VTT Technical Research Centre of Finland Ltd, PO Box 1000, 02044 VTT Espoo, Finland.
Aalto University, School of Chemical Engineering, Department of Bioproducts and Biosystems, PO Box 16300, 00076 Aalto, Finland.
Analyst. 2021 Dec 6;146(24):7503-7509. doi: 10.1039/d1an01794b.
Distinguishing different textile fibers is important for recycling waste textiles. Most studies on non-destructive optical textile identification have focused on classifying different synthetic and natural fibers but chemical recycling requires more detailed information on fiber composition and polymer properties. Here, we report the use of near infrared imaging spectroscopy and chemometrics for classifying natural and regenerated cellulose fibers. Our classifiers trained on images of consumer textiles showed 100% true positive rates based on model cross-validation and correctly identified on average 8-9 out of 10 test set pixels using images of specifically made cotton, viscose and lyocell samples of known compositions. These results are significant as they indicate the possibility to monitor and control fiber dosing and subsequent dope viscosity during chemical recycling of cellulose fibers. Our results also suggested the possibility to identify fibers purely based on polymer chain length. This finding opens the possibility to indirectly estimate dope viscosity and creates entirely new hypotheses for combining imaging spectroscopy with classification and regression methods within the broader field of cellulose modification.
鉴别不同的纺织纤维对于回收废旧纺织品很重要。大多数关于无损光学纺织识别的研究都集中在对不同的合成纤维和天然纤维进行分类上，但化学回收需要更详细的纤维成分和聚合物性能信息。在这里，我们报告了近红外成像光谱学和化学计量学在分类天然和再生纤维素纤维方面的应用。我们基于消费者纺织品图像训练的分类器在模型交叉验证中显示出 100%的真阳性率，并且使用特定制造的棉、粘胶和莱赛尔的已知成分的样本的图像，平均正确识别了测试集 10 个像素中的 8-9 个。这些结果意义重大，因为它们表明有可能在纤维素纤维的化学回收过程中监测和控制纤维剂量和随后的纺丝液粘度。我们的结果还表明，仅基于聚合物链长识别纤维的可能性。这一发现为间接估计纺丝液粘度开辟了可能性，并为将成像光谱学与分类和回归方法相结合创造了全新的假设，从而为纤维素改性这一更广泛的领域提供了新的思路。
