AIMC Topic: Food Contamination

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Robust DEEP heterogeneous ensemble and META-learning for honey authentication.

Food chemistry
Food fraud raises significant concerns to consumer health and economic integrity, with the adulteration of honey by sugary syrups representing one of the most prevalent forms of economically motivated adulteration. This study presents a novel framewo...

Fingerprinting of Boletus bainiugan: FT-NIR spectroscopy combined with machine learning a new workflow for storage period identification.

Food microbiology
Food authenticity and food safety issues have threatened the prosperity of the entire community. The phenomenon of selling porcini mushrooms as old mixed with new jeopardizes consumer safety. Herein, nucleoside contents and spectra of 831 Boletus bai...

Artificial Intelligence Applied to Honey C NMR Data: A New Path for Honey Recognition.

Journal of agricultural and food chemistry
Besides food authentication control based on acknowledged analytical methods, nowadays there is a continuous attempt to develop new approaches for unambiguously differentiating distinct food commodities. In this regard, current tendencies involve Art...

Slice-Inference-Assisted Lightweight Small Object Detection Model for Holographic Digital Immunoassay Quantification.

Analytical chemistry
Sensitive and cost-effective detection methods utilizing portable equipment are crucial for applications in food safety inspection, environmental monitoring, and clinical diagnosis. In this study, we propose a sliced inference-assisted lightweight sm...

Machine Learning-Assisted Multicolor Fluorescence Assay for Visual Data Acquisition and Intelligent Inspection of Multiple Food Hazards Regardless of Matrix Interference.

ACS sensors
Regarding the significant health risks of pesticide residue in foods, while current sensors still suffer from limited efficiency and stability, as well as difficulties in qualitative identification and quantitative detection of mixtures, development ...

Machine learning-assisted Fourier transform infrared spectroscopy to predict adulteration in coriander powder.

Food chemistry
Coriander is a widely used spice, valued for its flavor, aroma, and nutritional benefits in various cuisines and food products. However, adulteration, such as the addition of sawdust, poses significant risks to food safety and authenticity. This stud...

Machine learning: An effective tool for monitoring and ensuring food safety, quality, and nutrition.

Food chemistry
The domains of food safety, quality, and nutrition are inundated with complex datasets. Machine learning (ML) has emerged as a powerful tool in food science, offering fast, accessible, and effective solutions compared with conventional methods. This ...

The fluorescence spectrum combined with a broad learning system to characterize the content of difenoconazole in cabbage.

Analytical methods : advancing methods and applications
Pesticide residue detection plays an important role in vegetable quality and food safety. In this work, we propose a method for detecting difenoconazole pesticide residues based on fluorescence spectroscopy technology and machine learning algorithms....

A machine learning multimodal profiling of Per- and Polyfluoroalkyls (PFAS) distribution across animal species organs via clustering and dimensionality reduction techniques.

Food research international (Ottawa, Ont.)
Per- and polyfluoroalkyl substances (PFAS) contamination in aquatic and terrestrial organisms poses significant environmental and health risks. This study quantified 15 PFAS compounds across various tissues (liver, kidney, gill, muscle, skin, lung, b...

Application of Bioinformatics and Machine Learning Tools in Food Safety.

Current nutrition reports
PURPOSE OF REVIEW: Food safety is a fundamental challenge in public health and sustainable development, facing threats from microbial, chemical, and physical contamination. Innovative technologies improve our capacity to detect contamination early an...