Multi-Element Analysis of Commercial Cigarette Tobacco in Türkiye by ICP-MS: Manufacturer-Related Differences and Multivariate Compositional Patterns

Abstract Elemental concentrations were determined in 27 commercial cigarette tobacco samples from six manufacturers in Türkiye, with emphasis on manufacturer-related differences and multivariate compositional patterns. Thirteen elements (As, Ba, Cd, Co, Cr, Cu, Mn, Mo, Ni, Pb, Ti, V, and Zn) were quantified by inductively coupled plasma mass spectrometry (ICP-MS). Given the non-normal distribution of most elemental data, group differences were assessed using the Kruskal–Wallis H test with Dunn–Bonferroni post-hoc comparisons; interelement associations were examined by Spearman correlation, and multivariate structure was explored through PCA and hierarchical cluster analysis (HCA). Mn exhibited the highest mean concentration (135.67 mg/kg), followed by Ba (66.56 mg/kg), Zn (30.30 mg/kg), and Ti (26.55 mg/kg), whereas Cd (0.36 mg/kg) and Pb (0.37 mg/kg) occurred at lower concentrations. Significant manufacturer-related differences were identified for 10 elements (p < 0.05). Strong positive correlations were observed between Ti–V (ρ = 0.965), Cu–Ti (ρ = 0.883), and Cu–V (ρ = 0.864). The first two principal components explained 64.61% of the total variance, and HCA identified two major clusters comprising 19 and eight samples. Complete manufacturer-specific separation was not achieved, suggesting that within-manufacturer variability in raw material sourcing and blending may be comparable in scale to between-manufacturer differences. The results confirm that ICP-MS combined with multivariate statistics offers a practical framework for exploratory characterization of commercial tobacco elemental profiles.

Authors

Institutions

Publication Details

Journal
Research Square
Published
2026-11-10
DOI
https://doi.org/10.21203/rs.3.rs-11512982/v1
Primary Topic
Heavy Metals in Plants
Type
preprint
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
NOV
preprint

Multi-Element Analysis of Commercial Cigarette Tobacco in Türkiye by ICP-MS: Manufacturer-Related Differences and Multivariate Compositional Patterns

Ramazan GÜN
Research Square
Heavy Metals in Plants
preprint

Multi-Element Analysis of Commercial Cigarette Tobacco in Türkiye by ICP-MS: Manufacturer-Related Differences and Multivariate Compositional Patterns

Ramazan GÜN
preprint en

Abstract

Abstract Elemental concentrations were determined in 27 commercial cigarette tobacco samples from six manufacturers in Türkiye, with emphasis on manufacturer-related differences and multivariate compositional patterns. Thirteen elements (As, Ba, Cd, Co, Cr, Cu, Mn, Mo, Ni, Pb, Ti, V, and Zn) were quantified by inductively coupled plasma mass spectrometry (ICP-MS). Given the non-normal distribution of most elemental data, group differences were assessed using the Kruskal–Wallis H test with Dunn–Bonferroni post-hoc comparisons; interelement associations were examined by Spearman correlation, and multivariate structure was explored through PCA and hierarchical cluster analysis (HCA). Mn exhibited the highest mean concentration (135.67 mg/kg), followed by Ba (66.56 mg/kg), Zn (30.30 mg/kg), and Ti (26.55 mg/kg), whereas Cd (0.36 mg/kg) and Pb (0.37 mg/kg) occurred at lower concentrations. Significant manufacturer-related differences were identified for 10 elements (p < 0.05). Strong positive correlations were observed between Ti–V (ρ = 0.965), Cu–Ti (ρ = 0.883), and Cu–V (ρ = 0.864). The first two principal components explained 64.61% of the total variance, and HCA identified two major clusters comprising 19 and eight samples. Complete manufacturer-specific separation was not achieved, suggesting that within-manufacturer variability in raw material sourcing and blending may be comparable in scale to between-manufacturer differences. The results confirm that ICP-MS combined with multivariate statistics offers a practical framework for exploratory characterization of commercial tobacco elemental profiles.

Research Square
Bingöl University (TR)
Heavy Metals in Plants
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Laika access coming soon