Evidence map›Paper›PMID 40400041›Full record

ArticleBMC research notes2025

Comparative analysis of conventional and IRMS techniques for honey adulteration detection in accordance with ISIRI standards.

Abbas Alimoradian, Nafiseh Khansari, Mahdi Abdollahi, Behnoosh Ansari Asl, Sonia Asadi, Akbar Yousefi Maghool, Arash Rabiei, Ali Khademi, Mohsen Shamsi

Abstract readComparative Study
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC research notes, 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. 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

9 authors.

Abbas AlimoradianDepartment of Pharmacology, School of Medicine, Arak University of Medical Sciences, Arak, Iran.
Nafiseh KhansariFood and Drug Deputy, Arak University of Medical Sciences, Arak, Iran. nafisehkhansari@gmail.com.
Mahdi AbdollahiArak University of Medical Sciences, Arak, Iran.
Behnoosh Ansari AslFood and Drug Deputy, Arak University of Medical Sciences, Arak, Iran.
Sonia AsadiArak University of Medical Sciences, Arak, Iran.
Akbar Yousefi MaghoolDepartment of Chemistry, Faculty of Science, Arak University, Arak, Islamic Republic of Iran.
Arash RabieiDepartment of Chemistry, Faculty of Science, Bu-Ali Sina University, Hamedan, Islamic Republic of Iran.
Ali KhademiDepartment of Chemistry, College of Science, Alborz Campus, University of Tehran, Tehran, 14155-6455, Iran.
Mohsen ShamsiDepartment of Public Health, School of Health, Arak University of Medical Sciences, Arak, Iran.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveThis study assesses the comparative performance of Isotope Ratio Mass Spectrometry (IRMS) and conventional techniques in detecting honey adulteration. Standard analytical methods, in accordance with ISIRI guidelines, were evaluated alongside δ

resultsWhile conventional methods met ISIRI criteria in 18 out of 20 samples, IRMS produced contrasting findings-only 2 samples were identified as genuine and free from adulteration. Statistical evaluation using Prism software revealed a significant discrepancy between the two techniques, with a p-value < 0.05, indicating stronger confidence in IRMS results. The findings highlight the necessity of reconsidering current protocols for honey adulteration detection in order to preserve the purity and credibility of this high-value natural product.

Indexed as

Food ContaminationHoneyMass SpectrometryCarbon IsotopesFuraldehyde5-hydroxymethylfurfuralCarbon IsotopesFuraldehydeAdulterationHoneyIsotope ratio mass spectrometryLiquid chromatography

Identifiers

PMID40400041
PMCPMC12096562

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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.