Leibniz Institute of Surface Engineering (IOM), Department of Functional Coatings, Permoserstr. 15, D-04318, Leipzig, Germany.
Leibniz Institute of Surface Engineering (IOM), Department of Functional Coatings, Permoserstr. 15, D-04318, Leipzig, Germany.
Talanta. 2021 Jan 1;221:121567. doi: 10.1016/j.talanta.2020.121567. Epub 2020 Sep 2.
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/m. 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.
光谱成像技术被用于大规模监测经水洗或浸染（水溶液或分散液）后整理的宽幅纺织品在干燥过程末期的残余水分。此类数据对于优化能源效率和精确控制干燥过程至关重要。记录的光谱数据采用多元回归方法（如偏最小二乘法（PLS）算法）进行定量分析。预测模型的校准参考数据通过重量分析法确定。研究了不同吸水性（棉、聚酰胺、聚酯）天然或合成纤维的纺织品材料的干燥情况，这些纤维部分用光学增亮剂进行了整理。校准模型中考虑了水分含量在 0 到约 12wt% 的范围。对于所有系统，残余水分的预测均方根误差（RMSEP）约为 0.5wt%，即约 1g/m。除了定量测定水分含量外，光谱成像还提供了有关其在整个纺织品幅宽上的空间分布的详细信息，这有助于改善干燥过程的控制。特别是，证明了所开发的方法能够检测和可视化不均匀的水分分布。对纺织品样本表面所有光谱的水分含量的个体值进行平均，与相应的重量法参考值非常吻合。由于平均过程，即使在水分分布不均匀的情况下，两者之间的差值通常也低于 RMSEP。所开发的分析方法在水分含量在线监测方面具有高精度和广泛的能力，有望为技术纺织品加工过程中的有效过程控制提供潜力。
