AIMC Topic: Food Contamination

Clear Filters Showing 41 to 50 of 230 articles

Rapid and non-destructive detection of formaldehyde adulteration in shrimp based on deep learning-assisted portable Raman spectroscopy.

Food chemistry
Formaldehyde (FA), a known carcinogen, is occasionally used illegally as a preservative in seafood, while traditional detection methods for FA residues often fail to meet the practical needs for nondestructive detection. In this study, a approach was...

Joint control and machine learning prediction of co-formation and kinetic profiles of typical hazardous Maillard reaction products by catechin treatment in air-fried potato chips.

Food chemistry
The Maillard reaction generates hazardous processing contaminants, including acrylamide (AA) and Nε-(carboxymethyl)lysine (CML), necessitating effective inhibitors. Here we use machine learning approaches to predict how catechin treatment reduces sim...

Analysis of food safety based on machine learning: A comprehensive review and future prospects.

Food chemistry
Food safety challenges escalate with global population growth and complex supply chains. Traditional analytical methods, though precise, face limitations in speed and adaptability. Machine learning (ML) offers data-driven solutions, excelling in cont...

Research progress on molecularly imprinted polymers (MIPs)-based sensors for the detection of organophosphorus pesticides.

Food chemistry
Organophosphorus pesticides (OPs), widely used in agriculture, have become major focuses of research in food safety and environmental pollution control due to their neurotoxicity and environmental persistence. In recent years, molecularly imprinted p...

Machine Learning-Enhanced Chemiresistive Sensors for Ultra-Sensitive Detection of Methanol Adulteration in Alcoholic Beverages.

ACS sensors
Methanol poisoning poses significant health risks, particularly in less developed countries, where adulterated alcoholic beverages often lead to severe morbidity and mortality. Current diagnostic methods, such as gas-liquid chromatography and blood g...

Colorimetric sensor arrays constructed by novel LTP-prepared amorphous CoO nanozymes with high laccase-like activity for the intelligent identification of phenolic compounds in food.

Food chemistry
Distinguishing between natural antioxidant phenolic compounds or contaminants in food matrices is crucial for ensuring food quality and safety. In this context, nanozyme-based sensors have emerged as promising tools, demonstrating significant potenti...

Insights Powered by Artificial Intelligence: Analyzing the Extent of Method Validation in Pesticide Residue Literature.

Journal of agricultural and food chemistry
Validation of analytical methods to assess figures of merit and other key performance parameters is a fundamental requirement within the fitness-for-purpose concept. By combining generative AI and subject matter review, this perspective article provi...

Multifunctional Hydrogen-Bonded Organic Frameworks for Intelligent Anti-Counterfeiting and Food Safety Monitoring.

ACS applied materials & interfaces
In an era of increasing digital threats and product counterfeiting, this study introduces MA-IPA@NPA, a groundbreaking hydrogen-bonded organic framework (HOF) material designed for advanced anticounterfeiting applications. This innovative material sh...

Simultaneous determination of pesticide residues and rapid discrimination of corn production origin using ambient ionization mass spectrometry combined with machine learning.

Food chemistry
Food traceability is a critical aspect of quality control and food safety. In this study, a high-throughput analysis system with an analysis time of 13 min was developed for the detection of pesticide residues in corn, achieving low limits of detecti...