ArticleAnalytical and bioanalytical chemistry2025
Assessment of the differentiation of Sambucus nigra plant parts using a multi-target and suspect screening by LC-HRMS and ICP-OES.
Article in Analytical and bioanalytical chemistry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- LC-HRMS and Molecular Networking for Non-Targeted Screening of Emerging Contaminants in Cosmetics: Workflow, Applications, and Regulatory Challenges.Molecules (Basel, Switzerland) · 2026Review
- Comparison of the phenolic and antioxidant potential of five European herbal remedies by effect-directed analysis using offline two-dimensional liquid chromatography-high resolution mass spectrometry.Analytical and bioanalytical chemistry · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Traditional plant medicine is among the most popular and strongest growing forms of alternative medicine. Based on ancient healing practices, it still plays a significant role in the treatment of many medical conditions. Its impact significantly influenced the creation of therapeutic products by using medical herbs in a continuously growing market. Therefore, it is important to properly characterize and differentiate medical plant parts. In this work, a multi-target and suspect screening approach for the elemental composition as well as the organic compound profile of different plant parts of Sambucus nigra was assessed using inductively coupled plasma-optical emission spectroscopy (ICP-OES) and liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS) to identify potential biomarkers and establish their correlations with the total phenolic content (TPC). For the elemental analysis, calcium and potassium were found as the main elements screened in the S. nigra plant parts, but with no correlation to the TPC content. In contrast, phosphorus showed a slight correlation with the TPC and significant correlations with concentrations of several phenolic compounds. Regarding the organic compound profile, 88 features were tentatively identified with high confidence (levels 1-3), some of them for the first time in S. nigra. These compounds are mainly associated with phenolic compounds such as flavonoids or cinnamic acid derivatives, among others. Moreover, 18 of these compounds were identified as potential biomarkers to differentiate between plant parts (i.e. rutin, quercetin, bergapten, etc.). These findings not only give insights into the characterization of S. nigra but might serve as a basis for evaluating individual substances as well as different plant parts regarding their potential hepatoprotective characteristics. The LC-HRMS method could serve as a basis for future analyses of medicinal plants, aiming for reduced analysis time, high separation performance (especially for phenolic compounds), and better result comparability.
Indexed as
Identifiers
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.