AIMC Topic: Food Contamination

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Integrated deep eutectic solvent with amorphous metal-organic framework for highly sensitive electrochemical determination of dicofol in milk and water.

Food chemistry
High-throughput screening of deep eutectic solvents (DESs) was performed using artificial intelligence/quantum mechanical models. Ni-amorphous metal-organic frameworks (aMOFs) was synthesized through amine-DES aqueous solution. The target-specific DE...

Escherichia coli O157:H7 survival and transfer dynamics on cold chain packaging materials: An integrated experimental-machine learning framework.

International journal of food microbiology
This study presents a comprehensive investigation of Escherichia coli O157:H7 survival and transfer on six cold chain packaging materials through experimental characterization and machine learning modeling. Survival experiments revealed significant m...

Current trends and perspectives on the roles of multi-analytical methodologies in spirit authentication: Perspectives, challenges, and opportunities.

Food chemistry
The persistent occurrence of economically motivated food frauds and adulterations, causing substantial economic losses to enterprises and endangering consumers interests, critically impedes the sustainable development of the food industry. Spirits, c...

Recent Development of Methods and Techniques in the Detection of Mycotoxins in Agricultural Products.

Journal of agricultural and food chemistry
Mycotoxins are produced by fungi and possess cytotoxic properties that cause extensive cellular damage. Mycotoxins pose a significant threat to the harvesting and storage of crops as well as potential carcinogenic, teratogenic, and mutagenic risks to...

Proteomics coupled machine learning-innovative approach in geographical origin authentication of green Coffea arabica.

Food chemistry
The geographical authentication of green specialty coffee is an economically sensitive analytical task that is not yet fully resolved. We used an innovative combination of proteomic profiling with linear discriminant analysis for the authentication o...

Machine learning-enhanced SERS detection of melamine and its analogues in non-pretreated milk via filter-pressing assembled polytetrafluoroethylene-AgNPs substrate.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Melamine contamination from illegal additives, packaging contaminants, and pesticide residues threatens dairy product safety, demanding rapid detection. Traditional methods such as chromatography or mass spectrometry are precise but lack field applic...

Identification of syrup adulteration in wolfberry honey using CNN-CBAM-SVM combined with H NMR.

Food chemistry
To identify syrup adulteration in honey, a deep learning model based on the CNN-CBAM-SVM architecture combined with H NMR spectra was developed. The traditional CNN model was enhanced by incorporating the CBAM module and replacing the fully connected...

A Sn-Ta-O-doped vertical graphene electrochemical sensor based on a machine learning prediction model for monitoring cadmium in beverages.

Food chemistry
The growing diversity of beverages intensifies demand for on-site heavy metal detection to ensure safety. Multi-heteroatom co-doped graphene electrodes exhibit superior electrocatalytic activity and stability over traditional carbon-based electrodes,...

Smart Detection of Food Spoilage Using Microbial Volatile Compounds: Technologies, Challenges, and Future Outlook.

Journal of agricultural and food chemistry
Microbial volatile organic compounds (MVOCs) serve as early, noninvasive indicators of food spoilage and microbial contamination. This review critically assesses current methods for MVOC detection, including gas chromatography-mass spectrometry (GC-M...

Machine learning for polycyclic aromatic hydrocarbons analysis in roasted lamb: new insights from spectral and chemical data.

Food chemistry
Polycyclic aromatic hydrocarbons (PAHs) generated during lamb roasting pose health risks but are difficult to predict due to their low concentrations and complex features. Existing models fail to address data scarcity and low-concentration prediction...