In-line monitoring of the thickness distribution of adhesive layers in black textile laminates by hyperspectral imaging

Prediction of Honeydew Contaminations on Cotton Samples by In-Line UV Hyperspectral Imaging

UV hyperspectral imaging (225 nm-410 nm) was used to identify and quantify the honeydew content of real cotton samples. Honeydew contamination causes losses of millions of dollars annually. This study presents the implementation and application of UV hyperspectral imaging as a non-destructive, high-resolution, and fast imaging modality. For this novel approach, a reference sample set, which consists of sugar and protein solutions that were adapted to honeydew, was set-up. In total, 21 samples with different amounts of added sugars/proteins were measured to calculate multivariate models at each pixel of a hyperspectral image to predict and classify the amount of sugar and honeydew. The principal component analysis models (PCA) enabled a general differentiation between different concentrations of sugar and honeydew. A partial least squares regression (PLS-R) model was built based on the cotton samples soaked in different sugar and protein concentrations. The result showed a reliable performance with R²_cv = 0.80 and low RMSECV = 0.01 g for the validation. The PLS-R reference model was able to predict the honeydew content laterally resolved in grams on real cotton samples for each pixel with light, strong, and very strong honeydew contaminations. Therefore, inline UV hyperspectral imaging combined with chemometric models can be an effective tool in the future for the quality control of industrial processing of cotton fibers.

Near-infrared hyperspectral imaging for monitoring the thickness distribution of thin poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS) layers

The thickness of thin layers of the conductive polymer PEDOT:PSS in the range between about 60 and 300 nm was determined by a near-infrared spectroscopic method using a hyperspectral camera. The reflection spectra of the layers do not contain bands, but consist of a moderate slope of the overall reflectance in the range between 1320 and 1850 nm. Despite the low thickness, the spectra show an extremely strong dependence on the thickness of the layers, which allows their use for quantitative measurements. The prediction of quantitative thickness data from the reflection spectra was based on a chemometric approach using the partial least squares (PLS) algorithm. Calibration was carried out by means of spin-coated layers of PEDOT:PSS, whose thickness was determined by white-light interferometry and stylus profilometry. Finally, this resulted in a calibration model with a root mean square error of prediction (RMSEP) of about 9 nm. After external validation of this model, it was used for quantitative imaging of the thickness distribution in PEDOT:PSS layers. The precision of the predicted values was confirmed by comparison with data from the reference methods. Moreover, it was shown that this approach can be also used for hyperspectral imaging of the thickness of thin printed layers and structures of this conductive polymer on polymer film or paper with excellent thickness resolution. This analytical approach opens new possibilities for in-line process control by large-scale monitoring of thickness and homogeneity of thin layers of conductive polymers.

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

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.