Classification Analysis of Bituminous Coals Using a Combination of GC-MS and Chemometric Methods

Vitaliy N. Fomin, Ainabayev Assanali, Saule K. Aldabergenova, Daulet A. Kaykenov, Milana A. Turovets

2026

Full text Open access: Gold Metadata (CSL-JSON)
DOI 10.31489/2959-0663/2-26-6
Data confidence 70 medium
How this score is calculated

It is built from checkable properties of the record itself: whether the article has a DOI, how many independent sources confirmed its data, whether the required fields are filled in, and how recent the latest confirmation is. Disagreement between sources and a missing identifier lower it. A value of 80 or above means the record was published without further checking; 60 to 79 means it is published at reduced confidence, with a manual check advisable. A record verified by an editor by hand is never pushed below 90 by a recalculation. This scores our data about the article, not the article itself.

Abstract

A comprehensive approach to the analysis of organic matter in bituminous coals from Kazakhstani deposits was developed, based on a combination of liquid extraction, gas chromatography with mass spectrometric detection (GC-MS), and chemometric data processing. A triple extraction system of dichloromethane–chloroform–tetrachloromethane (1:1:1) was proposed for sample preparation, providing representative extraction of aliphatic, aromatic and heteroatomic components without additional extract concentration. Chromatographic profiles of 120 coal extracts from four sources in Central and Northern Kazakhstan were analyzed. Optimization of the chromatographic separation conditions was carried out using probabilistic-deterministic design of experiment, which made it possible to establish robust relationships between the instrument parameters (column heating rate, carrier gas pressure) and the chromatogram characteristics. Chemometric data processing using principal component analysis (PCA), hierarchical cluster analysis (HCA), and k-means method revealed reproducible grouping of samples based on chromatographic profile similarity. Compact clusters corresponding to conventionally designated coal sources were formed in the principal component space, which is confirmed by the clustering results. The obtained results demonstrate the applicability of GC-MS and the chemometric approach for coal classification analysis and provide the foundation for the creation of a database of chromatographic fingerprints of the organic phase of coals.