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Related Concept Videos

¹H NMR Signal Integration: Overview00:58

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The intensity of a signal, which can be represented by the area under the peak, depends on the number of protons contributing to that signal. The area under each peak is shown as a vertical line called an integral, with the integral value listed under it, as seen in the proton NMR spectrum of benzyl acetate. Each integral value is divided by the smallest integral value to obtain the ratio of the number of protons producing each signal. The ratio reveals the relative number of protons and not...
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The probability of having two carbon-13 atoms next to each other is negligible because of the low natural abundance of carbon-13. Consequently, peak splitting due to carbon-carbon spin-spin coupling is not observed in spectra. However, protons up to three sigma bonds away split the carbon signal according to the n+1 rule, resulting in complicated spectra.
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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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Mass spectrometry is an important technique for the identification of pure compounds. However, it has some limitations for the analysis of complex mixtures, often due to excessive fragmentation making the spectrum too complicated to decipher. Mass spectrometry can be combined with suitable separation methods in sequence, forming hyphenated methods, which are useful in the analysis of complex mixtures.
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Updated: Jan 9, 2026

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Multi-Method Integration for Spectral Band Importance Analysis in Coal Characterization.

Jie Zhang1,2, Tianju Zhao3, Youquan Dou1,2

  • 1National Environmental Protection Research Institute for Electric Power Co., Ltd., Nanjing 210031, China.

Sensors (Basel, Switzerland)
|December 11, 2025
PubMed
Summary

This study introduces a new framework for selecting spectral features to accurately assess coal quality using near-infrared spectroscopy. The method combines multiple analytical techniques for more reliable and interpretable results, improving online monitoring.

Keywords:
coal quality assessmentmachine learningmoisture in air-dried basis (Mad)multi-method analysisnear-infrared spectroscopyvolatile matter in air-dried basis (Vad)

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Area of Science:

  • Analytical Chemistry
  • Spectroscopy
  • Chemometrics

Background:

  • Near-infrared (NIR) spectroscopy is crucial for coal quality assessment.
  • Selecting robust spectral features is challenging due to trade-offs between statistical and machine learning methods.
  • Existing methods may lack stability or interpretability in feature selection.

Purpose of the Study:

  • To develop a robust multi-method analysis framework for spectral feature selection in coal quality assessment.
  • To integrate diverse analytical approaches for improved accuracy and reliability.
  • To create more interpretable and physicochemically coherent wavelength importance profiles.

Main Methods:

  • Proposed a multi-method analysis framework integrating statistical correlations, SHAP-interpreted machine learning, and latent-variable regression.
  • Introduced a novel fusion strategy synthesizing importance profiles based on consistency, smoothness, and local concentration.
  • Evaluated feature selection performance using Moisture (Mad) and Volatile Matter (Vad) prediction models.

Main Results:

  • The fusion strategy yielded more interpretable and coherent wavelength importance profiles.
  • Selected features demonstrated superior prediction performance across various regression models.
  • The framework showed particular robustness with limited training data, enhancing reliability.

Conclusions:

  • The proposed framework offers a structured methodology for identifying compact and informative spectral features.
  • This approach facilitates the development of efficient models for online coal quality monitoring.
  • The study contributes to improved process control through enhanced spectral analysis.