AIMC Topic: Food Contamination

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Recent advances on artificial intelligence-based approaches for food adulteration and fraud detection in the food industry: Challenges and opportunities.

Food chemistry Dec 10, 2024
Food adulteration is the deceitful practice of misleading consumers about food to profit from it. The threat to public health and food quality or nutritional valuable make it a major issue. Food origin and adulteration should be considered to safegua...
Fraud Humans Artificial Intelligence Food Industry Food Quality Food Contamination
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Mass Spectrometry Imaging for Spatial Ingredient Classification in Plant-Based Food.

Journal of the American Society for Mass Spectrometry Dec 7, 2024
Mass spectrometry imaging (MSI) techniques enable the generation of molecular maps from complex and heterogeneous matrices. A burger patty, whether plant-based or meat-based, represents one such complex matrix where studying the spatial distribution ...
Machine Learning Food Contamination Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization Vegetables Food Ingredients Food Analysis Spectrometry, Mass, Electrospray Ionization
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Artificial intelligence-driven quantification of antibiotic-resistant Bacteria in food by color-encoded multiplex hydrogel digital LAMP.

Food chemistry Dec 4, 2024
Antibiotic-resistant bacteria pose considerable risks to global health, particularly through transmission in the food chain. Herein, we developed the artificial intelligence-driven quantification of antibiotic-resistant bacteria in food using a color...
Vegetables Fruit Hydrogels Food Contamination Drug Resistance, Bacterial Nucleic Acid Amplification Techniques Food Microbiology Molecular Diagnostic Techniques Artificial Intelligence Color Methicillin-Resistant Staphylococcus aureus Anti-Bacterial Agents Escherichia coli
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Using three-dimensional fluorescence spectroscopy and machine learning for rapid detection of adulteration in camellia oil.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy Dec 3, 2024
Camellia oil had been widely utilized in the realms of cooking, healthcare, and beauty. Nevertheless, merchants frequently adulterated pure camellia oil with low-priced oils to cut costs. This study was aimed at identifying the authenticity of camell...
Machine Learning Support Vector Machine Food Contamination Neural Networks, Computer Least-Squares Analysis Discriminant Analysis Camellia Plant Oils Spectrometry, Fluorescence
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Machine learning-driven fluorescent sensor array using aqueous CsPbBr perovskite quantum dots for rapid detection and sterilization of foodborne pathogens.

Journal of hazardous materials Nov 24, 2024
With the growing global concern over food safety, the rapid detection and disinfection of foodborne pathogens have become critical in public health. This study presents a novel machine learning-driven fluorescent sensor array utilizing aqueous CsPbBr...
Machine Learning Titanium Calcium Compounds Quantum Dots Food Contamination Limit of Detection Oxides Food Microbiology
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Detecting Honey Adulteration: Advanced Approach Using UF-GC Coupled with Machine Learning.

Sensors (Basel, Switzerland) Nov 23, 2024
This article introduces a novel approach to detecting honey adulteration by combining ultra-fast gas chromatography (UF-GC) with advanced machine learning techniques. Machine learning models, particularly support vector regression (SVR) and least abs...
Chromatography, Gas Food Contamination Machine Learning Honey Support Vector Machine
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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...
Spectrum Analysis, Raman Food Contamination Silver Beverages Thiram Metal Nanoparticles Fruit and Vegetable Juices Malus Neural Networks, Computer Algorithms
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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 Contamination Foodborne Diseases Humans Food Safety Artificial Intelligence
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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...
Food, Organic Food Quality Quality Control Metals, Heavy Machine Learning Food Contamination Algorithms Food Analysis
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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...
Machine Learning Animals Milk Cattle Hyperspectral Imaging Food Contamination
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