AIMC Topic: Food Contamination

Clear Filters Showing 31 to 40 of 166 articles

Machine Learning for Predicting Zearalenone Contamination Levels in Pet Food.

Toxins Dec 23, 2024
Zearalenone (ZEN) has been detected in both pet food ingredients and final products, causing acute toxicity and chronic health problems in pets. Therefore, the early detection of mycotoxin contamination in pet food is crucial for ensuring the safety ...
Machine Learning Animals Electronic Nose Pets Animal Feed Tandem Mass Spectrometry Volatile Organic Compounds Food Contamination Zearalenone
View on PubMed DOI

Rapid detection of microplastics in chicken feed based on near infrared spectroscopy and machine learning algorithm.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy Dec 20, 2024
The main objective of this study was to evaluate the potential of near infrared (NIR) spectroscopy and machine learning in detecting microplastics (MPs) in chicken feed. The application of machine learning techniques in building optimal classificatio...
Algorithms Discriminant Analysis Machine Learning Animals Animal Feed Spectroscopy, Near-Infrared Chickens Food Contamination Principal Component Analysis Microplastics Least-Squares Analysis Support Vector Machine
View on PubMed DOI

Impurity detection of premium green tea based on improved lightweight deep learning model.

Food research international (Ottawa, Ont.) Dec 15, 2024
Tea may be mixed with impurities during picking and processing, which can lower their quality. At present, the sorting of impurities in premium green tea mainly relies on manual labor, which is inefficient. In response to the technical challenges in ...
Deep Learning Food Contamination Tea
View on PubMed DOI

Ensemble learning-assisted quantitative identifying influencing factors of cadmium and arsenic concentration in rice grain based multiplexed data.

Journal of hazardous materials Dec 14, 2024
Rapid and accurate prediction of rice Cd (rCd) and rice As (rAs) bioaccumulation are important for assessing the safe utilization of rice. Currently, there is lack of comprehensive and systematic exploration of the factors of rCd and rAs. Herein, ens...
Ensemble Learning Oryza Cadmium Environmental Monitoring Food Contamination Arsenic Machine Learning Edible Grain Soil Soil Pollutants China
View on PubMed DOI

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...
Food Quality Fraud Humans Food Contamination Food Industry Artificial Intelligence
View on PubMed DOI

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 Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization Vegetables Food Ingredients Food Contamination Food Analysis Spectrometry, Mass, Electrospray Ionization
View on PubMed DOI

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...
Hydrogels Vegetables Color Fruit Escherichia coli Food Contamination Anti-Bacterial Agents Drug Resistance, Bacterial Artificial Intelligence Food Microbiology Methicillin-Resistant Staphylococcus aureus Molecular Diagnostic Techniques Nucleic Acid Amplification Techniques
View on PubMed DOI

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...
Neural Networks, Computer Machine Learning Spectrometry, Fluorescence Least-Squares Analysis Discriminant Analysis Food Contamination Plant Oils Support Vector Machine Camellia
View on PubMed DOI

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...
Calcium Compounds Oxides Machine Learning Titanium Food Contamination Food Microbiology Quantum Dots Limit of Detection
View on PubMed DOI

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...
Food Contamination Machine Learning Honey Support Vector Machine Chromatography, Gas
View on PubMed DOI
Popular Topics
Recent Journals