A spectroscopic dataset for known provenance and post-consumer textiles

Near infrared (NIR) spectroscopy is a rapid, non-invasive technique often used for chemical bond structure identification, making it a prime candidate for feedstock identification and validation for industrial processes involving polymers. It has rapidly gained popularity in the textile industry; however, the availability of high-quality, known provenance NIR data for textile fibers and fabrics is limited. Applying NIR to answer questions such as fiber classification or polymer blend identification typically requires the use of models or algorithms. The underpinning data for these models is typically in proprietary libraries or self-built databases; thus, benchmarking model performance across the industry is challenging. Here, a new dataset is presented to address this challenge. The dataset contains data on textile specimens for applications including fiber content classification, systems and software development, and validation of textile sorting systems. The data repository includes directories for benchtop NIR spectral data, handheld NIR spectral data, and fabric-scale microscopy images.