AIMC Topic: Food Contamination

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Origin traceability of agricultural products: A lightweight collaborative neural network for spectral information processing.

Food research international (Ottawa, Ont.) Mar 6, 2025
The natural conditions of various regions, including climate, soil, and water quality, significantly influence the nutrient composition and quality of agricultural products. Identifying the origin of agricultural products can prevent adulteration, im...
Oryza Crops, Agricultural Food Quality Arachis Food Contamination Food Analysis Neural Networks, Computer Agriculture
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Prediction of the packaging chemical migration into food and water by cutting-edge machine learning techniques.

Scientific reports Mar 6, 2025
Chemicals transfer from the packaging materials and their dissolution in food and water can create health risks. Due to the costly and time-intensive nature of experimental measurements, employing artificial intelligence (AI) methodologies is benefic...
Algorithms Water Food Packaging Neural Networks, Computer Machine Learning Food Contamination
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A Surface-Enhanced Raman Spectroscopy Platform Integrating Dual Signal Enhancement and Machine Learning for Rapid Detection of Veterinary Drug Residues in Meat Products.

ACS applied materials & interfaces Mar 2, 2025
The detection and quantification of veterinary drug residues in meat remain a significant challenge due to the complex background interference inherent to the meat matrix, which compromises the stability and accuracy of spectroscopic analysis. This s...
Food Contamination Metal Nanoparticles Drug Residues Veterinary Drugs Meat Products Animals Machine Learning Spectrum Analysis, Raman
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Measuring the Level of Aflatoxin Infection in Pistachio Nuts by Applying Machine Learning Techniques to Hyperspectral Images.

Sensors (Basel, Switzerland) Mar 2, 2025
This paper investigates the use of machine learning techniques on hyperspectral images of pistachios to detect and classify different levels of aflatoxin contamination. Aflatoxins are toxic compounds produced by moulds, posing health risks to consume...
Hyperspectral Imaging Pistacia Aflatoxins Food Contamination Nuts Machine Learning
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Machine Learning-Assisted Chemical Tongues Based on Dual-channel Inclusion Complexes for Rapid Identification of Nonsteroidal Anti-inflammatory Drugs in Food.

ACS sensors Feb 24, 2025
The improper application of nonsteroidal anti-inflammatory drugs (NSAIDs) presents significant health hazards via vector food contamination. A critical limitation of these traditional existing approaches is their inability to concurrently discern and...
Spectrometry, Fluorescence Milk Imidazoles Anti-Inflammatory Agents, Non-Steroidal Bridged-Ring Compounds Machine Learning Food Contamination Imidazolidines Food Analysis Animals Macrocyclic Compounds Heterocyclic Compounds, 2-Ring
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Ultrafast on-site adulteration detection and quantification in Asian black truffle using smartphone-based computer vision.

Talanta Feb 14, 2025
Asian black truffle Tuber sinense (BT) is a premium edible fungus with medicinal value, but it is often prone to adulteration. This study aims to develop a fast, non-destructive, automatic, and intelligent method for identifying BT. A novel lightweig...
Ascomycota Smartphone Food Contamination Neural Networks, Computer
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Enhanced authentication of organic milk using MALDI-TOF MS with combined lipid-peptide fingerprinting and machine learning integration.

Food chemistry Feb 10, 2025
This study introduces a method for authenticating organic milk and determining its geographic region using matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS). Both lipids and peptides were analyzed, and their ...
Peptides Cattle Food Contamination Machine Learning Animals Milk Food, Organic Peptide Mapping Lipids Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization
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Initializing a Public Repository for Hosting Benchmark Datasets to Facilitate Machine Learning Model Development in Food Safety.

Journal of food protection Feb 6, 2025
While there is clear potential for artificial intelligence (AI) and machine learning (ML) models to help improve food safety, the development and deployment of these models in the food safety domain are by and large lacking. The absence of publicly a...
Soil Microbiology Food Contamination Benchmarking Food Safety Food Microbiology Databases, Factual Animals Chickens Humans Machine Learning
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Cost-efficient training of hyperspectral deep learning models for the detection of contaminating grains in bulk oats by fluorescent tagging.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy Feb 4, 2025
Computer vision based on instance segmentation deep learning models offers great potential for automating many visual inspection tasks, such as the detection of contaminating grains in bulk oats, a nutrient rich grain which is well-tolerated by peopl...
Food Contamination Fluorescent Dyes Hyperspectral Imaging Avena Deep Learning Edible Grain
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Real-Time Classification of Ochratoxin a Contamination in Grapes Using AI-Enhanced IoT.

Sensors (Basel, Switzerland) Jan 28, 2025
Ochratoxin A (OTA) contamination presents significant risks in viticulture, affecting the safety and quality of wine and grape-derived products. This study introduces a groundbreaking method for early detection and management of OTA, leveraging envir...
Ochratoxins Wine Artificial Intelligence Vitis Food Contamination
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