AIMC Topic: Meat

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Safely and autonomously cutting meat with a collaborative robot arm.

Scientific reports
Labor shortages in the United States are impacting a number of industries including the meat processing sector. Collaborative technologies that work alongside humans while increasing production abilities may support the industry by enhancing automati...

Integrative deep learning framework predicts lipidomics-based investigation of preservatives on meat nutritional biomarkers and metabolic pathways.

Critical reviews in food science and nutrition
Preservatives are added as antimicrobial agents to extend the shelf life of meat. Adding preservatives to meat products can affect their flavor and nutrition. This review clarifies the effects of preservatives on metabolic pathways and network molecu...

Combining Feature Selection Techniques and Neurofuzzy Systems for the Prediction of Total Viable Counts in Beef Fillets Using Multispectral Imaging.

Sensors (Basel, Switzerland)
In the food industry, quality and safety issues are associated with consumers' health condition. There is a growing interest in applying various noninvasive sensorial techniques to obtain quickly quality attributes. One of them, hyperspectral/multisp...

CT image segmentation of meat sheep Loin based on deep learning.

PloS one
There are no clear boundaries between internal tissues in sheep Computerized Tomography images, and it is difficult for traditional methods to meet the requirements of image segmentation in application. Deep learning has shown excellent performance i...

Rapid identification of counterfeited beef using deep learning-aided spectroscopy: Detecting colourant and curing agent adulteration.

Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association
The adulteration of meat products using colourants and curing agents has heightened concerns over food safety, thereby necessitating the development of advanced detection methods. This study introduces a deep-learning-based spectroscopic method for s...

Post-harvest biocontrol of Salmonella Enteritidis on Chicken breast meat and Shell eggs using multiphage cocktail.

Veterinaria italiana
The study aimed to evaluate the efficacy of a phage cocktail to reduce Salmonella Enteritidis contamination on perishable food items viz. chicken breast meat and shell eggs using different concentrations. Initially, four bacteriophages €P54, €P59, €P...

Marker-Free Isoelectric Focusing Patterns for Identification of Meat Samples via Deep Learning.

Analytical chemistry
Isoelectric focusing (IEF) is a powerful tool for resolving complex protein samples, which generates IEF patterns consisting of multiplex analyte bands. However, the interpretation of IEF patterns requires the careful selection of isoelectric point (...

Toward sustainable culture media: Using artificial intelligence to optimize reduced-serum formulations for cultivated meat.

The Science of the total environment
When considering options for future foods, cell culture approaches are at the fore, however, culture media to support the process has been identified as a significant contributor to the overall global warming potential (GWP) and cost of cultivated me...

Microbiological Quality Estimation of Meat Using Deep CNNs on Embedded Hardware Systems.

Sensors (Basel, Switzerland)
Spectroscopic sensor imaging of food samples meta-processed by deep machine learning models can be used to assess the quality of the sample. This article presents an architecture for estimating microbial populations in meat samples using multispectra...

Measuring water holding capacity in pork meat images using deep learning.

Meat science
Water holding capacity (WHC) plays an important role when obtaining a high-quality pork meat. This attribute is usually estimated by pressing the meat and measuring the amount of water expelled by the sample and absorbed by a filter paper. In this wo...