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<p>In the context of the assessment and validation of plasma-generated atomic oxygen (AO), within the framework of EU Horizon MOXY project, this study compared three non-invasive methodologies for the semi-quantitative evaluation of cleaning efficacy: CIELAB colorimetry, statistical analysis of image brightness histograms and a supervised machine learning method (TWS). The research focuses on assessing the strengths and limitations of these methods when applied to complex, highly textured substrates such as textiles. A benchmark set of simplified model systems (SMSs), consisting of pongee silk swatches artificially soiled with soot and treated with AO alongside seven alternative cleaning methods, was used as case study. Among the evaluated techniques, the machine-learning-based approach demonstrated high reliability and versatility for the selective detection of heterogeneous soiling on highly reflective surfaces. Due to its open-access design and user-friendly interface, the method has strong potential for wider use in systematically evaluating cleaning efficacy across various conservation contexts.</p>

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Evaluating cleaning efficacy by image-based machine learning: case study of soot removal from silk

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年8月1日

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  • Marta Cremonesi,
  • Gianluca Pastorelli,
  • Nan Yang,
  • Ehab Al-Emam,
  • Elisabetta Martinelli,
  • Daniela Comelli,
  • Alice Dal Fovo,
  • Raffaella Fontana,
  • Riccardo Cicchi,
  • Natalia Ortega Saez,
  • Jesse Berwouts,
  • Koen Janssens,
  • Geert Van der Snickt

摘要

In the context of the assessment and validation of plasma-generated atomic oxygen (AO), within the framework of EU Horizon MOXY project, this study compared three non-invasive methodologies for the semi-quantitative evaluation of cleaning efficacy: CIELAB colorimetry, statistical analysis of image brightness histograms and a supervised machine learning method (TWS). The research focuses on assessing the strengths and limitations of these methods when applied to complex, highly textured substrates such as textiles. A benchmark set of simplified model systems (SMSs), consisting of pongee silk swatches artificially soiled with soot and treated with AO alongside seven alternative cleaning methods, was used as case study. Among the evaluated techniques, the machine-learning-based approach demonstrated high reliability and versatility for the selective detection of heterogeneous soiling on highly reflective surfaces. Due to its open-access design and user-friendly interface, the method has strong potential for wider use in systematically evaluating cleaning efficacy across various conservation contexts.