AIMC Topic: Penaeidae

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Multidimensional strategy for discovering saltiness-enhancing peptides in shrimp heads integrating ultra-high pressure hydrolysis and machine learning.

Food chemistry
This study aims to develop a comprehensive strategy to investigate whether the integration of ultra-high pressure (UHP)-assisted enzymatic hydrolysis with machine learning and molecular docking can effectively identify salty peptides (SPs) from Litop...

An optimized domain-specific shrimp detection architecture integrating conditional GAN and weighted ensemble learning.

Scientific reports
Deep learning primarily operates on images which contain hidden patterns that are quantified through pixel intensities. Deep learning is used to analyze the image patterns and to recognize the objects. The detection process includes the creation of l...

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...

Assessing the effects of a 660 nm diode laser on crustacean eyes.

PloS one
Sustainable management of crustacean fisheries requires accurate and timely data for population modelling, but many stocks are data deficient. To address this challenge, a novel device using Class 3R 660 nm diode lasers and Artificial Intelligence al...

Rapid and chemical-free technique based on hyperspectral imaging combined with artificial intelligence for monitoring quality and shelf life of dried shrimp.

Food research international (Ottawa, Ont.)
A rapid and chemical-free method based on hyperspectral imaging (HSI) integrated with artificial intelligence (AI) for monitoring dried shrimp quality was developed. Dried shrimp was packaged in a polypropylene bag and chronologically monitored for c...

Microbiome determinants of productivity in aquaculture of whiteleg shrimp.

Applied and environmental microbiology
UNLABELLED: Aquaculture holds immense promise for addressing the food needs of our growing global population. Yet, a quantitative understanding of the factors that control its efficiency and productivity has remained elusive. In this study, we addres...

Characterization of Enterocytozoon hepatopenaei infection stages in shrimp using machine learning and gene network analysis.

Journal of invertebrate pathology
Enterocytozoon hepatopenaei (EHP), causing hepatopancreatic microsporidiosis (HPM), significantly impacts Litopenaeus vannamei, leading to economic losses. Using bioinformatics and machine learning, this study characterized EHP infection stages and h...

Deep Learning Used with a Colorimetric Sensor Array to Detect Indole for Nondestructive Monitoring of Shrimp Freshness.

ACS applied materials & interfaces
Intelligent colorimetric freshness indicator is a low-cost way to intuitively monitor the freshness of fresh food. A colorimetric strip sensor array was prepared by -dimethylaminocinnamaldehyde (PDL)-doped poly(vinyl alcohol) (PVA) and chitosan (Chit...

Machine learning and genetic algorithm-guided directed evolution for the development of antimicrobial peptides.

Journal of advanced research
INTRODUCTION: Antimicrobial peptides (AMPs) are valuable alternatives to traditional antibiotics, possess a variety of potent biological activities and exhibit immunomodulatory effects that alleviate difficult-to-treat infections. Clarifying the stru...

An advanced approach to identify antimicrobial peptides and their function types for penaeus through machine learning strategies.

BMC bioinformatics
BACKGROUND: Antimicrobial peptides (AMPs) are essential components of the innate immune system and can protect the host from various pathogenic bacteria. The marine environment is known to be one of the richest sources for AMPs. Effective usage of AM...