AIMC Topic: Food Contamination

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Combining deep learning and fluorescence imaging to automatically identify fecal contamination on meat carcasses.

Scientific reports Feb 14, 2022
Food safety and foodborne diseases are significant global public health concerns. Meat and poultry carcasses can be contaminated by pathogens like E. coli and salmonella, by contact with animal fecal matter and ingesta during slaughter and processing...
Feces Chickens Escherichia coli Deep Learning Salmonella Abattoirs Food Safety Optical Imaging Food Contamination Food Analysis Meat Animals
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A machine learning-driven approach for prioritizing food contact chemicals of carcinogenic concern based on complementary in silico methods.

Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association Jan 1, 2022
Carcinogenicity is one of the most critical endpoints for the risk assessment of food contact chemicals (FCCs). However, the carcinogenicity of FCCs remains insufficiently investigated. To fill the data gap, the application of standard experimental m...
Computer Simulation Food Contamination Food Analysis Carcinogens Machine Learning
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A Fluorescent Biosensor for Sensitive Detection of Typhimurium Using Low-Gradient Magnetic Field and Deep Learning via Faster Region-Based Convolutional Neural Network.

Biosensors Nov 11, 2021
In this study, a fluorescent biosensor was developed for the sensitive detection of typhimurium using a low-gradient magnetic field and deep learning via faster region-based convolutional neural networks (R-CNN) to recognize the fluorescent spots on...
Magnetic Fields Neural Networks, Computer Deep Learning Biosensing Techniques Food Contamination Salmonella typhimurium
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Establishment of a 13 genes-based molecular prediction score model to discriminate the neurotoxic potential of food relevant-chemicals.

Toxicology letters Nov 5, 2021
Although many neurotoxicity prediction studies of food additives have been developed, they are applicable in a qualitative way. We aimed to develop a novel prediction score that is described quantitatively and precisely. We examined cell viability, r...
Neural Networks, Computer Machine Learning Reproducibility of Results Humans Reactive Oxygen Species Cell Survival Calcium Risk Factors Cell Line, Tumor Food Contamination Food Additives Neural Stem Cells Central Nervous System Diseases Gene Expression Regulation
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Aflatoxin rapid detection based on hyperspectral with 1D-convolution neural network in the pixel level.

Food chemistry Apr 29, 2021
Aflatoxin is commonly exists in moldy foods, it is classified as a class one carcinogen by the World Health Organization. In this paper, we used one dimensional convolution neural network (1D-CNN) to classify whether a pixel contains aflatoxin. First...
Food Analysis Hyperspectral Imaging Aflatoxins Image Processing, Computer-Assisted Food Contamination Humans Neural Networks, Computer
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Raman spectroscopy combined with machine learning for rapid detection of food-borne pathogens at the single-cell level.

Talanta Feb 5, 2021
Rapid detection of food-borne pathogens in early food contamination is a permanent topic to ensure food safety and prevent public health problems. Raman spectroscopy, a label-free, highly sensitive and dependable technology has attracted more and mor...
Prospective Studies Machine Learning Bacteria Food Contamination Spectrum Analysis, Raman
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Pattern recognition based on machine learning identifies oil adulteration and edible oil mixtures.

Nature communications Oct 23, 2020
Previous studies have shown that each edible oil type has its own characteristic fatty acid profile; however, no method has yet been described allowing the identification of oil types simply based on this characteristic. Moreover, the fatty acid prof...
Machine Learning Fatty Acids Food Safety Food Contamination Plant Oils
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Improved Antibiotic Detection in Raw Milk Using Machine Learning Tools over the Absorption Spectra of a Problem-Specific Nanobiosensor.

Sensors (Basel, Switzerland) Aug 14, 2020
In this article we present the development of a biosensor system that integrates nanotechnology, optomechanics and a spectral detection algorithm for sensitive quantification of antibiotic residues in raw milk of cow. Firstly, nanobiosensors were des...
Metal Nanoparticles Limit of Detection Milk Drug Residues Biosensing Techniques Food Contamination Gold Animals Machine Learning Aptamers, Nucleotide Anti-Bacterial Agents
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Analysis of phthalate plasticizer migration from PVDC packaging materials to food simulants using molecular dynamics simulations and artificial neural network.

Food chemistry Feb 22, 2020
Based on the experimental data of gas chromatography-mass spectrometry, an improved artificial neural network was first established to predict the migration of 2-ethylhexyl phthalate (DEHP) plasticizer from poly(vinylidene chloride) (PVDC) into food ...
Food Contamination Diethylhexyl Phthalate Molecular Dynamics Simulation Neural Networks, Computer Solubility Temperature Gas Chromatography-Mass Spectrometry Plasticizers Food Packaging Polyvinyl Chloride
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Rapid detection of adulteration of minced beef using Vis/NIR reflectance spectroscopy with multivariate methods.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy Jan 14, 2020
High economic returns induce the continuous occurrence of meat adulteration. In this study, visible/near-infrared (Vis/NIR) reflectance spectroscopy with multivariate methods was used for the rapid detection of adulteration in minced beef. First, the...
Food Contamination Neural Networks, Computer Spectroscopy, Near-Infrared Meat Animals Red Meat Cattle Principal Component Analysis Support Vector Machine
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