Evidence map›Paper›PMID 40231123›Full record

ArticleFood chemistry: X2025

Assessing the efficacy of washing methods in reducing multi-pesticide residues in Brown, milled long, and milled basmati Rice.

Filipa Carreiró, Sílvia Cruz Barros, Carla Brites, Patricia Cazón, Duarte Torres, Fernando Ramos, Ana Sanches Silva

Abstract read
In one paragraph

Article in Food chemistry: X, 2025. 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. Review
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

7 authors.

Filipa CarreiróNational Institute for Agrarian and Veterinary Research (INIAV), I.P., Av. da República, 2780-157 Oeiras, Portugal.
Sílvia Cruz BarrosNational Institute for Agrarian and Veterinary Research (INIAV), I.P., Av. da República, 2780-157 Oeiras, Portugal.
Carla BritesNational Institute for Agrarian and Veterinary Research (INIAV), I.P., Av. da República, 2780-157 Oeiras, Portugal.
Patricia CazónNational Institute for Agrarian and Veterinary Research (INIAV), I.P., Av. da República, 2780-157 Oeiras, Portugal.
Duarte TorresEPIUnit-Institute of Public Health, University of Porto, 4200-450 Porto, Portugal.
Fernando RamosUniversity of Coimbra, Faculty of Pharmacy, Polo III, Azinhaga de St. Comba, 3000 548 Coimbra, Portugal.
Ana Sanches SilvaUniversity of Coimbra, Faculty of Pharmacy, Polo III, Azinhaga de St. Comba, 3000 548 Coimbra, Portugal.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Pesticides are vital for protecting crops from diseases but pose risks to human health and the environment, requiring careful evaluation. This study examines pesticide residues in rice and strategies for their removal. Brown, long-milled, and basmati-milled rice samples were spiked with 121 pesticides from different categories (e.g., carbamates, organophosphates, neonicotinoids, triazolobenzothiazoles) at 20 and 50 μg/mL to evaluate residue levels. Pesticides were extracted using the QuEChERS (quick, easy, cheap, effective, rugged, and safe) method and analyzed via High Performance Liquid Chromatography coupled with mass spectrometry. Residue removal was tested using washing methods with and without vinegar. Results showed that long-grain milled rice had higher pesticide residues, and adding vinegar significantly enhanced their removal. These findings contribute to improving food safety practices by providing insights into effective pesticide residue mitigation in rice.

Indexed as

Food processingPesticide residuesQuEChERSRiceWashing effect

Identifiers

PMID40231123
PMCPMC11995112

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.