Multivariate classification of echellograms: a new perspective in Laser-Induced Breakdown Spectroscopy analysis

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.

Modeling of laser-induced breakdown spectroscopic data analysis by an automatic classifier

Laser-induced breakdown spectroscopy (LIBS) is a multi-elemental and real-time analytical technique with simultaneous detection of all the elements in any type of sample matrix including solid, liquid, gas, and aerosol. LIBS produces vast amount of data which contains information on elemental composition of the material among others. Classification and discrimination of spectra produced during the LIBS process are crucial to analyze the elements for both qualitative and quantitative analysis. This work reports the design and modeling of optimal classifier for LIBS data classification and discrimination using the apparatus of statistical theory of detection. We analyzed the noise sources associated during the LIBS process and created a linear model of an echelle spectrograph system. We validated our model based on assumptions through statistical analysis of "dark signal" and laser-induced breakdown spectra from the database of National Institute of Science and Technology. The results obtained from our model suggested that the quadratic classifier provides optimal performance if the spectroscopy signal and noise can be considered Gaussian.

Femtosecond laser induced breakdown spectroscopy based standoff detection of explosives and discrimination using principal component analysis

We report the standoff (up to ~2 m) and remote (~8.5 m) detection of novel high energy materials/explosive molecules (Nitroimidazoles and Nitropyrazoles) using the technique of femtosecond laser induced breakdown spectroscopy (LIBS). We utilized two different collection systems (a) ME-OCT-0007 (commercially available) and (b) Schmidt-Cassegrain telescope for these experiments. In conjunction with LIBS data, principal component analysis was employed to discriminate/classify the explosives and the obtained results in both configurations are compared. Different aspects influencing the LIBS signal strength at far distances such as fluence at target, efficiency of collection system etc. are discussed.