Detection of Honey Adulteration Using UV-Vis Absorption Spectroscopy and Chemometrics.

Aya Ibrahim, Mohamed O Amin, Bessy D'Cruz, Bhavik Vyas, Igor K Lednev, Entesar Al-Hetlani

Abstract read

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Article in Foods (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

6 authors.

Aya IbrahimDepartment of Chemistry, College of Science, Kuwait University, Sabah Al Salem University City, P.O. Box 5969, Safat 13060, Shadadiya, Kuwait.

Mohamed O AminDepartment of Chemistry, University at Albany, State University of New York, 1400 Washington Avenue, Albany, NY 12222, USA.ORCID 0000-0003-1305-7746

Bessy D'CruzDepartment of Chemistry, College of Science, Kuwait University, Sabah Al Salem University City, P.O. Box 5969, Safat 13060, Shadadiya, Kuwait.

Bhavik VyasDepartment of Chemistry, University at Albany, State University of New York, 1400 Washington Avenue, Albany, NY 12222, USA.ORCID 0009-0001-3214-3749

Igor K LednevDepartment of Chemistry, University at Albany, State University of New York, 1400 Washington Avenue, Albany, NY 12222, USA.ORCID 0000-0002-6504-531X

Entesar Al-HetlaniDepartment of Chemistry, College of Science, Kuwait University, Sabah Al Salem University City, P.O. Box 5969, Safat 13060, Shadadiya, Kuwait.ORCID 0000-0003-2281-6257

Funding

Kuwait University SC09/24

6 · The paper itself

Abstract

This preliminary study demonstrates the use of ultraviolet-visible (UV-vis) absorption spectroscopy coupled with chemometric modeling to detect and quantify honey adulteration. UV-vis spectra of pure honey and samples experimentally adulterated with corn, agave, and date syrups were collected within the 200-500 nm range after their simple dilution with water. Orthogonal partial least squares discriminant analysis (OPLS-DA) distinguished pure honey from adulterated samples, achieving 88% accuracy on an external validation dataset. A second set of classification models differentiated between honey containing a single adulterant and that containing multiple adulterants, with a high prediction rate based on an external validation dataset. Quantitative analysis of adulterant content via orthogonal partial least squares regression analysis revealed the strong predictive performance of the models (R

Indexed as

chemometricsfood fraudhoney adulterationhoney authenticationsugar syrupsUV–vis spectroscopy

Identifiers

PMID42737200

DOI10.3390/foods15172953

PMCPMC13565015

What OpenQuestion holds

Textmetadata

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.