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A Practical Approach for Validation of Compound Identification in Comprehensive Two-Dimensional Gas

Friscilla Hermatasia1, Apitchaya Sanchai1,2, Nuttanee Tungkijanansin1

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|October 21, 2025
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Summary
This summary is machine-generated.

A new curve-fitting method accurately calculates retention indices for compound identification in GC×GC-MS without reference standards. This approach validates thousands of compounds, improving data reliability in complex chemical analyses.

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Area of Science:

  • Analytical Chemistry
  • Chromatography
  • Mass Spectrometry

Background:

  • Compound identification in comprehensive two-dimensional gas chromatography coupled with mass spectrometry (GC×GC-MS) often relies on retention indices (I).
  • Accurate calculation of retention indices is crucial for reliable compound identification.
  • Traditional methods require reference compound injection, which can be impractical.

Purpose of the Study:

  • To establish a simple curve-fitting approach for calculating retention indices (I) without the need for reference compound injection.
  • To apply this method for validating compound identification data in GC×GC-MS.
  • To assess the accuracy and applicability of the developed method for compound identification and reannotation.

Main Methods:

  • Developed a curve-fitting model using compound names, elution temperature, and experimental retention times to calculate 1D and 2D retention indices (I_Cal).
  • Investigated different mathematical models, selecting an exponential equation with eight coefficients as the most suitable.
  • Validated the model by comparing calculated I (I_Cal) with literature I (I_Lit) for 495 compounds.
  • Established criteria (Δ¹I and Δ²I within ±60 units) for validating compound identities based on the differences between I_Lit and I_Cal.
  • Applied the method to analyze 1741 peaks from 15 samples and screen 1285 compounds from 85 literature results.

Main Results:

  • The chosen exponential model demonstrated high accuracy, with average R² values of 0.9982 (¹I) and 0.9957 (²I).
  • The model achieved low errors: MAE of 8.29/12.89 and RMSE of 11.63/16.48 for ¹I and ²I, respectively.
  • Application to sample data identified 536 (out of 995) and 534 (out of 746) compounds as potentially misidentified based on tentative criteria.
  • The method successfully screened 1285 compounds from literature data, highlighting its utility in re-evaluating existing results.
  • Reannotation of 61 peaks with more correct identities was demonstrated.

Conclusions:

  • The developed curve-fitting approach provides a robust and efficient method for calculating retention indices in GC×GC-MS without reference standards.
  • This method significantly enhances the reliability of compound identification by flagging potentially misidentified compounds.
  • The approach is broadly applicable for validating compound identities in new datasets and re-evaluating published GC×GC-MS results.