AIMC Topic: Honey

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Robust Multiclass Feature Selection for the Authentication of Honey Botanical Origin via Nontargeted LC-MS Analysis.

Analytical chemistry Jun 11, 2025
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...
Machine Learning Chromatography, Liquid Liquid Chromatography-Mass Spectrometry Honey Support Vector Machine Mass Spectrometry
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Robust DEEP heterogeneous ensemble and META-learning for honey authentication.

Food chemistry Mar 22, 2025
Food fraud raises significant concerns to consumer health and economic integrity, with the adulteration of honey by sugary syrups representing one of the most prevalent forms of economically motivated adulteration. This study presents a novel framewo...
Food Contamination Saccharum Honey Deep Learning
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A machine learning approach fusing multisource spectral data for prediction of floral origins and taste components of Apis cerana honey.

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...
Animals Spectroscopy, Near-Infrared Machine Learning Amino Acids Honey Taste Spectrum Analysis, Raman Least-Squares Analysis Sugars Flowers Bees
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Identifying bee species origins of Philippine honey using X-ray fluorescence elemental analysis coupled with machine learning.

Food chemistry Feb 1, 2025
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...
Bees Machine Learning Spectrometry, X-Ray Emission Animals Philippines Honey
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Detecting Honey Adulteration: Advanced Approach Using UF-GC Coupled with Machine Learning.

Sensors (Basel, Switzerland) Nov 23, 2024
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...
Honey Chromatography, Gas Support Vector Machine Food Contamination Machine Learning
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Fluorescence spectroscopy combined with multilayer perceptron deep learning to identify the authenticity of monofloral honey-Rape honey.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy Nov 10, 2024
Honey authenticity is critical to honey quality. The development of a quick, easy, and non-destructive technique for determining the authenticity of honey encourages an improvement in honey quality. Here, the authenticity of monofloral honey-rape hon...
Honey Deep Learning Spectrometry, Fluorescence Neural Networks, Computer
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Machine Learning-Based Nanozyme Sensor Array as an Electronic Tongue for the Discrimination of Endogenous Phenolic Compounds in Food.

Analytical chemistry Sep 26, 2024
The detection of endogenous phenolic compounds (EPs) in food is of great significance in elucidating their bioactivity and health effects. Here, a novel bifunctional vanillic acid-Cu (VA-Cu) nanozyme with peroxidase-like and laccase-like activities w...
Vanillic Acid Neural Networks, Computer Honey Laccase Food Analysis Phenols Peroxidase Fruit and Vegetable Juices Machine Learning Nanostructures Tea Copper Electronic Nose Benzidines
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Investigating the impact of climate variables on the organic honey yield in Turkey using XGBoost machine learning.

Journal of the science of food and agriculture Aug 9, 2024
BACKGROUND: The Turkish organic honey industry, a major player in the global market, faces challenges due to climate fluctuations. Understanding the influence of climate factors on honey production is vital for sustainable farming and economic stabil...
Beekeeping Humidity Organic Agriculture Bees Climate Change Machine Learning Turkey Temperature Animals Honey Wind
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High-resolution proteomics and machine-learning identify protein classifiers of honey made by Sicilian black honeybees (Apis mellifera ssp. sicula).

Food research international (Ottawa, Ont.) Aug 8, 2024
Apis mellifera ssp. sicula, also known as the Sicilian black honeybee, is a Slow Food Presidium that produces honey with outstanding nutraceutical properties, including high antioxidant capacity. In this study, we used high-resolution proteomics to p...
Insect Proteins Machine Learning Honey Proteomics Animals Bees Proteome
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Application of untargeted liquid chromatography-mass spectrometry to routine analysis of food using three-dimensional bucketing and machine learning.

Scientific reports Jul 18, 2024
For the detection of food adulteration, sensitive and reproducible analytical methods are required. Liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS) is a highly sensitive method that can be used to obtain analytical finger...
Honey Food Analysis Machine Learning Liquid Chromatography-Mass Spectrometry Food Contamination
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