ArticleHeliyon2023
Ultraviolet-visible spectroscopy combined with machine learning as a rapid detection method to the predict adulteration of honey.
Article in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Detection of Honey Adulteration Using UV-Vis Absorption Spectroscopy and Chemometrics.Foods (Basel, Switzerland) · 2026Article
- Rapid Assessment of Honey Physicochemical Properties by Mid-Infrared Spectroscopy and Chemometric Modeling.ACS omega · 2026Article
- The Role of Artificial Intelligence in Modern Analytical Chemistry: Current Trends and Future Directions.International journal of analytical chemistry · 2026Review
- Application of spectroscopic technology with machine learning in Chinese herbs from seeds to medicinal materials: The case of genusJournal of pharmaceutical analysis · 2025Review
- Detecting Honey Adulteration: Advanced Approach Using UF-GC Coupled with Machine Learning.Sensors (Basel, Switzerland) · 2024Article
- Detection of adulteration in Iranian grape molasses added glucose/fructose/sugar beet syrups withFood science & nutrition · 2024Article
- Comparative study of physiochemical properties in Iranian multi-floral honeys: Local vs. commercial varieties.Heliyon · 2024Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Honey is often adulterated with inexpensive and artificial sweeteners. To overcome the time-consuming honey adulteration tests, which require precision, chemicals, and sample preparation, it is needful to develop trustworthy analytical methods to assure its authenticity. In the present study, the potential of ultraviolet-visible spectroscopy (UV-Vis) in predicting the sucrose content was evaluated by using Support Vector Regression (SVR) and Partial Least Square Regression (PLSR). To predict the sucrose content based on diagnostic wavelengths, a Point Spectro Transfer Function (PSTF) was evaluated using Multiple Linear Regression (MLR). For this purpose, the spectra of authentic (n = 12), commercial (n = 12), and adulterated (n = 16) honey samples were recorded. Four distinguished wavelengths from correlation analysis between sucrose content and spectra absorption were 216, 280, 316, and 603 nm. The SVR performed better calibration model than the PLSR estimations (RMSE = 0.97, and R
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