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Nuclear magnetic resonance (NMR) spectroscopy is a very valuable analytical technique for researchers. It has been used for more than 50 years as an analytical tool. F. Bloch and E. Purcell formulated NMR in 1946 and won the 1952 Nobel Prize in Physics  for their work. Biological macromolecules such as proteins, nucleic acids, lipids, and organic molecules including pharmaceutical compounds, can be studied using this versatile tool that exploits the magnetic properties of certain nuclei.
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The 1D NMR spectrum of large and complex molecules like natural products has complicated splitting patterns and overlapping signals, which can be easily interpreted using 2-dimensional (2D) NMR. Unlike 1D NMR, 2D NMR has two frequency axes that provide the coupling information between the nucleus A and nucleus B in a molecule. The process from which 2D spectra are obtained has four steps.
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The role of the detectors in High-Performance Liquid Chromatography (HPLC) is to analyze the solutes as they exit from the chromatographic column. The detector recognizes the solute's property and generates corresponding electrical signals, which are converted into a readable graph of the detector's response versus elution time called a chromatogram at the computer. There are several types of HPLC detectors, each with its own advantages and limitations, depending on the analyte...
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  9. Automotive Combustion And Fuel Engineering
  11. A Proposed Two-level Classification Approach For Forensic Detection Of Diesel Adulteration Using Nmr Spectroscopy And Machine Learning.
  1. Home
  3. Research Domains
  5. Engineering
  7. Automotive Engineering
  9. Automotive Combustion And Fuel Engineering
  11. A Proposed Two-level Classification Approach For Forensic Detection Of Diesel Adulteration Using Nmr Spectroscopy And Machine Learning.

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A proposed two-level classification approach for forensic detection of diesel adulteration using NMR spectroscopy and machine learning.

J K Dadson1, N Y Asiedu2, J A Iggo3

  • 1Department of Biochemistry and Biotechnology, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana. jaydadson1990@gmail.com.

Analytical and Bioanalytical Chemistry
|June 18, 2024

View abstract on PubMed

Summary
This summary is machine-generated.

A new method using NMR spectroscopy and machine learning effectively detects diesel fuel adulteration. This approach offers a potential alternative to current systems for identifying common adulterants like kerosene, naphtha, and premix.

Keywords:
ChemometricsFuel adulterationMachine learningProton NMR spectroscopy

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Area of Science:

  • Analytical Chemistry
  • Spectroscopy
  • Machine Learning

Background:

  • Diesel fuel adulteration is a significant issue in developing nations, including Ghana.
  • Current solvent tracer analysis methods have limitations, necessitating alternative detection tools.
  • Automotive fuel adulteration impacts fuel quality and engine performance.

Purpose of the Study:

  • To develop and validate a two-level classification method for detecting diesel fuel adulteration.
  • To combine Nuclear Magnetic Resonance (NMR) spectroscopy with machine learning (ML) for fuel analysis.
  • To identify specific adulterants in diesel fuel, including kerosene, naphtha, and premix.

Main Methods:

  • Utilized proton (1H) NMR spectroscopy for sample analysis.
  • Developed Support Vector Machine (SVM) classification models for two-level detection.
  • Trained ML models using datasets with 20-40% w/w adulterant levels.

    • Main Results:

    • The first-level SVM model achieved 97.5% accuracy in classifying samples as neat or adulterated.
    • The second-level SVM model showed high accuracy for kerosene and premix detection (0% error).
    • A 2.5% error rate was observed for naphtha detection; performance decreased with adulterant levels below 20% w/w.

    Conclusions:

    • The combined NMR spectroscopy and ML approach is a promising tool for rapid diesel adulteration detection.
    • This method could serve as an alternative or supplement to existing fuel marking systems in Ghana.
    • Further refinement is needed to improve detection accuracy for lower adulterant concentrations.