AIMC Topic: Seafood

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A FPGA based recurrent neural networks-based impedance spectroscopy system for detection of YAKE in tuna.

Scientific reports Jul 1, 2025
This paper evaluates the use of impedance spectroscopy combined with artificial intelligence. Both technologies have been widely used in food classification and it is proposed a way to improve classifications using recurrent neural networks that trea...
Neural Networks, Computer Animals Dielectric Spectroscopy Tuna Recurrent Neural Networks Food Analysis Seafood
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SwinFishNet: A Swin Transformer-based approach for automatic fish species classification using transfer learning.

PloS one May 20, 2025
The fish market is a crucial industry for both domestic economies and the global seafood trade. Accurate fish species classification (FSC) plays a significant role in ensuring sustainability, improving food safety, and optimizing market efficiency. T...
Seafood Image Processing, Computer-Assisted Fishes Animals Deep Learning Algorithms
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Quantitative analysis and visualization of chemical compositions during shrimp flesh deterioration using hyperspectral imaging: A comparative study of machine learning and deep learning models.

Food chemistry Mar 29, 2025
The current work explores hyperspectral imaging (HSI) to quantitatively identify changes in TVB-N and K value during shrimp flesh deterioration. The work developed low-level data fusion (LLF) and predictive models using both machine learning methods ...
Penaeidae Shellfish Deep Learning Seafood Hyperspectral Imaging Machine Learning Animals
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Deep-learning-assisted chemo-responsive alizarin red S-based hydrogel sensor for the rapid freshness sensing of aquatic product.

Food research international (Ottawa, Ont.) Mar 15, 2025
Rapid detection of freshness particularly in aquatic products demands efficient sensing strategies. Here, a novel deep-learning-assisted chemo-responsive alizarin red S-based hydrogel sensing platform was established for rapid freshness assay of aqua...
Deep Learning Food Analysis Hydrogen-Ion Concentration Animals Anthraquinones Seafood Alginates Fishes Hydrogels
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Hyperspectral imaging and deep learning for parasite detection in white fish under industrial conditions.

Scientific reports Nov 9, 2024
Parasites in fish muscle present a significant problem for the seafood industry in terms of both quality and health and safety, but the low contrast between parasites and fish tissue makes them exceedingly difficult to detect. The traditional method ...
Deep Learning Fish Diseases Animals Nematoda Fishes Hyperspectral Imaging Food Parasitology Seafood
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Nondestructive freshness prediction of large yellow croaker (Pseudosciaena crocea) using computer vision and machine learning techniques based on pupil color.

Journal of food science Oct 30, 2024
Conventional methods for evaluating of fish freshness based on physiological and biochemical methods are often destructive, complicated, and costly. This study aimed to predict the freshness of large yellow croaker which was sampled every second day ...
Perciformes Seafood Neural Networks, Computer Machine Learning Animals Algorithms Color
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Machine learning models to predict the bioaccessibility of parent and substituted polycyclic aromatic hydrocarbons (PAHs) in food: Impact on accurate health risk assessment.

Journal of hazardous materials Oct 9, 2024
Food intake is the primary pathway for polycyclic aromatic hydrocarbons (PAHs) to enter the human body. Once ingested, PAHs tend to accumulate, posing health risks. To accurately assess the risk of PAHs from food, concentrations of 10 parent PAHs (PP...
Shellfish Risk Assessment Food Contamination Seafood Polycyclic Aromatic Hydrocarbons Fishes Humans Machine Learning Animals
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Machine learning-enabled attapulgite/polyimide nanofiber composite aerogels-based colorimetric sensor array for real-time monitoring of balsa fish freshness.

Food chemistry Sep 20, 2024
This paper presents the development and application of attapulgite/polyimide nanofiber composite aerogels (ATP/PI NFAs) integrated with a range of acid-base indicators, fabricated using electrospinning and freeze-drying technologies. A detailed chara...
Machine Learning Animals Colorimetry Silicon Compounds Nanofibers Fishes Gels Magnesium Compounds Seafood
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Preventing illegal seafood trade using machine-learning assisted microbiome analysis.

BMC biology Sep 11, 2024
BACKGROUND: Seafood is increasingly traded worldwide, but its supply chain is particularly prone to frauds. To increase consumer confidence, prevent illegal trade, and provide independent validation for eco-labelling, accurate tools for seafood trace...
Seafood Commerce Animals Microbiota Bivalvia Machine Learning
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Exploring molecular mechanisms underlying changes in lipid fingerprinting of salmon (Salmo salar) during air frying integrating machine learning-guided REIMS and lipidomics analysis.

Food chemistry Aug 5, 2024
Lipid oxidation in air-fried seafood poses a risk to human health. However, the effect of a prooxidant environment on lipid oxidation in seafood at different air frying (AF) temperatures remains unknown. An integrated machine learning (ML) - guided R...
Cooking Oxidation-Reduction Hot Temperature Mass Spectrometry Lipid Metabolism Seafood Salmo salar Lipids Machine Learning Lipidomics Animals
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