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Attenuated Total Reflectance (ATR) Infrared Spectroscopy: Overview01:13

Attenuated Total Reflectance (ATR) Infrared Spectroscopy: Overview

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Attenuated total reflectance (ATR) infrared spectroscopy is a powerful analytical technique used to study the composition of materials. It is widely employed in chemistry, materials science, forensic science, and other fields where sample characterization is required. ATR has several advantages over traditional transmission IR spectroscopy, including the requirement of little to no sample preparation and the ability to analyze a wide range of samples.
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The underlying principle of Raman spectroscopy is based on the interaction between light and matter, specifically molecules' inelastic scattering of photons. When a monochromatic beam of light, typically from a laser source, interacts with a sample, most scattered light has the same frequency as the incident light. This is known as Rayleigh scattering.
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Raman Spectroscopy Instrumentation: Overview01:26

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A conventional Raman spectrophotometer includes a laser source, a sample holding system, a wavelength selector, and a detector.
The monochromatic laser source, typically using visible or near-infrared radiation, generates a highly focused beam of light. This light interacts with the molecules of the sample, scattering some of the light. Liquid and gaseous samples are usually tested in ordinary glass capillaries, while solids can be analyzed as powders packed in capillaries or as potassium...
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Updated: Oct 28, 2025

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Diffuse reflectance spectroscopy based rapid coal rank estimation: A machine learning enabled framework.

Nafisa Begum1, Abhik Maiti1, Debashish Chakravarty1

  • 1Department of Mining Engineering, IIT Kharagpur, India.

Spectrochimica Acta. Part A, Molecular and Biomolecular Spectroscopy
|July 17, 2021
PubMed
Summary

Diffuse reflectance spectroscopy (DRS) effectively classifies coal ranks using machine learning. This rapid method accurately distinguishes between lignite, sub-bituminous, bituminous, and anthracite coal types.

Keywords:
Coal rankDiffuse reflectance spectroscopyLogistic regressionRandom forest classifierSupport vector machine

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Area of Science:

  • Geoscience
  • Analytical Chemistry
  • Data Science

Background:

  • Accurate coal rank classification is crucial for resource management and utilization.
  • Traditional methods for coal analysis can be time-consuming and destructive.

Purpose of the Study:

  • To evaluate the efficacy of diffuse reflectance spectroscopy (DRS) for classifying coal samples into different ranks.
  • To explore the application of various machine learning algorithms for coal classification using spectral data.

Main Methods:

  • Diffuse reflectance spectroscopy (DRS) was employed to collect spectral data from coal samples across the Vis-NIR-SWIR range (350-2500 nm).
  • Spectral characteristics like profile shape, slope, and absorption intensity were analyzed.
  • Machine learning algorithms including Logistic Regression, Random Forest, and Support Vector Machines (SVM) were trained on the DRS dataset.
  • Class imbalance issues were addressed using SMOTE and minority class oversampling techniques.

Main Results:

  • Broad classification into lignite, sub-bituminous, bituminous, and anthracite achieved high accuracy (0.98) and an F1 score of 0.75.
  • Further sub-class level classification yielded good results with an accuracy of 0.77 and an F1 score of 0.64.
  • The study demonstrated improved classification accuracy after handling class imbalances.

Conclusions:

  • Diffuse reflectance spectroscopy is a viable and effective technique for rapid coal classification.
  • The integration of DRS with machine learning algorithms offers a powerful approach for coal rank determination.
  • This non-destructive method provides a foundation for developing efficient coal assessment systems.