Evidence map›Paper›PMID 39554449›Full record

ArticleACS omega2024

Dyed Hair and Swimming Pools: The Influence of Chlorinated and Nonchlorinated Agitated Water on Surface-Enhanced Raman Spectroscopic Analysis of Artificial Dyes on Hair.

Aidan P Holman, Roa Elsaigh, Ragd Elsaigh, Axell Rodriguez, Dmitry Kurouski

Abstract read
In one paragraph

Article in ACS omega, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Aidan P HolmanInterdisciplinary Faculty of Toxicology Program, Texas A&M University, College Station, Texas 77843, United States.
Roa ElsaighDepartment of Biochemistry and Biophysics, Texas A&M University, College Station, Texas 77843, United States.
Ragd ElsaighDepartment of Biochemistry and Biophysics, Texas A&M University, College Station, Texas 77843, United States.
Axell RodriguezDepartment of Biochemistry and Biophysics, Texas A&M University, College Station, Texas 77843, United States.
Dmitry KurouskiDepartment of Biochemistry and Biophysics, Texas A&M University, College Station, Texas 77843, United States.ORCID https://orcid.org/0000-0002-6040-4213

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Chlorine, commonly found in pools and tap water, presents an intriguing concern in forensic hair analysis due to its sources and composition. Current forensic analysis involves optical microscopy which is subjected to advanced training where even multiple experts can deliver opposing conclusions for the same hair sample. Despite challenges in traditional analysis methods, emerging techniques like surface-enhanced Raman spectroscopy (SERS) offer promising solutions, showcasing success even in harsh environments like prolonged sunlight or stagnant water immersion. This study employs partial least-squares discriminant analysis (PLS-DA) to evaluate SERS efficacy in identifying dyes on hair immersed in chlorinated and distilled moving water for up to eight weeks. Our results demonstrated that one semipermanent colorant overwhelmingly influenced Raman signals in dyed hair exposed to both chlorinated and nonchlorinated water over an eight-week period, masking other colorants' spectral signatures. Despite one colorant's dominance, PLS-DA identified underlying colorants and their exposure conditions, suggesting persistent, unique interactions between original colorants and the environment. This study demonstrates the high potential for PLS-DA-based identifications of dyes on hair using SERS.

Identifiers

PMID39554449
PMCPMC11561636

What OpenQuestion holds

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Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.