ArticleFoods (Basel, Switzerland)2025
Honey Differentiation Using Infrared and Raman Spectroscopy Analysis and the Employment of Machine-Learning-Based Authentication Models.
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 4 papers.
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Who cites it
4 citing papers in PubMed.
- HS-SPME-GC-MS Coupled with Chemometrics for Detecting HFCS and Invert Sugar Adulteration in Coriander Honey.Foods (Basel, Switzerland) · 2026Article
- Honey adulteration detection using the HS-SPME-IMS technique combined with chemometric analysis.Food chemistry: X · 2025Article
- Data-Driven FTIR Spectroscopy for the Discrimination of Nectars.Molecules (Basel, Switzerland) · 2025Article
- Deep Learning-Enhanced Spectroscopic Technologies for Food Quality Assessment: Convergence and Emerging Frontiers.Foods (Basel, Switzerland) · 2025Review
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3 authors.
Funding
Abstract
Due to rising concerns regarding the adulteration and mislabeling of honey, new directives at the European level encourage researchers to develop reliable honey authentication models based on rapid and cost-effective analytical techniques, such as vibrational spectroscopies. The present study discusses the identification of the main vibrational bands of the FT-Raman and ATR-IR spectra of the most consumed honey varieties in Transylvania: acacia, honeydew, and rapeseed, exposing the ways the spectral fingerprint differs based on the honey's varietal-dependent composition. Additionally, a pilot study on honey authentication describes a new methodology of processing the combined vibrational data with the most efficient machine learning algorithms. By employing the proposed methodology, the developed model was capable of distinguishing honey produced in a narrow geographical region (Transylvania) with an accuracy of 85.2% and 93.8% on training and testing datasets when the
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