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

• URL: http://arxiv.org/abs/2209.07637v1
• Date: Wed, 31 Aug 2022 16:15:55 GMT
• Title: Library transfer between distinct Laser-Induced Breakdown Spectroscopy systems with shared standards
• Authors: J. Vr\'abel (1 and 2), E. K\'epe\v{s} (1 and 2), P. Ned\v{e}ln\'ik (1), J. Buday (1 and 2), J. Cepm\'irek (3), P. Po\v{r}\'izka (1 and 2), J. Kaiser (1 and 2) ((1) CEITEC, Brno University of Technology, (2) Institute of Physical Engineering, Brno University of Technology, (3) Department of Geological Sciences, Faculty of Science, Masaryk University)
• Abstract summary: The cost related to setting up a new LIBS system is increased, as its extensive calibration is required. 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. The transfer is realized by a pipeline that consists of a variational autoencoder (VAE) and a fully-connected artificial neural network (ANN)
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Spectral Graph Reasoning Network for Hyperspectral Image Classification [0.43512163406551996]
  Convolutional neural networks (CNNs) have achieved remarkable performance in hyperspectral image (HSI) classification. We propose a spectral graph reasoning network (SGR) learning framework comprising two crucial modules. Experiments on two HSI datasets demonstrate that the proposed architecture can significantly improve the classification accuracy.
  arXiv  Detail & Related papers  (2024-07-02T20:29:23Z)
• ESSAformer: Efficient Transformer for Hyperspectral Image Super-resolution [76.7408734079706]
  Single hyperspectral image super-resolution (single-HSI-SR) aims to restore a high-resolution hyperspectral image from a low-resolution observation. We propose ESSAformer, an ESSA attention-embedded Transformer network for single-HSI-SR with an iterative refining structure.
  arXiv  Detail & Related papers  (2023-07-26T07:45:14Z)
• MST++: Multi-stage Spectral-wise Transformer for Efficient Spectral Reconstruction [148.26195175240923]
  We propose a novel Transformer-based method, Multi-stage Spectral-wise Transformer (MST++) for efficient spectral reconstruction. In the NTIRE 2022 Spectral Reconstruction Challenge, our approach won the First place.
  arXiv  Detail & Related papers  (2022-04-17T02:39:32Z)
• Unsupervised Machine Learning for Exploratory Data Analysis of Exoplanet Transmission Spectra [68.8204255655161]
  We focus on unsupervised techniques for analyzing spectral data from transiting exoplanets. We show that there is a high degree of correlation in the spectral data, which calls for appropriate low-dimensional representations. We uncover interesting structures in the principal component basis, namely, well-defined branches corresponding to different chemical regimes.
  arXiv  Detail & Related papers  (2022-01-07T22:26:33Z)
• Unsupervised Spectral Unmixing For Telluric Correction Using A Neural Network Autoencoder [58.720142291102135]
  We present a neural network autoencoder approach for extracting a telluric transmission spectrum from a large set of high-precision observed solar spectra from the HARPS-N radial velocity spectrograph.
  arXiv  Detail & Related papers  (2021-11-17T12:54:48Z)
• Mask-guided Spectral-wise Transformer for Efficient Hyperspectral Image Reconstruction [127.20208645280438]
  Hyperspectral image (HSI) reconstruction aims to recover the 3D spatial-spectral signal from a 2D measurement. Modeling the inter-spectra interactions is beneficial for HSI reconstruction. Mask-guided Spectral-wise Transformer (MST) proposes a novel framework for HSI reconstruction.
  arXiv  Detail & Related papers  (2021-11-15T16:59:48Z)
• Deep Spectral CNN for Laser Induced Breakdown Spectroscopy [11.978306421559587]
  This work proposes a spectral convolutional neural network (CNN) operating on laser induced breakdown spectroscopy (LIBS) signals to learn to disentangle spectral signals from the sources of sensor uncertainty. Our experiments demonstrate that the proposed method outperforms the existing approaches used by the Mars Science Lab for pre-processing and calibration for remote sensing observations from the Mars rover, 'Curiosity'
  arXiv  Detail & Related papers  (2020-12-03T02:23:48Z)
• Neural network-based on-chip spectroscopy using a scalable plasmonic encoder [0.4397520291340694]
  Conventional spectrometers are limited by trade-offs set by size, cost, signal-to-noise ratio (SNR), and spectral resolution. Here, we demonstrate a deep learning-based spectral reconstruction framework using a compact and low-cost on-chip sensing scheme.
  arXiv  Detail & Related papers  (2020-12-01T22:50:06Z)
• A Comparative study of Artificial Neural Networks Using Reinforcement learning and Multidimensional Bayesian Classification Using Parzen Density Estimation for Identification of GC-EIMS Spectra of Partially Methylated Alditol Acetates [0.304585143845864]
  This study reports the development of a pattern recognition search engine for a World Wide Web-based database of gas chromatography-electron impact mass spectra (GC-EIMS) of partially methylated Alditol acetates (PMAAs) The developed system is implemented on the world wide web, and is intended to identify PMAAs using submitted spectra of these molecules recorded on any GC-EIMS instrument.
  arXiv  Detail & Related papers  (2020-07-31T17:54:51Z)
• Deep Neural Networks for the Correction of Mie Scattering in Fourier-Transformed Infrared Spectra of Biological Samples [0.0]
  We propose an approach to approximate this complex preprocessing function using deep neural networks. Our proposed method overcomes the trade-off between time and the corrected spectrum being biased towards an artificial reference spectrum.
  arXiv  Detail & Related papers  (2020-02-18T16:07:07Z)
• Spectrum Translation for Cross-Spectral Ocular Matching [59.17685450892182]
  Cross-spectral verification remains a big issue in biometrics, especially for the ocular area. We investigate the use of Conditional Adversarial Networks for spectrum translation between near infra-red and visual light images for ocular biometrics.
  arXiv  Detail & Related papers  (2020-02-14T19:30:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.