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Meat

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Predictability of carcass traits in live Tan sheep by real-time ultrasound technology with least-squares support vector machines.

Animal science journal = Nihon chikusan Gakkaiho
This study aimed to investigate the performance of least-squares support vector machines to predict carcass characteristics in Tan sheep using noninvasive in vivo measurements. A total of 80 six-month-old Tan sheep (37 rams and 43 ewes) were examined...

Combining deep learning and fluorescence imaging to automatically identify fecal contamination on meat carcasses.

Scientific reports
Food safety and foodborne diseases are significant global public health concerns. Meat and poultry carcasses can be contaminated by pathogens like E. coli and salmonella, by contact with animal fecal matter and ingesta during slaughter and processing...

Mitigating spread of contamination in meat supply chain management using deep learning.

Scientific reports
Industry 4.0 recommends a paradigm shift from traditional manufacturing to automated industrial practices, especially in different parts of supply chain management. Besides, the Sustainable Development Goal (SDG) 12 underscores the urgency of ensurin...

New insights in improving sustainability in meat production: opportunities and challenges.

Critical reviews in food science and nutrition
Treating livestock as senseless production machines has led to rampant depletion of natural resources, enhanced greenhouse gas emissions, gross animal welfare violations, and other ethical issues. It has essentially instigated constant scrutiny of co...

Image based beef and lamb slice authentication using convolutional neural networks.

Meat science
Meat adulteration affects customers and the market. Existing meat authentication methods usually rely on special devices, and thus are limited to professional use only. Fake lamb or beef slices made from duck and fat appear in some Chinese hotpot res...

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...

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...

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...

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 (...

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...