AIMC Topic: Food Contamination

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Near-infrared spectroscopy combined with support vector machine for the identification of Tartary buckwheat (Fagopyrum tataricum (L.) Gaertn) adulteration using wavelength selection algorithms.

Food chemistry Oct 5, 2024
The frequent occurrence of adulterating Tartary buckwheat powder with crop flours in the market necessitates an urgent need for a simple analysis method to ensure the quality of Tartary buckwheat. This study employed near-infrared spectroscopy (NIRS)...
Algorithms Spectroscopy, Near-Infrared Discriminant Analysis Flour Fagopyrum Support Vector Machine Food Contamination Least-Squares Analysis
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Machine learning-assisted laccase-like activity nanozyme for intelligently onsite real-time and dynamic analysis of pyrethroid pesticides.

Journal of hazardous materials Sep 30, 2024
The intelligently efficient, reliable, economical and portable onsite assay toward pyrethroid pesticides (PPs) residues is critical for food safety analysis and environmental pollution traceability. Here, a fluorescent nanozyme Cu-ATP@ [Ru(bpy)] with...
Machine Learning Colorimetry Food Contamination Pyrethrins Pesticides Pesticide Residues Copper Laccase
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Adulteration detection of multi-species vegetable oils in camellia oil using Raman spectroscopy: Comparison of chemometrics and deep learning methods.

Food chemistry Sep 16, 2024
Oil adulteration is a global challenge in the production of high value-added natural oils. Raman spectroscopy combined with mathematical modeling can be used for adulteration detection of camellia oil (CAO). In this study, the advantages of tradition...
Deep Learning Spectrum Analysis, Raman Plant Oils Food Contamination Camellia Chemometrics
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Progress of machine learning-based biosensors for the monitoring of food safety: A review.

Biosensors & bioelectronics Sep 12, 2024
Rapid urbanization and growing food demand caused people to be concerned about food safety. Biosensors have gained considerable attention for assessing food safety due to selectivity, and sensitivity but poor stability inherently limits their applica...
Mycotoxins Pesticides Food Safety Metals, Heavy Biosensing Techniques Food Contamination Food Microbiology Humans Machine Learning
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Environmentally Sustainable Detection of Arsenic using Convolutional Neural Networks and Imidazole-Based Organic Probes: Application in Food Samples and Arsenic Album.

Chemical research in toxicology Sep 12, 2024
Arsenic contamination poses a significant health risk, particularly when it infiltrates water supplies. While current detection methods offer precise analysis, they often involve complex instrumentation not suitable for field use. This study presents...
Neural Networks, Computer Arsenic Colorimetry Food Contamination Food Analysis Imidazoles Molecular Structure
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Detection and quantification of groundnut oil adulteration with machine learning using a comparative approach with NIRS and UV-VIS.

Scientific reports Sep 9, 2024
Groundnut oil is known as a good source of essential fatty acids which are significant in the physiological development of the human body. It has a distinctive fragrant making it ideal for cooking which contribute to its demand on the market. However...
Machine Learning Spectroscopy, Near-Infrared Peanut Oil Food Contamination Principal Component Analysis Spectrophotometry, Ultraviolet Discriminant Analysis Palm Oil
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Machine learning supported single-stranded DNA sensor array for multiple foodborne pathogenic and spoilage bacteria identification in milk.

Food chemistry Sep 6, 2024
Ensuring food safety through rapid and accurate detection of pathogenic bacteria in food products is a critical challenge in the food supply chain. In this study, a non-specific optical sensor array was proposed for the identification of multiple pat...
Cattle Biosensing Techniques Bacteria Machine Learning Animals Milk DNA, Single-Stranded DNA, Bacterial Food Contamination Food Microbiology Neural Networks, Computer
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Fusion features of microfluorescence hyperspectral imaging for qualitative detection of pesticide residues in Hami melon.

Food research international (Ottawa, Ont.) Sep 4, 2024
Pesticide residues are identified as a significant food safety issue in Hami melons, and their rapid and accurate detection is deemed critical for ensuring public health. In response to the cumbersome procedures with existing chemical detection metho...
Dioxolanes Cucurbitaceae Triazoles Hyperspectral Imaging Pesticide Residues Discriminant Analysis Algorithms Pyrethrins Food Contamination Least-Squares Analysis Machine Learning
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Microwave detection technique combined with deep learning algorithm facilitates quantitative analysis of heavy metal Pb residues in edible oils.

Journal of food science Aug 13, 2024
The heavy metal content in edible oils is intricately associated with their suitability for human consumption. In this study, standard soybean oil was used as a sample to quantify the specified concentration of heavy metals using microwave sensing te...
Lead Plant Oils Neural Networks, Computer Metals, Heavy Deep Learning Microwaves Soybean Oil Algorithms Food Contamination
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Sága, a Deep Learning Spectral Analysis Tool for Fungal Detection in Grains-A Case Study to Detect Fusarium in Winter Wheat.

Toxins Aug 13, 2024
Fusarium head blight (FHB) is a plant disease caused by various species of the fungus. One of the major concerns associated with spp. is their ability to produce mycotoxins. Mycotoxin contamination in small grain cereals is a risk to human and anim...
Mycotoxins Triticum Deep Learning Plant Diseases Fusarium Edible Grain Fungicides, Industrial Food Contamination
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