Automated optimization and construction of chemometric models based on highly variable raw chromatographic data

Area of Science:

• Analytical Chemistry
• Chemometrics
• Forensic Science

Background:

• Direct chemometric interpretation of raw chromatographic data offers advantages but faces challenges in data alignment and feature selection.
• Existing data alignment methods struggle with highly variable background matrices in complex samples.
• High data rates in modern chromatography generate numerous variables, necessitating automated feature selection.

Purpose of the Study:

• To develop and validate a robust data alignment approach for raw chromatographic data.
• To implement an automated feature selection method for constructing reliable chemometric models.
• To address challenges in analyzing complex samples like simulated arson debris using GC-MS.

Main Methods:

• Utilized a series of deuterated alkanes as retention anchors for precise chromatographic data alignment.
• Developed a novel cluster resolution metric for automated feature selection.
• Employed partial least squares discriminant analysis (PLS-DA) for chemometric modeling.
• Analyzed simulated arson debris samples using passive headspace extraction and GC-MS.

Main Results:

• The proposed alignment method effectively corrects retention time shifts, even in samples with variable backgrounds.
• The automated feature selection routine successfully identified relevant variables, reducing model complexity and improving interpretability.
• The combined approach enabled robust chemometric modeling for the classification of simulated arson debris samples.

Conclusions:

• The novel alignment strategy using deuterated alkanes provides accurate data alignment for chemometric analysis.
• Automated feature selection based on the cluster resolution metric enhances the efficiency and reliability of chemometric model development.
• This integrated approach offers a powerful solution for analyzing complex chromatographic data, particularly in forensic applications.