点石成金？高光谱技术如何让废旧塑料纯度突破99%！

每年4月22日的世界地球日，都提醒着我们关注环境问题，而塑料污染无疑是其中一个棘手的挑战。大量的塑料垃圾难以有效回收，传统的分选流程难以实现塑料的高纯度分类，这不仅是资源的巨大浪费，也限制了再生塑料的应用价值，最终只能堆积或焚烧，对地球造成沉重负担。如何才能让这些废弃的塑料“变废为宝”，以更高的纯度回归生产循环，成为我们共同探索的方向。

传统的塑料分选方法，无论是人工操作还是视觉相机，都难以应对日益复杂的塑料混合物。想象一下，面对五花八门的塑料制品，想要一一辨别它们的“真身”并精确分类，无疑是一项艰巨的任务。然而，科技的进步正在打破这一瓶颈。

一种被誉为“材料成分鉴定官”的技术——高光谱成像，正在已更高的精度改变着塑料回收的格局。这项技术通过捕捉物体在连续光谱范围内的反射或辐射信息，构建一个包含丰富化学成分信息的“光谱指纹库”。它能够识别出不同塑料分子的吸收特征，实现对微小光谱差异的高度敏感识别，对可降解塑料也能精准区分，并且这一切都可以在产线上高速、无损地完成。

Every year, Earth Day on April 22nd serves as a crucial reminder to address environmental challenges, and plastic pollution stands out as a particularly thorny one. The sheer volume of plastic waste is difficult to recycle effectively. Traditional sorting methods struggle to achieve high purity levels, leading to significant resource waste and limiting the value of recycled plastic. Ultimately, much of this ends up in landfills or incinerators, placing a heavy burden on our planet. Finding ways to turn this discarded plastic into valuable resources, returning it to the production cycle with higher purity, is a shared goal we are actively pursuing.

Traditional plastic sorting methods, whether manual or standard visual cameras, are ill-equipped to handle increasingly complex plastic mixtures. Imagine trying to identify the "true identity" of a bewildering array of plastic products and sort them accurately – it's truly a tall order. However, technological advancements are breaking through this bottleneck.

Hyperspectral imaging, often dubbed a "material composition detective," is revolutionizing plastic recycling with unprecedented accuracy. This technology captures the reflectance or emission information of objects across a continuous spectral range, creating a "spectral fingerprint database" rich in chemical composition data. It can identify the absorption characteristics of different plastic molecules, enabling highly sensitive recognition of even subtle spectral differences. Biodegradable plastics can also be precisely distinguished. Crucially, all of this can be done at high speed and without damaging the material on a production line.

高光谱可以获得连续的光谱曲线

Hyperspectral imaging provides continuous spectral curves

(Upper left: grayscale image, Upper right: RGB image, Lower left: multispectral image, Lower right: hyperspectral image)

这项技术已经在多个应用场景中大显身手。例如，英国伦敦大学的研究人员采用近红外高光谱技术对不同尺寸、不同材料的塑料样本进行检测，包括可堆肥材料（甘蔗衍生和棕榈叶衍生）、可堆肥塑料（PLA、PBAT）和传统塑料（PP、PET和LDPE），重点关注950~1730nm波段，使用了主成分分析（PCA）和偏最小二乘判别分析（PLS-DA）。实验结果显示，对于尺寸大于10毫米×10毫米的样品，分类准确率达到100%，而对于较小碎片，准确率略有下降。该结果充分证明了高光谱技术在实际塑料分选中的高效性。

This technology has already proven its mettle in various applications. For instance, researchers at University College London in the UK utilized Near-Infrared Hyperspectral Imaging (NIR-HSI) to analyze plastic samples of different sizes and materials, including compostable materials (sugarcane- and palm leaf-derived), compostable plastics (PLA, PBAT), and conventional plastics (PP, PET, and LDPE). Focusing on the 950~1730nm range and employing Principal Component Analysis (PCA) and Partial Least Squares Discriminant Analysis (PLS-DA), their experiments showed a 100% classification accuracy for samples larger than 10mm x 10mm, with a slight decrease for smaller fragments. This clearly demonstrates the high efficiency of hyperspectral technology in practical plastic sorting.

