ArticleMolecules (Basel, Switzerland)2021
Origin Identification of Hungarian Honey Using Melissopalynology, Physicochemical Analysis, and Near Infrared Spectroscopy.
Article in Molecules (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Carbohydrate and enzymatic activity profiling for the quality assessment of Canadian honeys.Food chemistry: X · 2026Article
- Influence of Ecological Zones and Honey Bee Morphometric Traits on the Physicochemical Properties of Honey in Kazakhstan.Foods (Basel, Switzerland) · 2026Article
- Melissopalynological and Physicochemical Analysis of Selected Honey Samples from Romania, Spain, Malaysia, and New Zealand.Foods (Basel, Switzerland) · 2026Article
- Informative Wavelength Selection for Evaluation of Bacterial Spoilage in Raw Salmon (Foods (Basel, Switzerland) · 2025Article
- The Use of Fluorescence Spectrometry Combined with Statistical Tools to Determine the Botanical Origin of Honeys.Foods (Basel, Switzerland) · 2024Article
- Portable NIR Spectroscopy to Simultaneously Trace Honey Botanical and Geographical Origins and Detect Syrup Adulteration.Foods (Basel, Switzerland) · 2024Article
- Article
- Rapid analysis technologies with chemometrics for food authenticity field: A review.Current research in food science · 2024Review
- Evaluation of the Antioxidant Activities and Phenolic Profile of ShennongjiaMolecules (Basel, Switzerland) · 2023Article
- Emerging applications of metabolomics in food science and future trends.Food chemistry: X · 2022Article
- Miniaturized NIR Spectroscopy in Food Analysis and Quality Control: Promises, Challenges, and Perspectives.Foods (Basel, Switzerland) · 2022Review
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5 authors.
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Abstract
The objective of the study was to check the authenticity of Hungarian honey using physicochemical analysis, near infrared spectroscopy, and melissopalynology. In the study, 87 samples from different botanical origins such as acacia, bastard indigo, rape, sunflower, linden, honeydew, milkweed, and sweet chestnut were collected. The samples were analyzed by physicochemical methods (pH, electrical conductivity, and moisture), melissopalynology (300 pollen grains counted), and near infrared spectroscopy (NIRS:740-1700 nm). During the evaluation of the data PCA-LDA models were built for the classification of different botanical and geographical origins, using the methods separately, and in combination (low-level data fusion). PC number optimization and external validation were applied for all the models. Botanical origin classification models were >90% and >55% accurate in the case of the pollen and NIR methods. Improved results were obtained with the combination of the physicochemical, melissopalynology, and NIRS techniques, which provided >99% and >81% accuracy for botanical and geographical origin classification models, respectively. The combination of these methods could be a promising tool for origin identification of honey.
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