Advancing recycling efficiency through hyperspectral identification of fabric blends

Tristan Guay; Marie-Christine Ferland; François-René Lachapelle

doi:10.1117/12.3002945

11 March 2024 Advancing recycling efficiency through hyperspectral identification of fabric blends

Tristan Guay, Marie-Christine Ferland, François-René Lachapelle

Proceedings Volume 12893, Photonic Instrumentation Engineering XI; 128930P (2024) https://doi.org/10.1117/12.3002945
Event: SPIE OPTO, 2024, San Francisco, California, United States

Abstract

Textile sorting for recycling and revalorisation is a multifaceted challenge that requires accurate material classification. We demonstrate the effectiveness of short-wave infrared (SWIR) hyperspectral imaging as a method employed to address this challenge. The utilization of various data processing strategies enables us to ascertain the accuracy of textile blends. Employing the Multivariate Curve Resolution-Alternating Least Square algorithm, we establish an uncertainty range of ±2.7 - 5.0% using pure elements as a training set. To achieve this, we employ multiple pre-processing methods to enhance the spectrum and assess alternative regression algorithms, such as Multivariate Regression-Partial Least Square and Principal Component Algorithm. Additionally, we conducted tests using two hyperspectral systems with distinct spectral ranges: one extending up to 2500 nm and the other up to 1700 nm. Furthermore, a study on the influence of fabric color on regression and textile spectra was conducted.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Tristan Guay, Marie-Christine Ferland, and François-René Lachapelle "Advancing recycling efficiency through hyperspectral identification of fabric blends", Proc. SPIE 12893, Photonic Instrumentation Engineering XI, 128930P (11 March 2024); https://doi.org/10.1117/12.3002945

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