QC4SUSTEX – Quality Control for Sustainable Textiles

“QC4SUSTEX: Authentication and security of sustainable textiles and development of equipment for the environmental control in their manufacturing” is a research project developed by a consortium of 5 Galician SMEs: AMSLAB, MESTRELAB, ORGANISTRY, 2XMIL and XENOTECHS, further supported by two research groups at the University of Santiago de Compostela and at Citius (IT Singular research centre).

The project is funded by the Galician Innovation Agency (GAIN), through the Conecta-PEME programme and the European Regional Development Fund (ERDF) within the Thematic Objective I, “Strengthening research, technological development and innovation”, included in the framework of programme FEDER Galicia 2014-2020. It also has the support of the Consellería de Economía, Emprego e Industria. The total budget of the project amounts to 1.384.032,10 €.

The objective of the project is the development of efficient solutions to be sold globally to meet the specific demands of the sustainable fashion market for quality control. The three main objectives of the project are:

  1. To develop tools for the authentication in the lab of sustainability claims or certifications of fibres and fabrics used in clothes and footwear.
  2. To evaluate and improve the safety of materials used in the manufacturing of sustainable fashion.
  3. To develop an integral turnkey service to control the environmental impact on water of the suppliers to the main textile retailers. This will include equipment for continuous monitorisation, in-situ sampling solutions for inspections and an application for the in-situ determination of chemical substances by colorimetry.

During 2019 the consortium worked in several areas related to sustainable fashion verification:

  • Although early days, several tools were developed and commercialized, including testing for vegan products and organic cotton.
  • We have identified markers to differentiate recycled and virgin synthetic fibres (PET and polyamide) using advanced chemometric tools and the newly developed Mnova ElViS, as a first step towards applying machine learning techniques.
  • A standard test to simulate microplastic release during machine-washing has been designed and validated, with images being processed and analysis using artificial vision techniques.
  • Several bio-based dyes were evaluated as alternatives to petroleum-derived ones.
  • An app for Android to carry out in-situ effluent colorimetric measurements was developed with the textile industry in mind. Our study concludes that recycling of textile fibres does not pose any specific chemical risk.