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The classification of plastics is very important in the recycling industry. A quick online classification allows the installation of the equipment in this line of work. Whether qualitative or quantitative analysis, the basic component of any laser-induced breakdown spectroscopy (LIBS) measurement is the emission spectrum recorder from single plasma. Each fire of the laser atomizes a portion of the sample in the pulse focal volume and produces plasma that excites and re-excites the atoms to emit light. The plasma light is collected and recorded in an ensuing measurement. In this sense, the LIBS technique offers all possible advantages: speed, the possibility of online analysis, nondestructive analysis, and so on. In this article, we discuss details related to the analysis of the emission spectrum. The plastics used in this study were low-density polyethylene (PE), high-density PE, polypro-pylene, polystyrene, and poly(ethylene terephthalate). Hierarchical cluster analysis was proven to be the best method because the four polymers could be divided into two clusters, which allowed their identification and classification in a fast and easy way that could be carried out with commercial software and could be implanted online in a recycling factory, as conventional data analysis techniques are limited to the qualitative identification and calculation of elemental abundances. Principal component analysis on LIBS spectra can be used to better describe the chemical variations in the samples and to extract a greater understanding of the chemical structure.

Laser-induced breakdown spectroscopy (LIBS) is an emerging analytical technique to perform elemental analysis in natural samples independent of their physical state (solid, liquid, or gaseous). Due to its instrumental features, LIBS shows promising potential to perform analysis in situ and in environments at risk. Since the analytical performance of LIBS strongly depends on the choice of experimental conditions, each particular application needs a specific instrumental adjustment. The present study evaluated three LIBS instrumental parameters regarding their influences on signal-to-noise ratio (SNR) of seven elements in soil samples: laser pulse energy, delay time, and integration time gate. A multivariate technique was used due to the significant interaction among the evaluated parameters. Subsequently, to optimize LIBS parameters for each individual element response, a method for multiple response optimization was used. With only one simple screening design, it was possible to obtain a good combination among the studied parameters in order to simultaneously increase the SNR for all analytes. Moreover, the analysis of individual response for elements is helpful to understand their physical behavior in the plasma and also how they are embedded in the sample matrix.

This work presents two rapid and effective methods for the identification of analgesic pills. LIBS spectra from pills in the 200-1,000-nm spectral window were compared with reference spectral libraries stored by computer. The libraries consisted of representative spectra from various groups of analgesic pills samples. The forms of emission spectra of paracetamol, ibuprofen, and acetylsalicylic acid were studied. Simple statistical correlation methods including linear correlation were used. Also LIBS spectra from pills in the 200-1,000-nm spectral window were analyzed to obtain the C:C 2 :H:N:O ratios. These ratios could be used to sort the three most used analgesic pills. Simple calibration curves with the studied ratios are sufficient to classify correctly the analgesic pills studied. These techniques can be useful for the identification of analgesic pills.

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