Hyperspectral Imaging for Non-Destructive Detection of Chemical Residues on Textiles

Detecting chemical residues on surfaces is critical in environmental monitoring, industrial hygiene, public health, and incident management after chemical releases. Compounds such as acrylonitrile (ACN) and tetraethylguanidine (TEG), widely used in chemical processes, can pose risks upon residual exposure. Hyperspectral imaging (HSI), a high-resolution, non-destructive method, offers a secure and effective solution to identify and spatially map chemical contaminants based on spectral signatures. In this study, we present an HSI-based framework to detect and differentiate ACN and TEG residues on textile surfaces. High-resolution spectral data were collected from three representative textiles using a hyperspectral camera operating in the short-wave infrared range. The spectral datasets were processed using standard normal variate transformation, Savitzky–Golay filtering, and principal component analysis to enhance contrast and identify material-specific features. The results demonstrate the effectiveness of this approach in resolving spectral differences corresponding to distinct chemical residues and concentrations but also provide a practical and scalable method for detecting chemical contaminants in consumer and industrial textile materials, supporting reliable residue assessment and holding promise for broader applications in safety-critical fields.