AIMC Topic: Food Contamination

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Ag@CDS SERS substrate coupled with lineshape correction algorithm and BP neural network to detect thiram in beverages.

Talanta Nov 18, 2024
Surface enhanced Raman scattering (SERS) has been proved an effective analytical technique due to its high sensitivity, however, how to identify and extract useful information from raw SERS spectra is still a problem that needs to be resolved. In thi...
Silver Beverages Fruit and Vegetable Juices Neural Networks, Computer Food Contamination Malus Algorithms Metal Nanoparticles Spectrum Analysis, Raman Thiram
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Recommendations for the Development of Artificial Intelligence Applications for the Retail Level.

Journal of food protection Nov 13, 2024
Some of the early applications of artificial intelligence (AI) for food safety appear to be intended for use at the level of manufacturing and distribution. Artificial intelligence applications to facilitate foodborne illness outbreak investigations,...
Food Safety Artificial Intelligence Humans Food Contamination Foodborne Diseases
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Enhanced food authenticity control using machine learning-assisted elemental analysis.

Food research international (Ottawa, Ont.) Nov 13, 2024
With the increasing attention being paid to the authenticity of food, efficient and accurate techniques that can solve relevant problems are crucial for improving public trust in food. This review explains two main aspects of food authenticity, namel...
Metals, Heavy Food Contamination Food, Organic Food Analysis Food Quality Quality Control Machine Learning Algorithms
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Milk adulteration identification using hyperspectral imaging and machine learning.

Journal of dairy science Nov 8, 2024
Milk adulteration poses a global concern, with developing countries facing higher risks due to unsatisfactory monitoring systems and policies. Surprisingly, this common issue has often been overlooked in many countries. Contrary to popular belief, ad...
Milk Hyperspectral Imaging Animals Cattle Food Contamination Machine Learning
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Carcinogenic and non-carcinogenic risks caused by rice contamination with heavy metals and their effect on the prevalence of cardiovascular disease (Using machine learning).

Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association Nov 7, 2024
INTRODUCTION: The safety and health of food products are essential in the food industry, and the risk of contamination from various contaminants must be evaluated. Exposure to HMs from the environment (especially food) causes various adverse effects ...
Humans Machine Learning Female Aged Cohort Studies Food Contamination Oryza Metals, Heavy Cardiovascular Diseases Adult Carcinogens Middle Aged Prevalence Male
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A novel four-modal nano-sensor based on two-dimensional Mxenes and fully connected artificial neural networks for the highly sensitive and rapid detection of ochratoxin A.

Talanta Nov 5, 2024
Timely and accurate on-site detection of ochratoxin A (OTA) is extremely important for global public health. In this study, a fluorescence/colorimetric biosensor based on TiC nano-materials (TiC-NMS) and a machine-learning (ML) based fluorescence/col...
Titanium Nanostructures DNA, Single-Stranded Food Contamination Ochratoxins Neural Networks, Computer Limit of Detection Spectrometry, Fluorescence Colorimetry Biosensing Techniques
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Detection of mussels contaminated with cadmium by near-infrared reflectance spectroscopy based on RELS-TSVM.

Journal of food science Nov 4, 2024
Eating mussels contaminated with cadmium (Cd) can seriously harm health. In this study, a non-destructive and rapid detection method for Cd-contaminated mussels based on near-infrared reflectance spectroscopy was studied. The spectral data of Cd-cont...
Support Vector Machine Food Contamination Least-Squares Analysis Animals Spectroscopy, Near-Infrared Shellfish Bivalvia Cadmium
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Machine learning driven metal oxide-based portable sensor array for on-site detection and discrimination of mycotoxins in corn sample.

Food chemistry Nov 1, 2024
Cereals, grains, and feedstuffs are prone to contamination by fungi during various stages from growth to storage. These fungi may produce harmful mycotoxins impacting food quality and safety. Thus, the development of quick and reliable methods for on...
Oxides Discriminant Analysis Zea mays Food Contamination Limit of Detection Mycotoxins Machine Learning Metals
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Decoding wheat contamination through self-assembled whole-cell biosensor combined with linear and non-linear machine learning algorithms.

Biosensors & bioelectronics Oct 21, 2024
The contamination of mycotoxins is a serious problem around the world. It has detrimental effects on human beings and leads to tremendous economic loss. It is essential to develop a rapid and non-destructive method for contamination recognition parti...
Machine Learning Algorithms Biosensing Techniques Humans Neural Networks, Computer Triticum Support Vector Machine Mycotoxins Fungi Food Contamination Least-Squares Analysis
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Machine learning models to predict the bioaccessibility of parent and substituted polycyclic aromatic hydrocarbons (PAHs) in food: Impact on accurate health risk assessment.

Journal of hazardous materials Oct 9, 2024
Food intake is the primary pathway for polycyclic aromatic hydrocarbons (PAHs) to enter the human body. Once ingested, PAHs tend to accumulate, posing health risks. To accurately assess the risk of PAHs from food, concentrations of 10 parent PAHs (PP...
Animals Shellfish Fishes Humans Risk Assessment Seafood Machine Learning Food Contamination Polycyclic Aromatic Hydrocarbons
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