Evidence map›Paper›PMID 41376071›Full record

ArticleFoods (Basel, Switzerland)2025

Rediscovering Olive Mill Wastewater: New Chemical Insights Through Untargeted UHPLC-QTOF-MS Data-Dependent Analysis Approach.

Laura Alessandroni, Massimo Ricciutelli, Simone Angeloni, Giovanni Caprioli, Gianni Sagratini

Abstract read
In one paragraph

Article in Foods (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Laura AlessandroniChemistry Interdisciplinary Project (ChIP), School of Pharmacy, University of Camerino, 62032 Camerino, Italy.ORCID 0000-0002-3533-3791
Massimo RicciutelliChemistry Interdisciplinary Project (ChIP), School of Pharmacy, University of Camerino, 62032 Camerino, Italy.ORCID 0000-0002-4782-7350
Simone AngeloniChemistry Interdisciplinary Project (ChIP), School of Pharmacy, University of Camerino, 62032 Camerino, Italy.ORCID 0000-0001-6571-2242
Giovanni CaprioliChemistry Interdisciplinary Project (ChIP), School of Pharmacy, University of Camerino, 62032 Camerino, Italy.ORCID 0000-0002-5530-877X
Gianni SagratiniChemistry Interdisciplinary Project (ChIP), School of Pharmacy, University of Camerino, 62032 Camerino, Italy.ORCID 0000-0001-6231-8709

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With the advent of new analytical technologies and the urgent environmental problem, reopening investigations into polluting waste matrices becomes a priority. Olive mill wastewater is a pollutant and phytotoxic by-product of olive oil production. An untargeted UHPLC-QTOF analysis of three olive mill wastewaters from three different olive cultivars was performed, and modern informatic platforms were involved to characterize the chemical components in-depth. Data elaboration and statistical analysis confirmed the differences between samples and revealed a total of 364 annotated compounds, including iridoids, phenolic compounds, flavonoids, lignans, cinnamic acid derivatives, and pyrrolidine derivatives. Many of these metabolites, including compounds with known antioxidant and bioactive potential, are scarcely reported in olive products and by-products. The outcomes of this work could be useful for rethinking olive mill wastewater as a source of bioactive compounds to develop and optimize new detoxification strategies.

Indexed as

bioactive compoundsLC-MSmetabolitesolive mill wastewaterolive oilolive oil by-productsuntargeted analysis

Identifiers

PMID41376071
PMCPMC12692448

What OpenQuestion holds

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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.