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Food Contamination

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Deep Learning-Based Quantitative Assessment of Melamine and Cyanuric Acid in Pet Food Using Fourier Transform Infrared Spectroscopy.

Sensors (Basel, Switzerland)
Melamine and its derivative, cyanuric acid, are occasionally added to pet meals because of their nitrogen-rich qualities, leading to the development of several health-related issues. A nondestructive sensing technique that offers effective detection ...

Accelerating the Detection of Bacteria in Food Using Artificial Intelligence and Optical Imaging.

Applied and environmental microbiology
In assessing food microbial safety, the presence of Escherichia coli is a critical indicator of fecal contamination. However, conventional detection methods require the isolation of bacterial macrocolonies for biochemical or genetic characterization,...

Multi-Pesticide Residue Analysis Method Designed for the Robot Experimenters.

Journal of agricultural and food chemistry
Robots replacing humans as the executioners is crucial work for intelligent multi-pesticide residue analysis to maximize reproducibility and throughput while minimizing the expertise required to perform the entire process. Traditional analysis method...

Enhanced artificial intelligence for electrochemical sensors in monitoring and removing of azo dyes and food colorant substances.

Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association
It is necessary to determine whether synthetic dyes are present in food since their excessive use has detrimental effects on human health. For the simultaneous assessment of tartrazine and Patent Blue V, a novel electrochemical sensing platform was d...

Detection of adulteration in mutton using digital images in time domain combined with deep learning algorithm.

Meat science
A novel method based on digital images in time domain combined with convolutional neural network (CNN) is proposed for discrimination and analysis of the adulterated mutton. For this, 195 sample images during the constant temperature heating process ...

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

Scientific reports
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...

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
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...

A Fluorescent Biosensor for Sensitive Detection of Typhimurium Using Low-Gradient Magnetic Field and Deep Learning via Faster Region-Based Convolutional Neural Network.

Biosensors
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...

Establishment of a 13 genes-based molecular prediction score model to discriminate the neurotoxic potential of food relevant-chemicals.

Toxicology letters
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...

Aflatoxin rapid detection based on hyperspectral with 1D-convolution neural network in the pixel level.

Food chemistry
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...