Sustainability Conference
    EditorsSatyadev Rosunee
    PublisherUniversity of Mauritius
    Number of pages11
    Publication statusPublished - 16 Jul 2012
    EventInternational Textile and Apparel Sustainability Conference (ITASC) - Mauritius
    Duration: 16 Jul 201221 Jul 2012


    ConferenceInternational Textile and Apparel Sustainability Conference (ITASC)


    • Schuster-Kubelka-Munk Theory
    • Derbyshire-Marshall
    • Pineo
    • Linearisation
    • Colour Match Prediction
    • Dye Recipe Prediction


    Dive into the research topics of 'Improving Textile Sustainability through Accurate Dye Recipe Prediction using the Mrango-Owens Modification of the Single Constant Kubelka-Munk Equation'. Together they form a unique fingerprint.

    Cite this