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In a flame photometer, when a solution like potassium chloride is aspirated into the flame, the solvent evaporates, leaving behind dehydrated salt. This salt dissociates into free gaseous atoms in their ground state. Some of these atoms absorb energy from the flame, leading to their excitation. The excited atoms return to the ground state, emitting photons at characteristic wavelengths. Because only electronic transitions are involved, the resulting emission lines are very narrow. The intensity...
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A fluorescence-based multivariate method for biodiesel quantification in undiluted diesel-biodiesel blends without

Fernando R Conceição1, Flávio S Michels2, Evaristo A Falcão3

  • 1Optics and Photonics Group, Institute of Physics, Federal University of Mato Grosso do Sul, PO Box 549, 79070-900 Campo Grande, MS, Brazil; Federal Institute of Mato Grosso do Sul, 79750-000 Nova Andradina, SP, Brazil.

Spectrochimica Acta. Part A, Molecular and Biomolecular Spectroscopy
|December 24, 2023
PubMed
Summary

This study introduces a rapid method using excitation-emission matrices (EEMs) and parallel factor analysis (PARAFAC) to quantify biodiesel in diesel-biodiesel blends (DBBs) without sample prep. The developed EEMs-PARAFAC model accurately determines biodiesel content in undiluted blends.

Keywords:
BiodieselDieselEEMFluorescencePARAFACQuantification

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Area of Science:

  • Analytical Chemistry
  • Spectroscopy
  • Chemometrics

Background:

  • Biodiesel is a renewable fuel, but accurate quantification in blends is crucial.
  • Traditional methods for biodiesel quantification often require sample preparation, increasing analysis time and cost.
  • Developing rapid, accurate, and sample-prep-free methods for biodiesel content analysis is highly desirable.

Purpose of the Study:

  • To develop and validate an analytical model for quantifying biodiesel content in undiluted diesel-biodiesel blends (DBBs).
  • To utilize excitation-emission matrices (EEMs) coupled with parallel factor analysis (PARAFAC) for rapid and accurate biodiesel assessment.
  • To establish a method that eliminates the need for pre-sample preparation.

Main Methods:

  • Acquisition of excitation-emission matrices (EEMs) for diesel-biodiesel blends.
  • Decomposition of EEMs using parallel factor analysis (PARAFAC) to extract relevant components.
  • Development of a quantitative analytical model based on PARAFAC loading values.
  • Validation of the model using statistical parameters like LOD, LOQ, RMSEP, and bias.

Main Results:

  • The EEMs-PARAFAC model successfully quantified biodiesel content in undiluted DBBs.
  • The analytical model demonstrated a limit of detection (LOD) of 2.5% and a limit of quantification (LOQ) of 11% w/w.
  • Model robustness was confirmed through rigorous statistical analysis of prediction errors and bias.

Conclusions:

  • An accurate and robust analytical model based on EEMs-PARAFAC was developed for biodiesel quantification.
  • The method offers a significant advantage by eliminating the need for sample preparation in DBBs analysis.
  • This approach provides a rapid and efficient tool for assessing biodiesel content in fuel blends.