AIMC Topic: Food Safety

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Future horizons of Raman spectroscopy in food science: Emerging techniques and innovations for enhanced analysis.

Food chemistry
Raman spectroscopy is a key technology for to ensure sustainable and efficient food safety practices owing to its non-invasive and sensitive molecular analysis. This review highlights the recent advancements in Raman-based techniques, which offer imp...

Nondestructive freshness recognition of chicken breast meat based on deep learning.

Scientific reports
Identifying chicken breast freshness is an important component of poultry food safety. Traditional methods for chicken breast freshness recognition suffer from issues such as high cost, difficulty in recognition, and low efficiency. In this study, th...

Applications of benchtop and portable spectroscopy techniques for food quality monitoring.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Food safety and quality have become major worldwide issues. Because of their quickness, effectiveness, and non-destructive nature, spectroscopy methods are essential for guaranteeing the safety and quality of food items. These techniques include Four...

CRISPR/Cas-Based Biosensing Strategies for Non-Nucleic Acid Contaminants in Food Safety: Status, Challenges, and Perspectives.

Journal of agricultural and food chemistry
Non-nucleic acid targets (non-NATs), such as heavy metals, toxins, and pesticide residues, pose critical threats to food safety. Although CRISPR/Cas systems were initially developed for nucleic acid detection, recent advances have enabled their adapt...

MFDF-UNet: Multiscale feature depth-enhanced fusion network for colony adhesion image segmentation.

Journal of microbiological methods
Colony counting plays a crucial role in evaluating food quality and safety. The segmentation of colony adhesion images can significantly enhance the accuracy of food safety assessments. To achieve high-precision segmentation of colony adhesion images...

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...

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...

A novel approach for calculating food safety models and health risk assessments of potentially toxic elements (PTEs) in cow milk.

Food chemistry
This study introduces the Milk Quality Index (MQI), a novel metric for assessing milk quality that utilizes machine learning to enhance predictive accuracy. Lead (Pb) levels (318 ± 185 mg/kg) exceeded safety limits, with chromium (Cr), aluminum (Al),...

Hyperspectral Imaging and Deep Learning for Quality and Safety Inspection of Fruits and Vegetables: A Review.

Journal of agricultural and food chemistry
Quality inspection of fruits and vegetables linked to food safety monitoring and quality control. Traditional chemical analysis and physical measurement techniques are reliable, they are also time-consuming, costly, and susceptible to environmental a...

Progress in machine learning-supported electronic nose and hyperspectral imaging technologies for food safety assessment: A review.

Food research international (Ottawa, Ont.)
The growing concern over food safety, driven by threats such as food contaminations and adulterations has prompted the adoption of advanced technologies like electronic nose (e-nose) and hyperspectral imaging (HSI), which are increasingly enhanced by...