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