AIMC Topic: Food Quality

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Decoding the spectrum of meat quality: advances in hyperspectral imaging for multi-attribute analysis.

Food chemistry
Hyperspectral imaging (HSI) has emerged as a powerful non-destructive technique for evaluating fresh meat quality across multiple attributes simultaneously. This review critically examines recent advances in HSI applications for fresh beef, pork, and...

Leveraging pre-trained computer vision models for accurate classification of meat freshness.

Food chemistry
Increasing concerns about food quality and safety have led to research into ways to assess meat freshness. Advances in deep learning, particularly image classification, enable up new possibilities for fast and non-destructive methods of evaluating me...

Deep learning-based regression of food quality attributes using near-infrared spectroscopy and hyperspectral imaging: A review.

Food chemistry
Near-infrared (NIR) spectroscopy and hyperspectral imaging (HSI) are two popular non-destructive tools for food quality and safety inspection. For food quality attributes quantification, the key is to develop regression models to link the features (s...

Rapid detection of food quality indicators using ELM and near-infrared spectroscopy.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
To achieve rapid, non-destructive detection of food quality indicators, this study introduces a novel method that combines near-infrared (NIR) spectroscopy with the Extreme learning machine (ELM) model. Eight spectral preprocessing methods and three ...

Deep learning-based multimodal fusion for quality prediction of chili paste using hyperspectral imaging and near-infrared spectroscopy.

Food chemistry
A deep learning-based intelligent multimodal system was developed to non-destructively evaluate chili paste quality by fusing color features extracted from hyperspectral images acquired by Hyperspectral Imaging (HSI), spectral features derived from H...

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...

Automated seafood freshness detection and preservation analysis using machine learning and paper-based pH sensors.

Scientific reports
Seafood, including fish, prawns and various marine products, is a critical component of global nutrition due to its high protein content, essential fatty acids, vitamins and minerals. Traditional methods for assessing seafood freshness such as sensor...

Artificial intelligence-driven food quality prediction: Applying machine learning ensemble models for dynamic forecasting of pork pH and meat color changes.

Food chemistry
This study presents a food chemistry-driven approach to predict post-slaughter pork quality dynamics, focusing on the biochemical mechanisms governing pH evolution and meat color development over 48 h. The interconversion of myoglobin redox states an...

Low-field NMR-based deep learning for non-destructive quality assessment of frozen model foods.

Food chemistry
Gel model foods with 90 % and 80 % water content were prepared for non-destructive quality testing of samples frozen under gradient temperature conditions. First, the drip loss and textural indices of the frozen samples were measured, and the nucleat...

A Synergistic Approach Using Photoacoustic Spectroscopy and AI-Based Image Analysis for Post-Harvest Quality Assessment of Conference Pears.

Molecules (Basel, Switzerland)
This study presents a non-invasive approach to monitoring post-harvest fruit quality by applying CO laser photoacoustic spectroscopy (COLPAS) to study the respiration of "Conference" pears from local and commercially stored (supermarket) sources. Con...