Evidence map›Paper›PMID 40646986›Full record

ArticleFoods (Basel, Switzerland)2025

Italian Honeydew Honey Characterization by

Dalila Iannone, Laura Ruth Cagliani, Roberto Consonni

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. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

3 authors.

Dalila IannoneInstitute of Chemical Sciences and Technologies "G. Natta" (SCITEC), National Research Council, Via Corti 12, 20133 Milan, Italy.
Laura Ruth CaglianiInstitute of Chemical Sciences and Technologies "G. Natta" (SCITEC), National Research Council, Via Corti 12, 20133 Milan, Italy.
Roberto ConsonniInstitute of Chemical Sciences and Technologies "G. Natta" (SCITEC), National Research Council, Via Corti 12, 20133 Milan, Italy.ORCID 0000-0003-0184-2384

Funding

European Union Next-GenerationEU CN00000022
6 · The paper itself

Abstract

Honeydew honey represents a bee-derived product with different organoleptic characteristics and distinct properties with respect to floral honey. The market interest in honeydew honey has been growing in recent years due to its higher bioactive characteristics with respect to floral honey. The need for a deeper chemical characterization aimed to evaluate a possible botanical differentiation attracted the use of different analytical approaches. The present work aims to distinguish the botanical honeydew origin by using Nuclear Magnetic Resonance (NMR) spectroscopy and a multivariate approach. Two different data pretreatments have been considered to obtain the best sample discrimination. The saccharide content significantly affects the differentiation of the botanical variety consisting of fir, oak, citrus fruits, eucalyptus, and forest mainly by using a classification approach taking advantage of the Orthogonal Signal Correction filters. Notwithstanding the botanical diversity of the honeydew honey (HDH) samples, fir honeydew (F-HDH), oak honeydew (O-HDH), and eucalyptus honeydew (E-HDH) resulted always well discriminated among all the botanical varieties investigated, while citrus fruits honeydew (CF-HD) and forest honeydew (FO-HDH) did not. In particular, F-HDH resulted characterized by sucrose, erlose, maltose, maltotriose, maltotetraose, and melezitose, E-HDH resulted enriched in α, β-glucose and β-fructose in furanosidic form, and O-HDH enriched in β-fructose in furanosidic form, isomaltose.

Indexed as

botanical originhoneydew honeymultivariate statistical analysisNMR spectroscopy

Identifiers

PMID40646986
PMCPMC12249152

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.