ReviewMolecules (Basel, Switzerland)2025
Molecular Networking in Cosmetic Analysis: A Review of Non-Targeted Profiling for Safety Hazards and Bioactive Compounds.
Review in Molecules (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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.
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.
Who cites it
4 citing papers in PubMed.
- Structure-guided parameter optimization of feature-based molecular networking for DIA-HRMS screening of antihistamines in cosmetics.Analytical and bioanalytical chemistry · 2026Article
- Lab-on-a-tip platform with cotton matrices for colorimetric detection of carmine in lipsticks.RSC advances · 2026Article
- LC-HRMS and Molecular Networking for Non-Targeted Screening of Emerging Contaminants in Cosmetics: Workflow, Applications, and Regulatory Challenges.Molecules (Basel, Switzerland) · 2026Review
- Anticancer potential of Dendrocnide meyeniana through phytochemical profiling, ADMET analysis, molecular docking, and in silico cytotoxicity evaluation.Scientific reports · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Molecular networking (MN) is a novel mass spectrometry data analysis method that has advanced significantly in recent years and has rapidly emerged as a popular technique. By visualizing the connections between structurally similar compounds in mass spectra, MN greatly enhances the efficiency with which harmful substances and bioactive ingredients in cosmetics are screened. In this review, we summarize the principles and main categories of MN technology and systematically synthesize its progress in cosmetic testing applications based on 83 recent studies (2020 to 2025). These applications include screening banned additives, analyzing complex matrix components, and identifying efficacy-related ingredients. We highlight MN's successful application in detecting prohibited substances, such as synthetic dyes and adulterants, with limits of detection (LOD) as low as 0.1-1 ng/g, even in complex matrices, such as emulsions and colored products. MN-guided isolation has enabled the structural elucidation of over 40 known and novel compounds in the analysis of natural ingredients. We also discuss current challenges, such as limitations in instrument sensitivity, matrix effects, and the lack of cosmetic-specific component databases. Additionally, we outline future prospects for expanding MN's application scope in cosmetic testing and developing it toward computer-aided intelligence. This review aims to provide valuable references for promoting innovation in cosmetic testing methods and strengthening quality control in the industry.
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What OpenQuestion holds
Registered trials
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.