Abstract
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
Highlights
Since the boom of their production in the 1950s, polymers have significantly increased the quality of human life by greatly expanding the availability of everyday products on the market and facilitating innovation in diverse areas of life, such as health care, food safety, electronics, transport, and aerospace [1]
The present work demonstrates the possibility of using laser-induced breakdown spectroscopy (LIBS) for the identification of 20 virgin polymer types, which is, to the best of our knowledge, the highest number reported in the literature so far
The problem of the extensive cluster overlaps related to the high spectral similarity of polymers could be resolved by a two-step optimization of the cluster purity based on the k-nearest neighbors (k-NN) algorithm
Summary
Since the boom of their production in the 1950s, polymers have significantly increased the quality of human life by greatly expanding the availability of everyday products on the market and facilitating innovation in diverse areas of life, such as health care, food safety, electronics, transport, and aerospace [1]. With only about 9% of all plastics recycled and 12% incinerated, the vast majority of the plastics ever produced ended up in landfills or the natural environment [2]. The most viable route for plastic recovery is the physical re-processing of the plastic waste into granulates or new products known as mechanical recycling [8]. As the quality of the resulting recyclates highly depends on the purity of the plastic fractions, a thorough identification and sorting of the incoming waste is required [9]. While manual sorting was the only available option in the past, development of a near-infrared (NIR) technology enabled its automatization resulting in lower recycling costs, higher accuracies, and greater amounts of plastics recycled. There is a need for a method which could fill these gaps and enable the current recycling rates to increase
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