Monitoring of the residual moisture content in finished textiles during converting by NIR hyperspectral imaging

Olesya Daikos; Tom Scherzer

doi:10.1016/j.talanta.2020.121567

Monitoring of the residual moisture content in finished textiles during converting by NIR hyperspectral imaging

Talanta. 2021 Jan 1:221:121567. doi: 10.1016/j.talanta.2020.121567. Epub 2020 Sep 2.

Authors

Olesya Daikos¹, Tom Scherzer²

Affiliations

¹ Leibniz Institute of Surface Engineering (IOM), Department of Functional Coatings, Permoserstr. 15, D-04318, Leipzig, Germany.
² Leibniz Institute of Surface Engineering (IOM), Department of Functional Coatings, Permoserstr. 15, D-04318, Leipzig, Germany. Electronic address: tom.scherzer@iom-leipzig.de.

PMID: 33076115
DOI: 10.1016/j.talanta.2020.121567

Abstract

Hyperspectral imaging was used for large-scale monitoring of the residual moisture in wide textile webs at the end of the drying process that follows their washing or finishing by impregnation in aqueous solutions or dispersions. Such data are essential for optimizing the energy efficiency and the precise control of the drying process. Quantitative analysis of the recorded spectral data was carried out with multivariate regression methods such as the partial least squares (PLS) algorithm. Reference data for calibration of the prediction models were determined by gravimetry. The drying of textile materials from both natural or synthetic fibers possessing different water absorption capacities (cotton, polyamide, polyester), which were partially finished with an optical brightener, was investigated. Moisture contents in the range from 0 to about 12 wt% were considered in the calibration models. For all systems, the root mean square error of prediction (RMSEP) for the residual moisture was found to be about 0.5 wt%, that is, about 1 g/m². In addition to the quantitative determination of the water content, hyperspectral imaging provides detailed information about its spatial distribution across the textile web, which may help to improve the control of the drying process. In particular, it was demonstrated that the developed methods were capable of detecting and visualizing inhomogeneous moisture distributions. Averaging of the individual values of the moisture content predicted from all spectra across the surface of the textile samples resulted in a very close correlation with the corresponding gravimetric reference values. Due to the averaging process, the difference between both values is generally lower than RMSEP even in case of samples with inhomogeneous distribution of the moisture. The high precision and the broad capabilities of the developed analytic methods for in-line monitoring of the moisture content hold the potential for an efficient process control in technical textile converting processes.

Keywords: Drying process; Hyperspectral imaging; Impregnation; In-line monitoring; Moisture; Process control.