Over the past few years, laser-induced breakdown spectroscopy (LIBS) has earned a lot of attention in the field of online polymer identification. Unlike the well-established near-infrared spectroscopy (NIR), LIBS analysis is not limited by the sample thickness or color and therefore seems to be a promising candidate for this task. Nevertheless, the similar elemental composition of most polymers results in high similarity of their LIBS spectra, which makes their discrimination challenging. To address this problem, we developed a novel chemometric strategy based on a systematic optimization of two factors influencing the discrimination ability: the set of experimental conditions (laser energy, gate delay, and atmosphere) employed for the LIBS analysis and the set of spectral variables used as a basis for the polymer discrimination. In the process, a novel concept of spectral descriptors was used to extract chemically relevant information from the polymer spectra, cluster purity based on the k-nearest neighbors (k-NN) was established as a suitable tool for monitoring the extent of cluster overlaps and an in-house designed random forest (RDF) experiment combined with a cluster purity–governed forward selection algorithm was employed to identify spectral variables with the greatest relevance for polymer identification. Using this approach, it was possible to discriminate among 20 virgin polymer types, which is the highest number reported in the literature so far. Additionally, using the optimized experimental conditions and data evaluation, robust discrimination performance could be achieved even with polymer samples containing carbon black or other common additives, which hints at an applicability of the developed approach to real-life samples.

Graphical abstract

中文翻译：

---

通过激光诱导击穿光谱 (LIBS) 和化学计量学鉴定 20 种聚合物类型

在过去几年中，激光诱导击穿光谱（LIBS）在聚合物在线识别领域受到了广泛关注。与成熟的近红外光谱 (NIR) 不同，LIBS 分析不受样品厚度或颜色的限制，因此似乎是这项任务的有希望的候选者。然而，大多数聚合物的相似元素组成导致它们的 LIBS 光谱高度相似，这使得它们的区分具有挑战性。为了解决这个问题，我们开发了一种新的化学计量策略，基于对影响区分能力的两个因素的系统优化：用于 LIBS 分析的一组实验条件（激光能量、门延迟和大气）和一组光谱变量用作聚合物鉴别的基础。正在进行中，使用光谱描述符的新概念从聚合物光谱中提取化学相关信息，建立了基于 k 最近邻 (k-NN) 的簇纯度作为监测簇重叠程度的合适工具和内部设计的随机森林（RDF）实验与簇纯度控制的前向选择算法相结合，用于识别与聚合物识别最相关的光谱变量。使用这种方法，可以区分 20 种原始聚合物类型，这是迄今为止文献中报道的最高数量。此外，使用优化的实验条件和数据评估，即使使用含有炭黑或其他常见添加剂的聚合物样品，也可以实现强大的辨别性能，

图形概要