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Applications of IR Spectroscopy: Overview01:11

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The non-destructive nature and ability to provide valuable chemical information make IR spectroscopy a versatile technique with broad applications in various scientific and industrial fields. IR spectroscopy is commonly used to identify and characterize organic and inorganic compounds. It provides information about the functional groups present in a molecule and the bonding between atoms. This helps in the structural elucidation of compounds during organic synthesis, pharmaceutical research,...
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In atomic emission spectroscopy (AES), high-temperature atomizers excite a broad range of elements and molecules that generate complex emissions from sources such as oxides, hydroxides, and flame combustion products in the flame or plasma. Several strategies can be employed to minimize spectral interferences caused by overlapping emission lines or bands. These include increasing instrument resolution, choosing alternative emission lines, optimally placing the detector in low-background regions,...
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When electromagnetic radiation passes through a material, atoms or molecules transition from a lower to a higher energy state by absorbing radiation corresponding to the energy difference between the two states. The absorption of infrared (IR) radiation causes transitions between vibrational energy levels in a molecule. Therefore, IR spectroscopy is a useful analytical tool for determining the molecular structure of molecules.
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Inductively coupled plasma (ICP) is the common plasma source used in atomic emission spectroscopy (AES), a technique that detects and analyzes various elements in a sample. This method is often called inductively coupled plasma atomic emission spectroscopy (ICP-AES).
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Interference leads to systematic error in atomic absorption (AA) measurements by enhancing or diminishing the analytical signal or the background. These interferences can be grouped into three main categories: spectral interference, chemical interference, and physical interference.
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Explainable artificial intelligence for spectroscopy data: a review.

Jhonatan Contreras1,2, Thomas Bocklitz3,4,5

  • 1Institute of Physical Chemistry (IPC) and Abbe Center of Photonics (ACP), Friedrich Schiller University Jena, Member of the Leibniz Centre for Photonics in Infection Research (LPI), Helmholtzweg 4, 07743, Jena, Germany.

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Summary
This summary is machine-generated.

This review explores explainable artificial intelligence (XAI) in spectroscopy, finding most studies identify key spectral bands using methods like SHAP and LIME. Future work should adapt XAI for unique spectroscopic data challenges.

Keywords:
Explainable artificial intelligenceInterpretabilityMachine learningRaman spectraSHAPSpectroscopy

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Area of Science:

  • Spectroscopy
  • Artificial Intelligence
  • Data Analysis

Background:

  • Explainable artificial intelligence (XAI) is crucial in image analysis but underutilized in spectroscopy.
  • Understanding spectral data requires interpretable AI models.

Purpose of the Study:

  • To systematically review the application of XAI in spectroscopy.
  • To identify benefits, challenges, and common methods used.

Main Methods:

  • Systematic literature search following PRISMA 2020 guidelines.
  • Inclusion/exclusion criteria applied to 259 initial results, yielding 21 studies.
  • Analysis of AI techniques and their application in spectral data analysis.

Main Results:

  • Most studies used XAI for identifying significant spectral bands, not intensity peaks.
  • Popular XAI methods include SHapley Additive exPlanations (SHAP), LIME-inspired masking, and Class Activation Mapping (CAM).
  • Model-agnostic and easy-to-use XAI methods were preferred.

Conclusions:

  • XAI is emerging in spectroscopy, primarily for spectral band identification.
  • SHAP, LIME, and CAM are key methods, valued for their interpretability.
  • Further research is needed to develop novel XAI methods tailored for spectroscopy.