In the increasingly pressing context of improving recycling, optical technologies present a broad potential to support the adequate sorting of plastics. Nevertheless, the commercially available solutions (for example, employing near-infrared spectroscopy) generally focus on identifying mono-materials of a few selected types which currently have a market-interest as secondary materials. Current progress in photonic sciences together with advanced data analysis, such as artificial intelligence, enable bridging practical challenges previously not feasible, for example in terms of classifying more complex materials. In the present paper, the different techniques are initially reviewed based on their main characteristics. Then, based on academic literature, their suitability for monitoring the composition of multi-materials, such as different types of multi-layered packaging and fibre-reinforced polymer composites as well as black plastics used in the motor vehicle industry, is discussed. Finally, some commercial systems with applications in those sectors are also presented. This review mainly focuses on the materials identification step (taking place after waste collection and before sorting and reprocessing) but in outlook, further insights on sorting are given as well as future prospects which can contribute to increasing the circularity of the plastic composites’ value chains.

在改善回收利用的日益紧迫的背景下，光学技术在支持塑料的充分分类方面具有广阔的潜力。然而，商业上可用的解决方案（例如，采用近红外光谱）通常侧重于识别几种选定类型的单一材料，这些材料目前作为二次材料具有市场兴趣。当前光子科学的进展与人工智能等先进的数据分析相结合，能够克服以前无法实现的实际挑战，例如在对更复杂的材料进行分类方面。在本文中，根据不同技术的主要特点对它们进行了初步综述。然后，根据学术文献，讨论了它们监测多种材料成分的适用性，例如不同类型的多层包装和纤维增强聚合物复合材料以及汽车行业使用的黑色塑料。最后，还介绍了一些在这些领域应用的商业系统。本综述主要关注材料识别步骤（在废物收集之后、分类和再加工之前进行），但在展望中，给出了对分类的进一步见解以及未来的前景，这有助于提高塑料复合材料价值链的循环性。