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

TL;DR

This paper presents a machine learning pipeline using a variational autoencoder and neural networks to transfer spectra between different LIBS systems sharing standards, enabling inter-laboratory compatibility.

Contribution

It introduces a novel spectral transfer method for LIBS systems differing only in spectrometers and optics, facilitating shared spectral libraries and reference measurements.

Findings

Effective spectral transfer demonstrated with low Euclidean and cosine distances.

Improved clustering accuracy in transferred spectra.

Method outperforms baseline approaches.

Abstract

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.