Global textile production is mainly based on polyester and cotton fibers. A majority of textiles at the end of their lifecycle are currently landfilled or incinerated, but will be increasingly recycled in the future. Here, we discuss how the polyester content in blended textiles can be estimated based on hyperspectral near infrared imaging with the aim of developing machine vision for textile characterization and recycling. Differences in the textile samples were first visualized based on a principal component model and the polyester contents of individual image pixels were then predicted using image regression. The results showed average prediction errors of 2.2-4.5% within a range of 0-100% polyester and enabled visualizing the spatial changes in the polyester contents of the textiles. We foresee that digitalized tools similar to what we report here will be increasingly important in the future as more emphasis is placed on coordinated collection, sorting and reuse of waste textiles.

全球纺织品生产主要基于聚酯和棉纤维。目前，大多数纺织品在其生命周期结束时都被填埋或焚化，但将来将越来越多地被回收利用。在这里，我们讨论如何基于高光谱近红外成像估计混合纺织品中的聚酯含量，目的是开发用于机器表征和回收的机器视觉。首先基于主成分模型显示纺织品样品中的差异，然后使用图像回归预测单个图像像素的聚酯含量。结果显示，在0-100％的聚酯范围内，平均预测误差为2.2-4.5％，并且可以直观地看到纺织品中聚酯含量的空间变化。