ArticleAnalytical and bioanalytical chemistry2024
Volatile-compound fingerprinting and discrimination of positional isomers in stamp-pad ink tracing using HS-GC-IMS combined with multivariate statistical analysis.
Article in Analytical and bioanalytical chemistry, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Estimating bloodstain age using headspace gas chromatography-ion mobility spectrometry (HS-GC-IMS) and machine learning.International journal of legal medicine · 2026Article
- The Metabolic Network of Chilled Yak Meat During Storage Was Constructed Based on Metabolomics Technology.Foods (Basel, Switzerland) · 2025Article
- Traceability and discrimination of opium poppy shell analogues using HS-GC-IMS combined with machine learning algorithms.Analytical and bioanalytical chemistry · 2025Article
- Characterization of flavor volatiles in raw and cooked pigmented onion (Current research in food science · 2024Article
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Authors and funding
10 authors.
Funding
Abstract
The rising crime rate associated with document forgery has a significant impact on public safety and social stability. In document fraud cases, determining the origin of a particular stamp-pad ink is the most important objective. In this study, a comprehensive analysis of the volatile compounds in quick-drying stamp-pad inks from six commonly used brands were performed for the first time, utilizing a combination of headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS) and multivariate statistical analysis methods. Visual and comparative analysis of the differential volatile components among different stamp-pad ink samples was conducted using fingerprints and volcano plots. A total of 127 volatile compounds were accurately identified, with ketones, esters, alcohols, and aldehydes being the most abundant compounds in the stamp-pad inks. Hierarchical clustering analysis (HCA), including dendrograms and clustering heatmaps, was utilized to explore the correlations between these compounds and the samples. Additionally, the precise identification of positional isomers and functional group isomers of aliphatic compounds was achieved. To achieve accurate discrimination of various stamp-pad ink samples, a multivariate statistical analysis method was utilized to establish a classification model for them. Based on the results obtained from HS-GC-IMS, effective discrimination among different brands of stamp-pad ink samples was achieved through principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA). The model exhibited excellent performance, with the fit index of dependent variables (R
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