Al Ktash Mohammad, Knoblich Mona, Eberle Max, Wackenhut Frank, Brecht Marc
Process Analysis and Technology PA & T, Reutlingen University, Alteburgstraße 150, 72762 Reutlingen, Germany.
Institute of Physical and Theoretical Chemistry, Eberhard Karls University Tübingen, Auf der Morgenstelle 18, 72076 Tübingen, Germany.
J Imaging. 2024 Dec 5;10(12):310. doi: 10.3390/jimaging10120310.
Ultraviolet (UV) hyperspectral imaging shows significant promise for the classification and quality assessment of raw cotton, a key material in the textile industry. This study evaluates the efficacy of UV hyperspectral imaging (225-408 nm) using two different light sources: xenon arc (XBO) and deuterium lamps, in comparison to NIR hyperspectral imaging. The aim is to determine which light source provides better differentiation between cotton types in UV hyperspectral imaging, as each interacts differently with the materials, potentially affecting imaging quality and classification accuracy. Principal component analysis (PCA) and Quadratic Discriminant Analysis (QDA) were employed to differentiate between various cotton types and hemp plant. PCA for the XBO illumination revealed that the first three principal components (PCs) accounted for 94.8% of the total variance: PC1 (78.4%) and PC2 (11.6%) clustered the samples into four main groups-hemp (HP), recycled cotton (RcC), and organic cotton (OC) from the other cotton samples-while PC3 (6%) further separated RcC. When using the deuterium light source, the first three PCs explained 89.4% of the variance, effectively distinguishing sample types such as HP, RcC, and OC from the remaining samples, with PC3 clearly separating RcC. When combining the PCA scores with QDA, the classification accuracy reached 76.1% for the XBO light source and 85.1% for the deuterium light source. Furthermore, a deep learning technique called a fully connected neural network for classification was applied. The classification accuracy for the XBO and deuterium light sources reached 83.6% and 90.1%, respectively. The results highlight the ability of this method to differentiate conventional and organic cotton, as well as hemp, and to identify distinct types of recycled cotton, suggesting varying recycling processes and possible common origins with raw cotton. These findings underscore the potential of UV hyperspectral imaging, coupled with chemometric models, as a powerful tool for enhancing cotton classification accuracy in the textile industry.
紫外线（UV）高光谱成像在原棉的分类和质量评估方面显示出巨大潜力，原棉是纺织工业中的关键材料。本研究评估了使用两种不同光源（氙弧灯（XBO）和氘灯）的紫外线高光谱成像（225 - 408纳米）与近红外高光谱成像相比的效果。目的是确定哪种光源在紫外线高光谱成像中能更好地区分棉花类型，因为每种光源与材料的相互作用不同，可能会影响成像质量和分类准确性。主成分分析（PCA）和二次判别分析（QDA）被用于区分各种棉花类型和大麻植株。XBO照明的PCA结果显示，前三个主成分（PC）占总方差的94.8%：PC1（78.4%）和PC2（11.6%）将样本聚为四个主要组——大麻（HP）、回收棉（RcC）以及与其他棉花样本区分开的有机棉（OC），而PC3（6%）进一步将RcC分开。使用氘光源时，前三个PC解释了89.4%的方差，有效区分了HP、RcC和OC等样本类型与其余样本，PC3清晰地将RcC分开。当将PCA分数与QDA结合时，XBO光源的分类准确率达到76.1%，氘光源的分类准确率达到85.1%。此外，还应用了一种名为全连接神经网络分类的深度学习技术。XBO和氘光源的分类准确率分别达到83.6%和90.1%。结果突出了该方法区分传统棉花和有机棉花以及大麻的能力，以及识别不同类型回收棉的能力，表明了不同的回收过程以及与原棉可能的共同来源。这些发现强调了紫外线高光谱成像与化学计量模型相结合作为提高纺织工业中棉花分类准确性的强大工具的潜力。
