Rapid Identification of Beached Marine Plastics Pellets Using Laser-Induced Breakdown Spectroscopy: A Promising Tool for the Quantification of Coastal Pollution.

The rapid identification of beached marine micro-plastics is essential for the determination of the source of pollution and for planning the most effective strategies for remediation. In this paper, we present the results obtained by applying the laser-induced breakdown spectroscopy (LIBS) technique on a large sample of different kinds of plastics that can be found in a marine environment. The use of chemometric analytical tools allowed a rapid classification of the pellets with an accuracy greater than 80%. The LIBS spectrum and statistical tests proved their worth to quickly identify polymers, and in particular, to distinguish C-O from C-C backbone pellets, and PE from PP ones. In addition, the PCA analysis revealed a correlation between appearance (surface pellets roughness) and color (yellowing), as reported by other recent studies. The preliminary results on the analysis of metals accumulated on the surface of the pellets are also reported. The implication of these results is discussed in view of the possibility of frequent monitoring of the marine plastic pollution on the seacoast.

Exploiting Self-Absorption for Plasma Characterization in Laser-Induced Breakdown Spectroscopy Experiments: A Comparison of Two Recent Approaches.

In this article, we compare two analytical methods that have been recently proposed: the columnar density Saha-Boltzmann plot method of Cristoforetti and Tognoni (Cristoforetti, G.; Tognoni, E. Spectrochim. Acta, Part B, 2013, 79-80, 63-71) and the C-sigma model of Aragon and Aguilera (Aragon, C.; Aguilera, J. A. J. Quant. Spectrosc. Radiat. Trans. 2014, 149, 90-102). Both methods are based on the exploitation of self-absorbed lines for the characterization of plasmas in laser-induced breakdown spectroscopy experiments. However, although the two methods can be safely applied in many cases, their usefulness is limited in many practical cases of interest because of the intrinsic constraints of the used plasma model or because of the complexity of the numerical treatment. The two methods are presented here and critically discussed. Finally, an extended C-sigma approach is proposed to merge the advantages of the two methods, overcoming their intrinsic limitations and simplifying the numerical treatment.

Comparison of Convolutional and Conventional Artificial Neural Networks for Laser-Induced Breakdown Spectroscopy Quantitative Analysis.

The introduction of "deep learning" algorithms for feature identification in digital imaging has paved the way for artificial intelligence applications that up to a decade ago were considered technologically impossible to achieve, from the development of driverless vehicles to the fully automated diagnostics of cancer and other diseases from histological images. The success of deep learning applications has, in turn, attracted the attention of several researchers for the possible use of these methods in chemometrics, applied to the analysis of complex phenomena as, for example, the optical emission of laser-induced plasmas. In this paper, we will discuss the advantages and disadvantages of convolutional neural networks, one of the most diffused deep learning techniques, in laser-induced breakdown spectroscopy (LIBS) applications (classification and quantitative analysis), to understand the real potential of "deep LIBS" in practical everyday use. In particular, the comparison with the results obtained using "shallow" artificial neural networks will be presented and discussed, taking as a case study the analysis of six bronze samples of known composition.

Determination of Spectroscopic Parameters of Ag(I) and Ag(II) Emission Lines Using Time-Independent Extended C-Sigma Method.

The knowledge of the spectroscopic parameters of the elemental emission lines is important for diagnostics of laser-induced plasmas and the application of calibration-free/fundamental parameters analytical methods. In this paper, we used the recently proposed time-independent extended C-sigma method for determining, for the first time, the transition probabilities and Stark broadening coefficients of several neutral (TIECS) and ionic silver emission lines. The method allows for a compensation of self-absorption in the plasma, thus providing a measure of the spectroscopic parameters which is not affected by the optical thickness of the plasma.

Analytical and mathematical methods for revealing hidden details in ancient manuscripts and paintings: A review.

In this work, a critical review of the current nondestructive probing and image analysis approaches is presented, to revealing otherwise invisible or hardly discernible details in manuscripts and paintings relevant to cultural heritage and archaeology. Multispectral imaging, X-ray fluorescence, Laser-Induced Breakdown Spectroscopy, Raman spectroscopy and Thermography are considered, as techniques for acquiring images and spectral image sets; statistical methods for the analysis of these images are then discussed, including blind separation and false colour techniques. Several case studies are presented, with particular attention dedicated to the approaches that appear most promising for future applications. Some of the techniques described herein are likely to replace, in the near future, classical digital photography in the study of ancient manuscripts and paintings.

Micro-Laser-Induced Breakdown Spectroscopy (Micro-LIBS) Study on Ancient Roman Mortars.

The laser-induced breakdown spectroscopy (LIBS) technique was used for analyzing the composition of an ancient Roman mortar (5th century A.D.), exploiting an experimental setup which allows the determination of the compositions of binder and aggregate in few minutes, without the need for sample treatment. Four thousand LIBS spectra were acquired from an area of 10 mm2, with a 50 µm lateral resolution. The elements of interest in the mortar sample (H, C, O, Na, Mg, Al, Si, K, Ca, Ti, Mn, Fe) were detected and mapped. The collected data graphically shown as compositional images were interpreted using different statistical approaches for the determination of the chemical composition of the binder and aggregate fraction. The methods of false color imaging, blind separation, and self-organizing maps were applied and their results are discussed in this paper. In particular, the method based on the use of self-organizing maps gives well interpretable results in very short times, without any reduction in the dimensionality of the system.