ArticleFoods (Basel, Switzerland)2023
Rapid and Automated Method for Detecting and Quantifying Adulterations in High-Quality Honey Using Vis-NIRs in Combination with Machine Learning.
Article in Foods (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 38 citations in OpenAlex.
- Reliable Quantification of Powdered Ginger Adulteration by Vis-NIR Spectroscopy and Chemometrics.Molecules (Basel, Switzerland) · 2026Article
- The Journey of Artificial Intelligence in Food Authentication: From Label Attribute to Fraud Detection.Foods (Basel, Switzerland) · 2025Review
- Detecting Honey Adulteration: Advanced Approach Using UF-GC Coupled with Machine Learning.Sensors (Basel, Switzerland) · 2024Article
- Honey Adulteration Detection via Ultraviolet-Visible Spectral Investigation Coupled with Chemometric Analysis.Foods (Basel, Switzerland) · 2024Article
- Machine learning assisted biosensing technology: An emerging powerful tool for improving the intelligence of food safety detection.Current research in food science · 2024Review
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
Honey is one of the most adulterated foods, usually through the addition of sweeteners or low-cost honeys. This study presents a method based on visible near infrared spectroscopy (Vis-NIRs), in combination with machine learning (ML) algorithms, for the correct identification and quantification of adulterants in honey. Honey samples from two botanical origins (orange blossom and sunflower) were evaluated and adulterated with low-cost honey in different percentages (5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, and 50%). The results of the exploratory analysis showed a tendency to group the samples according to botanical origin, as well as the presence of adulteration. A supervised analysis was performed to detect the presence of adulterations. The best performance with 100% accuracy was achieved by support vector machines (SVM) and random forests (RF). A regression study was also carried out to quantify the percentage of adulteration. The best result was obtained by support vector regression (SVR) with a coefficient of determination (R
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Registered trials
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