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Updated: Jul 29, 2025
10:14
Chromatographic Fingerprinting by Template Matching for Data Collected by Comprehensive Two-Dimensional Gas Chromatography
Published on: September 2, 2020
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A new constraint to model background signals when processing chromatographic-spectral second-order data with
Maira D Carabajal1, Rocío Pellegrino Vidal2, Juan A Arancibia1
1Departamento de Química Analítica, Facultad de Ciencias Bioquímicas y Farmacéuticas, Universidad Nacional de Rosario, Instituto de Química Rosario (CONICET-UNR), Suipacha 531, 2000, Rosario, Argentina.
Analytica Chimica Acta
|May 27, 2023
Summary
Chemometric analysis using multivariate curve resolution-alternating least-squares (MCR-ALS) can be improved. A new background interpolation constraint reduces ambiguity in chromatographic-spectral data processing.
Area of Science:
- Chemometrics
- Analytical Chemistry
- Spectroscopy
Background:
- Multivariate curve resolution-alternating least-squares (MCR-ALS) is standard for processing second-order chromatographic-spectral data.
- Baseline contributions in data can cause abnormal lumps or negative dips in MCR-ALS retrieved background profiles.
Purpose of the Study:
- To address abnormal features in MCR-ALS retrieved background profiles caused by baseline contributions.
- To introduce a novel constraint for improving MCR-ALS analysis of chromatographic-spectral data.
Main Methods:
- Developed and described a new background interpolation constraint for MCR-ALS.
- Utilized simulated and experimental chromatographic-spectral data for validation.
- Assessed the impact of the constraint on rotational ambiguity.
Main Results:
- The new constraint effectively mitigates abnormal lumps and negative dips in retrieved background profiles.
- Rotational ambiguity in MCR-ALS solutions was reduced.
- Analyte concentrations from experimental data aligned with previously reported values.
Conclusions:
- The proposed background interpolation constraint enhances the reliability of MCR-ALS.
- Improved interpretation of physicochemical properties from processed data.
- Reduces ambiguity in MCR-ALS solutions for chromatographic-spectral data analysis.
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