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Effects of EDTA on End-Point Detection Methods01:18

Effects of EDTA on End-Point Detection Methods

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Different methods, such as visual observance of metal-ion indicators, spectroscopic techniques, and potentiometric methods, can determine the endpoint of an EDTA titration.
In the visual method, metal-ion indicators (metallochromic dyes), which have distinct colors in their free and complex forms, are added to the mixture to signal the titration's end point. They form stable complexes with metal ions, but these complexes are weaker than the corresponding metal–EDTA complexes. As a...
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Related Experiment Video

Updated: May 7, 2026

Measurement of Tissue Non-Heme Iron Content using a Bathophenanthroline-Based Colorimetric Assay
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One-Drop Serum Screening Test to Monitor Tissue Iron Accumulation.

Gabriely S Folli1,2, Anne Louise S Torres2, Matthews Martins2

  • 1Departamento of Chemistry, Federal University of Espírito Santo (UFES), Av. Fernando Ferrari, 514, Vitória, Espírito Santo 29075-910, Brazil.

Analytical Chemistry
|May 30, 2025
PubMed
Summary
This summary is machine-generated.

A new minimally invasive method uses Fourier transform infrared spectroscopy and machine learning to detect iron overload (IO) and measure iron levels in blood and tissues without biopsies. This approach aids in early diagnosis and organ assessment, improving patient outcomes.

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Area of Science:

  • Analytical Chemistry
  • Biomedical Spectroscopy
  • Machine Learning in Medicine

Background:

  • Iron overload (IO) causes significant cellular damage, necessitating early diagnosis for effective treatment and improved survival rates.
  • Current diagnostic methods like biopsies and imaging techniques are invasive, costly, or provide limited data, hindering routine screening.
  • Fourier transform infrared spectroscopy (FTIR) combined with machine learning offers a promising non-invasive alternative for IO diagnosis.

Purpose of the Study:

  • To develop a minimally invasive method for identifying iron overload (IO) and quantifying iron levels in blood and multiple tissues.
  • To assess the performance of Partial Least Squares Discriminant Analysis (PLS-DA) and Partial Least Squares (PLS) regression models for IO detection and quantification.
  • To validate the chemical information derived from FTIR spectra for understanding iron-induced biological responses.

Main Methods:

  • Collected blood and tissue samples (heart, liver, spleen, kidney) from control and iron-administered groups with varying iron doses (250, 500, 1000 mg kg⁻¹).
  • Utilized Fourier transform infrared spectroscopy (FTIR) to obtain spectral data from samples.
  • Constructed PLS-DA classification models (binary and multiclass) and PLS regression models for iron quantification, validated using permutation tests.

Main Results:

  • PLS-DA models demonstrated satisfactory performance in classifying control versus iron-administered groups and different iron dosage levels.
  • PLS regression models accurately quantified iron concentrations in blood and tissues, showing excellent linearity and low errors.
  • Permutation tests confirmed the robustness and chemical basis of the developed models, indicating real biological information capture.

Conclusions:

  • The developed FTIR and machine learning method is a viable, minimally invasive approach for detecting iron overload and quantifying iron levels in blood and tissues.
  • This technique eliminates the need for tissue biopsies, offering a more accessible and potentially routine screening tool.
  • The spectral insights provide a deeper understanding of systemic stress responses and biomolecular adaptations to iron dysregulation.