Honey adulteration poses a huge challenge with considerable health and economic consequences, underscoring the necessity for effective and precise quality evaluation techniques. This research introduces a novel approach for classifying levels of hone...
Analytical methods : advancing methods and applications
Jul 31, 2025
The botanical origin of honey significantly impacts its nutritional composition, quality, and price. Traditional identification methods are often complex, require expensive equipment, and are time-consuming. This article proposes a rapid detection me...
To identify syrup adulteration in honey, a deep learning model based on the CNN-CBAM-SVM architecture combined with H NMR spectra was developed. The traditional CNN model was enhanced by incorporating the CBAM module and replacing the fully connected...
Honey is one of the most frequently frauded foods due to the high market price of certain kinds of monofloral honey. Traditional authentication methods involving pollen or targeted analysis have limitations that can be manipulated by fraudsters. Nont...
To determine the authenticity of honey, a deep learning network based on the Canny-GoogLeNet architecture combined with three-dimensional (3D) fluorescence spectroscopy was established. The canny edge detection algorithm was used to extract 3D spectr...
This study presents a simple approach for detecting honey adulteration by integrating calorimetric data from differential scanning calorimetry (DSC) with machine learning classification (MLC) techniques, specifically using convolutional neural networ...
Honey is a valuable natural food product, prized for its nutritional and therapeutic properties. However, the widespread issue of honey adulteration, often involving the addition of plant-based syrups, poses significant challenges to global markets. ...
Food research international (Ottawa, Ont.)
Feb 25, 2025
This study explores the use of near-infrared (NIR), mid-infrared (MIR), and Raman spectral fusion for the rapid prediction of floral origins and main taste components in Apis cerana (A. cerana) honey. Feature-level fusion with the partial least squar...
Stingless bee honey is emerging as a superfood, given its enhanced health and therapeutic benefits. In this paper, we used handheld X-ray fluorescence spectroscopy (hXRF) with machine learning techniques to classify Philippine honey based on its ento...
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 abs...
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