ProductUpdated on 11 May 2026
RETEX - AI-Powered Textile Recognition
Field Engineer at Specim, Spectral Imaging Ltd
Munich, Germany
About
Specim RETEX combines hyperspectral imaging and AI to accurately identify textile materials, including complex blends and impurities, enabling automated material separation and scalable textile processing.
Why Hyperspectral Imaging?
Accurate material identification is essential for modern textile processing. Conventional machine vision technologies often struggle with blended and visually similar materials. Hyperspectral imaging provides detailed spectral information beyond the visible range, enabling precise material recognition that is not achievable with RGB or multispectral systems.
Built for Real-World Deployment
Specim RETEX is designed for integration into existing processes without requiring system redesign. The modular architecture supports deployment from laboratory environments to full-scale industrial systems. Early adopters are already using RETEX in production to improve material recognition and enable data-driven textile processing.
KEY CAPABILITIES
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High-accuracy identification of cotton, polyester, polyamide, viscose, wool, and acrylic
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Recognition of blends and material composition
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Elastane detection, including in previously unseen blends
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Detection of impurities and color recognition
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**Real-time processing for production environments
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Product Variants
Specim RETEX Core
AI classification engine for integration into custom systems.
Specim RETEX Lab
Hyperspectral textile analysis for R&D and validation.
Specim RETEX Modules
Hyperspectral and AI components for textile recognition and processing — designed for OEMs, integrators, and research use.
Specim RETEX System
Scalable industrial solution for textile processing and material separation.
Applications
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Material identification and separation
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Quality control
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Textile classification and analysis
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Circular textile processes
Performance
High-Accuracy Material Identification
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Up to 99% accuracy for cotton
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Up to 98% accuracy for polyester
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Up to 99% accuracy for polyamide
Recognizes blends (e.g., polycotton), wool, elastane, and other textile materials.
Covers over 95% of common textile materials.
Looking for
- Collaboration & Research Partners
- AI / Software Solutions & Integrators
- Industrial Demonstration & Use-Cases
- Customers
Applies to
- Industrial Automation & Quality Inspection
- Recycling & Circular Economy
Organisation
Similar opportunities
Service
Machine learning for hyperspectral imaging in agri-food applications
- Food, Pharma & Agriculture
- Data Analysis & AI Services
- Testing & Validation Services
Miguel Salvadó
Data scientist at ASINCAR
Oviedo (Asturias), Spain
Product
BlackIndustry Station - Ready-to-use hyperspectral imaging setup for fast data acquisition
- Customers
- Food, Pharma & Agriculture
- Recycling & Circular Economy
- Biomedical Imaging & Diagnostics
- Industrial Automation & Quality Inspection
Tobias Kreklow
CEO at HAIP Solutions GmbH
Hannover, Germany