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Development of an automated data processing method for sample to sample comparison of seized methamphetamines.

Sanggil Choe¹, Jaesin Lee, Hyeyoung Choi

¹Forensic Chemistry Division, Southern District Office of National Forensic Service, Yangsan, Republic of Korea.

Forensic Science International

|November 7, 2012

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Methamphetamine profiling links seized drug samples. New Visual Basic Application (VBA) modules automate data processing for gas chromatography-flame ionization detector (GC-FID) analysis, improving similarity assessment and identifying common origins of illicit methamphetamine.

Area of Science:

Forensic Chemistry
Analytical Chemistry
Computational Chemistry

Background:

Methamphetamine profiling is crucial for tracing drug supply chains and synthetic methods.
Analyzing minor impurities in methamphetamine aids in estimating precursors and clandestine manufacturing techniques.
Evaluating sample similarity through chromatographic peak analysis is vital for forensic investigations.

Purpose of the Study:

To develop automated data processing modules for gas chromatography-flame ionization detector (GC-FID) analysis of methamphetamine.
To enhance the efficiency and accuracy of similarity assessment between seized methamphetamine samples.
To facilitate the identification of common origins and links between drug samples in South Korea.

Main Methods:

Development of Microsoft Visual Basic Application (VBA) modules for automated data processing.

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Utilizing gas chromatography-flame ionization detector (GC-FID) with a DB-5 column and internal standards.

Applying Pearson correlation coefficient for similarity assessment of processed chromatographic data.

Main Results:

The developed VBA modules efficiently processed GC-FID raw data, correcting for retention time shifts and response deviations.
Similarity assessment using Pearson correlation yielded coefficients greater than 0.99 for samples from the same case.
Analysis of 131 seized samples identified four samples from two cases with common origins, exhibiting visually identical chromatograms.

Conclusions:

The developed VBA modules significantly expedite and simplify the processing of GC-FID data for methamphetamine analysis.
These modules enable robust similarity assessment by recognizing peak patterns across entire chromatograms, without requiring individual peak spectral identification.
The automated system is a valuable tool for augmenting the forensic investigation of seized methamphetamine samples, aiding in the identification of trafficking networks.