Classification of metals by means of Laser-induced Breakdown Spectroscopy and chemometric methods

Táto diplomová práca sa zaoberá klasifikáciou kovov pomocou spektroskopie laserom indukovanej plazmy (LIBS) a chemometrických metód. Práca poskytuje prehľad o štúdiách na danú tému. Sú vybrané tri široko používané chemometrické klasifikačné metódy: "Soft Independent Modeling of Class Analogy" (SIMCA), "Partial Least Squares Discriminant Analysis" (PLS-DA) a variácia umelých neurónových sietí (ANN), "Feedforward Multilayer Perceptron". Rôzne prístupy k prieskumovej analýze su tiež preskúmané. Metódy sú stručne opísané. Následne sú klasifikátory experimentálne porovnané.This thesis deals with the classification of metals by means of laser-induced breakdown spectroscopy (LIBS) and chemometric methods. The work gives a review of the studies reported on the subject. Three widely used chemometric classification methods are selected: Soft Independent Modelling of Class Analogy (SIMCA), Partial Least Squares Discriminant Analysis (PLS-DA) and a variation of Artificial Neural Networks (ANN), the Feedforward Multilayer Perceptron. Several approaches to exploratory data analysis are also considered. The methods are described, briefly stating their working principle. Subsequently, the performance of the classifiers is experimentally assessed, using several figures of merit.

Enhancement of detection limits in Laser-Induced Breakdown Spectroscopy (LIBS) using nanoparticles

Bakalárska práca popisuje možnosti zlepšovania detekčných limít metódy spektroskopie laserom indukovanej plazmy (LIBS). Zhŕňa modifikácie klasickej aparatúry, ktoré využívajú metódy double-pulsed LIBS (DPLIBS), Townsend effect plasma sectroscopy (TEPS), resonance enhanced LIBS (RELIBS), spark discharge LIBS (SDLIBS), flame-enhanced LIBS (FELIBS) aj nové postupy pri príprave vzoriek, ktoré používa metóda nanoparticle enhanced LIBS (NELIBS). Popisuje mechanizmy, ktoré využívajú jednotlivé metódy k zníženiu detekčných limít a obsahuje prehľad dosiahnutých zlepšení oproti klasickej metóde LIBS. Podrobnejšie sa zaoberá najnovšou metódou nanoparticle enhanced LIBS a experimentálne overuje a skúma vplyv nanočastíc rôznych typov a veľkostí na intenzitu emisného spektra metódy LIBS.This bachelor's thesis describes the options of ehnancing the detection limits of laser-induced breakdown spectroscopy (LIBS). It summarizes different modifications of the classical LIBS apparatus, which are used by methods double-pulsed LIBS (DPLIBS), Townsend effect plasma sectroscopy (TEPS), resonance enhanced LIBS (RELIBS), spark discharge LIBS (SDLIBS), flame-enhanced LIBS (FELIBS), and new ways of sample preparation, such as are used in the method nanoparticle enhanced LIBS (NELIBS). It briefly describes the mechanisms, which are used by each method to reduce the detection limit and it contains an overview of obtained enhancements against the classical method LIBS. It deals with the method nanoparticle enhanced LIBS in more detail and experimentally verifies and studies the effects of nanoparticles of different types and sizes on the emission spectrum of the method LIBS.

Library transfer between distinct Laser-Induced Breakdown Spectroscopy systems with shared standards

The mutual incompatibility of distinct spectroscopic systems is among the most limiting factors in Laser-Induced Breakdown Spectroscopy (LIBS). The cost related to setting up a new LIBS system is increased, as its extensive calibration is required. Solving the problem would enable inter-laboratory reference measurements and shared spectral libraries, which are fundamental for other spectroscopic techniques. In this work, we study a simplified version of this challenge where LIBS systems differ only in used spectrometers and collection optics but share all other parts of the apparatus, and collect spectra simultaneously from the same plasma plume. Extensive datasets measured as hyperspectral images of heterogeneous specimens are used to train machine learning models that can transfer spectra between systems. The transfer is realized by a pipeline that consists of a variational autoencoder (VAE) and a fully-connected artificial neural network (ANN). In the first step, we obtain a latent representation of the spectra which were measured on the Primary system (by using the VAE). In the second step, we map spectra from the Secondary system to corresponding locations in the latent space (by the ANN). Finally, Secondary system spectra are reconstructed from the latent space to the space of the Primary system. The transfer is evaluated by several figures of merit (Euclidean and cosine distances, both spatially resolved; k-means clustering of transferred spectra). The methodology is compared to several baseline approaches.Comment: 32 pages, 22 figure

