AIMC Topic: Food Contamination
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Ratiometric fluorescence sensor based on deep learning for rapid and user-friendly detection of tetracycline antibiotics.
Food chemistry
Apr 15, 2024
The detection of tetracycline antibiotics (TCs) in food holds great significance in minimizing their absorption within the human body. Hence, this study aims to develop a rapid, convenient, real-time, and accurate detection method for detecting antib...
Integrating transformer-based machine learning with SERS technology for the analysis of hazardous pesticides in spinach.
Journal of hazardous materials
Apr 3, 2024
This study introduces an innovative strategy for the rapid and accurate identification of pesticide residues in agricultural products by combining surface-enhanced Raman spectroscopy (SERS) with a state-of-the-art transformer model, termed SERSFormer...
Machine learning models for prediction of Escherichia coli O157:H7 growth in raw ground beef at different storage temperatures.
Meat science
Dec 30, 2023
Shiga toxin-producing Escherichia coli (STEC) can be life-threatening and lead to major outbreaks. The prevention of STEC-related infections can be provided by control measures at all stages of the food chain. The growth performance of E. coli O157:H...
Quantitative Rapid Magnetic Immunoassay for Sensitive Toxin Detection in Food: Non-Covalent Functionalization of Nanolabels vs. Covalent Immobilization.
Toxins
Dec 20, 2023
In this study, we present a novel and ultrasensitive magnetic lateral flow immunoassay (LFIA) tailored for the precise detection of zearalenone, a mycotoxin with significant implications for human and animal health. A versatile and straightforward me...
Rapid identification of counterfeited beef using deep learning-aided spectroscopy: Detecting colourant and curing agent adulteration.
Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association
Oct 5, 2023
The adulteration of meat products using colourants and curing agents has heightened concerns over food safety, thereby necessitating the development of advanced detection methods. This study introduces a deep-learning-based spectroscopic method for s...
Deep Learning-Based Quantitative Assessment of Melamine and Cyanuric Acid in Pet Food Using Fourier Transform Infrared Spectroscopy.
Sensors (Basel, Switzerland)
May 24, 2023
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
Dec 19, 2022
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
Dec 15, 2022
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
Sep 9, 2022
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
May 16, 2022
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 ...
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