Abstract：
Near-infrared (NIR) dyes have a unique ability to interact favorably with light in the NIR region, which is particularly interesting where stealth and camouflage are paramount, such as in military uniforms. Characterization of cotton fabric dyed with NIR-absorbing dyes using visible-NIR (Vis-NIR) and short-wave infrared (SWIR) hyperspectral imaging was done. The aim of the study was to discern spectral changes caused by variations in dye concentration and dyeing temperature as these parameters directly influence color- and crocking-fastness of fabrics impacting the camouflage effect. The fabric was dyed at three concentrations (2.5, 5, and 10%) and two dyeing temperatures (55 °C and 85 °C) and principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were performed on the spectra to discriminate the fabrics based on dye concentrations. The PCA models successfully segregated the fabrics based on the dye concentration and dyeing temperature, while PLS-DA models demonstrated classification accuracies between 75 and 100% in the Vis-NIR range. Spectra in the SWIR region could not be used to detect the differences in the concentrations of the NIR dyes. This finding is promising, as it aligns with the objective of creating NIR-dyed camouflage fabrics that remain indistinguishable under varying dye concentrations. These results open possibilities for further exploration in enhancing the stealth capabilities of textiles in military applications.
摘要：
近红外（NIR）染料具有与NIR区域中的光有利地相互作用的独特能力，这在隐身和伪装最重要的地方特别有趣，比如军装。使用可见NIR（Vis-NIR）和短波红外（SWIR）高光谱成像对NIR吸收染料染色的棉织物进行了表征。研究的目的是辨别染料浓度和染色温度变化引起的光谱变化，因为这些参数直接影响织物的颜色和摩擦牢度，从而影响伪装效果。在三种浓度（2.5、5和10％）和两种染色温度（55°C和85°C）下对织物进行染色，并对光谱进行主成分分析（PCA）和偏最小二乘判别分析（PLS-DA），以根据染料浓度区分织物。PCA模型根据染料浓度和染色温度成功地分离了织物，而PLS-DA模型在Vis-NIR范围内显示出75％至100％的分类精度。SWIR区域中的光谱不能用于检测NIR染料浓度的差异。这个发现很有希望,因为它与创建NIR染色伪装织物的目标一致，这些织物在不同的染料浓度下仍然无法区分。这些结果为进一步探索增强纺织品在军事应用中的隐身能力开辟了可能性。
