Assessing the authenticity of honey is a serious problem that has gained much interest internationally because honey has frequently been subject to various fraudulent practices, including mislabelling of botanical and geographical origin and mixing with sugar syrups or honey of lower quality. To protect the health of consumers and avoid competition, which could create an unstable market, consumers, beekeepers and regulatory bodies are interested in having reliable analytical methodologies to detect non-compliant honey. This paper gives an overview of the different approaches used to assess the authenticity of honey, specifically by the application of advanced instrumental techniques, including spectrometric, spectroscopic and chromatographic methods coupled with chemometric interpretation of the data. Recent development in honey analysis and application of the honey authentication process in the Romanian context are highlighted, and future trends in the process of detecting and eliminating fraudulent practices in honey production are discussed.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1016/j.foodchem.2019.125595 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Volatile markers for unifloral safflower honey: An untargeted and targeted metabolomics analysis.
Food Chem
December 2024
Key Laboratory of Dairy Science, Ministry of Education, Department of Food Science, Northeast Agricultural University, Harbin 150030, PR China. Electronic address:
The volatile markers and aroma properties of unifloral safflower honey in Xinjiang, China were identified for the authentication. An untargeted metabolomics analysis was performed to compare the volatile components in safflower honey with those in four other unifloral honey and the nectar plants of safflower honey through headspace solid-phase microextraction-chromatography-mass spectrometry. Tentative markers, including benzaldehyde, longifolene, and cedrol, were comprehensively screened through variable importance in projection based on orthogonal partial least-squares discrimination analysis, nectar origin volatile components analysis, and odor characteristics analysis.
View Article and Find Full Text PDFDistinguishing the botanical origins of rare honey through untargeted metabolomics and machine learning interpreting flavonoid profiles.
Food Chem
January 2025
State Key Laboratory of Resource Insects, Institute of Apiculture Research, Chinese Academy of Agricultural Sciences, Beijing 100093, China. Electronic address:
Distinguishing the botanic origins of monofloral honey is the foremost concern in ensuring its authentication. In this work, an innovative, green, and comprehensive approach was developed to distinguish the botanic origins of four types of rare honey, and the strategy involved in the following aspects: Based on theoretical design, suitable natural deep eutectic solvent (NADES) was screened to extract flavonoids from honey samples; after NADES extracts were directly analyzed by high-resolution mass spectrometry, the discrimination models of monofloral honey were established by untargeted metabolomics combined with machine learning. Based on the comparison of various models, the Random Forest algorithm had higher prediction accuracy for four types of monofloral honey, and characteristic compounds for each rare monofloral honey were screened based on SHapley Additive exPlanations values.
View Article and Find Full Text PDFIdentification of 3-methoxytyramine as a specific biomarker for beet-sugar-fed honey: A two year surveillance study in South Korea.
Food Res Int
January 2025
New Hazardous Substances Division, National Institute of Food and Drug Safety Evaluation, Ministry of Food and Drug Safety, Osong, Cheongju, Chungcheongbuk-do 28159, Republic of Korea. Electronic address:
Honey is highly vulnerable to food fraud, and there are growing concerns about product authenticity. The commonly used stable carbon isotope ratios in the Calvin (C3) and Hatch-Slack (C4) photosynthesis cycles in plant feed cannot distinguish between beet-sugar-fed honey and natural honey. However, 3-methoxytyramine (3-MT) can be used as specific biomarker for identifying adulteration of beet-sugar-fed honey.
View Article and Find Full Text PDFDetecting Honey Adulteration: Advanced Approach Using UF-GC Coupled with Machine Learning.
Sensors (Basel)
November 2024
Department of Analytical Chemistry, Faculty of Sciences, University of Cadiz, Agrifood Campus of International Excellence (ceiA3), IVAGRO, 11510 Puerto Real, Spain.
This article introduces a novel approach to detecting honey adulteration by combining ultra-fast gas chromatography (UF-GC) with advanced machine learning techniques. Machine learning models, particularly support vector regression (SVR) and least absolute shrinkage and selection operator (LASSO), were applied to predict adulteration in orange blossom (OB) and sunflower (SF) honeys. The SVR model achieved R values above 0.
View Article and Find Full Text PDFAdvances in infrared spectroscopy and chemometrics for honey analysis: a comprehensive review.
Crit Rev Food Sci Nutr
December 2024
Department of Chemistry and Biochemistry, Faculty of Arts and Sciences, Holy Spirit University of Kaslik, Jounieh, Lebanon.
Article Synopsis
- Honey analysis is important for ensuring quality and authenticity, but traditional methods can be slow and complicated.
- Infrared spectroscopy offers a fast and effective alternative for honey analysis, particularly when combined with multivariate analysis.
- This paper reviews recent studies on using mid- and near-infrared spectroscopy to assess honey's properties, including antioxidant activity, adulteration detection, and origin determination.
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!