Development of a device and methodology for Laser-Induced Breakdown Spectroscopy (LIBS)

Táto práca sa zaoberá prenosom analytických modelov medzi rôznymi systémami spektroskopie laserom indukovanej plazmy (LIBS) a porovnaním LIBS výsledkov získaných na rôznych systémoch. Instrumentácia LIBS aj spracovanie LIBS spektier sú vysoko flexibilné. Bohužiaľ, kvôli týmto flexibilitám sú výsledky získané na jednom LIBS systéme zriedka priamo porovnateľné s výsledkami získanými na inom systéme. Toto je ďalej komplikované rôznymi, často neznámymi, účinkami algoritmov spracovania LIBS spektier. V dôsledku toho sú modely analýzy spravidla špecifické pre systém (a parametre). Prenos analytických modelov medzi rôznými systémami by viedol k významnému zlepšeniu analytických schopností metódy LIBS a k miernemu zníženiu nákladov v priemyselných aplikáciách LIBS. Práca skúma vplyv rôznych stratégií merania metódou LIBS. Naďalej, práca skúma transformáciu získaných LIBS spektier prostredníctvom spracovávania údajov. Práca sa napokon zaoberá prenosom analytických modelov medzi rôznymi LIBS systémami

Development of a device and methodology for Laser-Induced Breakdown Spectroscopy (LIBS)

Táto práca sa zaoberá prenosom analytických modelov medzi rôznymi systémami spektroskopie laserom indukovanej plazmy (LIBS) a porovnaním LIBS výsledkov získaných na rôznych systémoch. Instrumentácia LIBS aj spracovanie LIBS spektier sú vysoko flexibilné. Bohužiaľ, kvôli týmto flexibilitám sú výsledky získané na jednom LIBS systéme zriedka priamo porovnateľné s výsledkami získanými na inom systéme. Toto je ďalej komplikované rôznymi, často neznámymi, účinkami algoritmov spracovania LIBS spektier. V dôsledku toho sú modely analýzy spravidla špecifické pre systém (a parametre). Prenos analytických modelov medzi rôznými systémami by viedol k významnému zlepšeniu analytických schopností metódy LIBS a k miernemu zníženiu nákladov v priemyselných aplikáciách LIBS. Práca skúma vplyv rôznych stratégií merania metódou LIBS. Naďalej, práca skúma transformáciu získaných LIBS spektier prostredníctvom spracovávania údajov. Práca sa napokon zaoberá prenosom analytických modelov medzi rôznymi LIBS systémami.This work deals with the transfer of analysis models across various laser-induced breakdown spectroscopy (LIBS) systems and the comparison of LIBS measurements obtained on distinct systems. Both the LIBS instrumentation and the data processing applied to LIBS data are highly flexible. Unfortunately, due to these flexibilities, results obtained on one LIBS system are rarely directly comparable to results obtained by a different system. This is further complicated by the various, often unknown, impact of the wide range of data processing algorithms on the LIBS data. Consequently, analysis models are generally system (and parameter) specific. The transfer of analysis models across various systems would result in the significant enhancement of the analytical capabilities of LIBS and in moderate cost reductions in industrial LIBS applications. The work studies the impact of various data collection strategies on LIBS data. Moreover, the work investigates the transformation of the acquired LIBS data through data processing. Lastly, the work addresses the transfer of analysis models across various LIBS systems.

ANN-LIBS analysis of mixture plasmas: detection of xenon

We developed an artificial neural network method for characterising crucial physical plasma parameters (i.e., temperature, electron density) in a fast and precise manner that mitigates common issues arising in evaluation of LIBS spectra.</jats:p