With the increase in waste streams, industrial sorting has become a major issue. The main challenge is to minimise sorting errors to avoid serious recycling problems and significant quality degradation of the final recycled product. Making use of near infrared (NIR) technology, some industrialists have already designed sorting machines able to discriminate between several types of plastics with good reliability. However, these devices are not suited to dark plastics, which are very common in WEEE (Waste Electronic and Electrical Equipment). In order to overcome this obstacle, mid-wavelength infrared (MIR) technology can be used instead of NIR. Nevertheless, the new spectral range is poorer in terms of wavelength for some plastics of interest ($2712-5274nm$), which makes the sorting task harder in an industrial context where spectrum identification is subject to imprecision and uncertainty. This article shows the benefit of combining this promising optical technology with a cautious machine learning procedure to optimise recycling. When the information provided by the device regarding a plastic fragment to be sorted is insufficient to discriminate between candidate materials, the proposed procedure, taking advantage of the belief functions theory, blows the fragment into a container dedicated to more than one specific material. This cautious sorting enables the containers dedicated to the specific materials to contain less impurities, which leads to higher-quality secondary raw materials. The proposed sorting procedure is illustrated and compared with a more conventional approach using real industrial data.

随着废物流的增加，工业分类已经成为主要问题。主要挑战是最大程度地减少分类错误，以避免严重的回收问题和最终回收产品的明显质量下降。利用近红外（NIR）技术，一些工业家已经设计出了分选机，能够以良好的可靠性区分几种塑料。但是，这些设备不适用于深色塑料，而深色塑料在WEEE（废弃电子电气设备）中非常常见。为了克服这一障碍，可以使用中波长红外（MIR）技术代替NIR。不过，对于某些感兴趣的塑料，新光谱范围的波长范围较差（$2712-5274ñ米$），这在频谱识别受不精确和不确定性影响的工业环境中使分拣任务更加困难。本文显示了将这种有前途的光学技术与谨慎的机器学习程序相结合以优化回收利用的好处。当设备提供的有关待分类塑料碎片的信息不足以区分候选物料时，建议的程序将利用信念函数理论将碎片吹入专用于一种以上特定物料的容器中。这种谨慎的分类可以使专用于特定材料的容器包含更少的杂质，从而提高了次级原料的质量。