通过高光谱相机获取的不同塑料的原始吸收光谱

Raw absorbance spectra of sugarcane derived packaging, PP, PLA, PET, LDPE, PBAT and palm leaf derived packaging acquired by hyperspectral camera

意大利的一个研究团队通过检测PET、PS和PLA的主要吸收峰（分别出现在1150nm至1660nm区间），成功区分了不同塑料类型。论文指出，这种方法能够定量评估分选过程的准确性，为工业应用提供了可靠依据。

An Italian research team successfully differentiated various plastic types, including PET, PS, and PLA, by detecting their main absorption peaks (occurring between 1150nm and 1660nm). Their paper highlights that this method allows for quantitative evaluation of sorting accuracy, providing a reliable basis for industrial applications.

PET样品光谱特征 / Spectral signatures of PET samples in the NIR region

PLA样品光谱特征 / Spectral signatures of PLA samples in the NIR region

PS样品光谱特征 / Spectral signatures of PS samples in the NIR region

国内的研究人员同样进行了分析，结合RGB和高光谱成像数据，开发了一种多尺度特征融合算法，实现了对透明PET、蓝色PET和透明PP瓶的高效辨识，整体分类准确率达到95.55%，而蓝色PET的准确率高达97.5%。这表明采用多传感器融合方法能够进一步提高分选系统的稳定性和准确率。

Chinses researchers have also contributed significantly. By analyzing combined RGB and hyperspectral imaging data, they developed a multi-scale feature fusion algorithm to achieve efficient identification of transparent PET, blue PET, and transparent PP bottles. This resulted in an overall classification accuracy of 95.55%, with blue PET reaching an impressive 97.5%. This work indicates that integrating multiple sensors can further enhance the stability and accuracy of sorting systems.

塑料瓶的平均光谱曲线 / Mean spectral curve of waste plastic bottles

杂乱瓶子的分类图。（a）RGB 图像。（b）基本实况。（c-h）分别预测了不同特征融合方法的分类图。

Classification maps for cluttered bottles. (a) RGB image. (b) Ground truth. (c-h) show the classification maps predicted by different feature fusion methods, respectively.

值得一提的是，我们也为塑料回收提供了成熟的高光谱解决方案。高光谱塑料识别系统集成到各类塑料分选机中，无论是针对整瓶还是碎片化塑料，都能通过数据接口将精准的识别结果反馈给控制系统，进而通过气阀实现自动化的高效分选。目前，工业高光谱相机已经推出，凭借其高帧频的特点，能够满足产线上快速、连续分选的要求。

更进一步，工程师利用900~1700nm近红外高光谱相机对土壤中的微塑料颗粒进行了识别研究，这为解决更复杂、更贴近实际环境的塑料回收提供了重要的实验基础和技术支持。

We also offer mature hyperspectral solutions for plastic recycling. Our hyperspectral plastic identification system can be integrated into various plastic sorting machines, whether for whole bottles or plastic flakes. Through data interfaces, precise identification results are relayed to the control system, enabling automated, high-efficiency sorting via air jets. Industrial hyperspectral cameras are now available, and their high frame rates are well-suited for fast, continuous sorting on production lines.

Furthermore, engineers have conducted research on identifying microplastic particles in soil using 900–1700nm near-infrared hyperspectral cameras. This provides a vital experimental foundation and technical support for tackling more complex, real-world plastic recycling challenges.

塑料分选实验装置及分选结果

Experimental setup and sorting results for plastic separation

综上所述，高光谱成像技术凭借其精准的材料识别能力和高效的在线检测特性，正为塑料回收行业注入新的活力。这项“材料成分鉴定官”不仅能够显著提升回收效率和材料纯度，更能为循环经济注入强劲动力，助力我们实现更绿色、更可持续的未来。让科技赋能回收，共同开启塑料“点石成金”的新篇章！

In conclusion, hyperspectral imaging technology, with its precise material identification capabilities and efficient online inspection features, is breathing new life into the plastic recycling industry. This "material composition detective" not only significantly boosts recycling efficiency and material purity but also injects strong momentum into the circular economy. It's a game-changer that helps us move towards a greener, more sustainable future. By empowering recycling with technology, we can truly turn trash into treasure and open a new chapter in plastic recycling.

案例来源 / Source：

1. Taneepanichskul N, Hailes HC and Miodownik M (2023) Automatic identification and classification of compostable and biodegradable plastics using hyperspectral imaging. Front. Sustain. 4:1125954.

2. Moroni, M.; Mei, A. Characterization and Separation of Traditional and Bio-Plastics by Hyperspectral Devices. Appl. Sci. 2020, 10, 2800.

3. Cai, Z.; Yang, J.; Fang, H.; Ji, T.; Hu, Y.; Wang, X. Research on Waste Plastics Classification Method Based on Multi-Scale Feature Fusion. Sensors 2022, 22, 7974.