AIMC Topic: Food Contamination

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Smartphone-based fluorescence Eu/Ce-MOFs hydrogel sensor for sensitive and visual detection of tetracyclines with machine learning-assistance.

Food chemistry
The excessive use of tetracyclines (TCs) poses a significant threat to human health, necessitating the development of convenient, rapid, and intelligent detection methods for monitoring TCs residues in food products. In this work, we present a hetero...

Machine learning-assisted spectroscopic methods for detecting adulteration in Barrantes wine from Folla Redonda grapes.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
The present study explores the application of advanced machine learning algorithms combined with vis-NIRS and FTIR spectroscopy to detect and quantify adulteration in Barrantes wine, produced from the Folla Redonda grape, a variety exclusive to the G...

Synergistic multi-level fusion framework of VNIR and SWIR hyperspectral data for soybean fungal contamination detection.

Food chemistry
Current methods for detecting soybean fungal contamination are often destructive, time-consuming, and labor-intensive. This study proposed an efficient approach by fusing visible and near-infrared (VNIR) and short-wave infrared (SWIR) hyperspectral i...

Applications of benchtop and portable spectroscopy techniques for food quality monitoring.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Food safety and quality have become major worldwide issues. Because of their quickness, effectiveness, and non-destructive nature, spectroscopy methods are essential for guaranteeing the safety and quality of food items. These techniques include Four...

Microbial and enzymatic strategies for aflatoxin control: Integrating intelligent detection and computational design.

Food chemistry
Aflatoxins (AFs), potent carcinogenic mycotoxins, pose a major global threat to human health. This review offers an in-depth summary of microorganisms capable of degrading AFs, including bacteria, probiotics, and fungi, and highlights the key enzymes...

Exploration of the fluorine-fluorine interaction mechanism in fluoroquinolone antibiotics recognition and ciprofloxacin detection on the basis of fluorine-doped carbon quantum dots and machine learning.

Food chemistry
The uncontrolled use of antibiotics poses a significant threat to human health and ecosystems. Accurate differentiation and trace detection of fluoroquinolone antibiotics (FQs) in foods are imperative. Fluorine-doped carbon quantum dots chelated with...

Direct detection and differentiation of the Vibrio harveyi clade using MALDI-TOF MS integrated with artificial intelligence for effective outbreak management.

Food chemistry
Accurate identification of Vibrio harveyi clade species is critical for seafood safety and the control of aquaculture diseases. However, existing methods demonstrate limited classification performance. This study presents an artificial intelligence-a...

CRISPR/Cas-Based Biosensing Strategies for Non-Nucleic Acid Contaminants in Food Safety: Status, Challenges, and Perspectives.

Journal of agricultural and food chemistry
Non-nucleic acid targets (non-NATs), such as heavy metals, toxins, and pesticide residues, pose critical threats to food safety. Although CRISPR/Cas systems were initially developed for nucleic acid detection, recent advances have enabled their adapt...

Detection and quantification of formaldehyde adulteration in cow and buffalo milk using UV-Vis-NIR spectroscopy with machine learning.

Food chemistry
This work uses UV-Vis-NIR spectroscopy (200-1700 nm), spectral preprocessing, principal component analysis (PCA), and machine learning (ML) to identify and quantify formalin adulteration in cow and buffalo milk. Formalin was added to milk at various ...

Application of MALDI-TOF MS-based peptidome profiling for the identification of Bacillus cereus, Staphylococcus aureus, and Escherichia coli in single and mixed inoculum.

Food chemistry
The detection of mixed-species bacterial samples plays a vital role in ensuring food safety, yet research in this area remains notably limited. This study investigates the integration of MALDI-TOF MS-derived peptidome profiles with artificial intelli...