ArticleFoods (Basel, Switzerland)2024
Honey Adulteration Detection via Ultraviolet-Visible Spectral Investigation Coupled with Chemometric Analysis.
Article in Foods (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Detection of Honey Adulteration Using UV-Vis Absorption Spectroscopy and Chemometrics.Foods (Basel, Switzerland) · 2026Article
- Βotanical Origin Confirmation and Adulteration Testing of Monofloral Honey Using ATR-FTIR Spectroscopy in Combination with Pattern Recognition and Dimension Reduction Techniques.Foods (Basel, Switzerland) · 2026Article
- Modern Fluorescence Strategies for Honey Characterization: Analytical Advances, Emerging Technologies, Methodological Challenges, and Future Perspectives.Foods (Basel, Switzerland) · 2026Review
- Honey Fraud as a Moving Analytical Target: Omics-Informed Authentication Within a Multi-Layer Analytical Framework.Foods (Basel, Switzerland) · 2026Review
- Precise Discrimination Between Rape Honey and Acacia Honey Based on Sugar and Amino Acid Profiles Combined with Machine Learning.Foods (Basel, Switzerland) · 2025Article
- Leveraging RegNet and CBAM for precise detection of honey adulteration using thermal image analysis.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Any change in the composition or physicochemical parameters of honey outside the standardized intervals may be deemed fraud, irrespective of direct introduction of certain substances or feeding honeybees with syrups. Simple and rapid tools along with more sophisticated ones are required to monitor fraudulent practices in the honey trade. In this work, UV-Vis spectroscopy was used to identify and quantify six Romanian honey types (five monofloral and one polyfloral) mixed with commercially available corn syrup, corn syrup with plant extracts, inverted syrup, and fruit syrup at different concentrations (5%, 10%, 20%, 30%, 40%, and 50%). Relevant spectral features were used to develop a neural model, which was able to pinpoint adulteration, regardless of the honey and adulterant type. The proposed model was able to detect adulteration levels higher than 10%, thereby serving as a cost-effective and reliable tool to monitor honey quality.
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Registered trials
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