Acceptability of Artificial Intelligence in Poultry Processing and Classification Efficiencies of Different Classification Models in the Categorisation of Breast Fillet Myopathies

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41210%2F21%3A85683" target="_blank" >RIV/60460709:41210/21:85683 - isvavai.cz</a>

Result on the web

<a href="https://www.frontiersin.org/articles/10.3389/fphys.2021.712649/full" target="_blank" >https://www.frontiersin.org/articles/10.3389/fphys.2021.712649/full</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3389/fphys.2021.712649" target="_blank" >10.3389/fphys.2021.712649</a>

Result language

angličtina

Original language name

Acceptability of Artificial Intelligence in Poultry Processing and Classification Efficiencies of Different Classification Models in the Categorisation of Breast Fillet Myopathies

Original language description

Breast meat from modern fast-growing big birds is affected with myopathies such as woody breast (WB), white striping, and spaghetti meat (SM). The detection and separation of the myopathy-affected meat can be carried out at processing plants using technologies such as bioelectrical impedance analysis (BIA). However, BIA raw data from myopathy-affected breast meat are extremely complicated, especially because of the overlap of these myopathies in individual breast fillets and the human error associated with the assignment of fillet categories. Previous research has shown that traditional statistical techniques such as ANOVA and regression, among others, are insufficient in categorising fillets affected with myopathies by BIA. Therefore, more complex data analysis tools can be used, such as support vector machines (SVMs) and backpropagation neural networks (BPNNs), to classify raw poultry breast myopathies using their BIA patterns, such that the technology can be beneficial for the poultry industry in

Czech name

—

Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

30105 - Physiology (including cytology)

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2021

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Frontiers in Physiology

ISSN

1664-042X

e-ISSN

1664-042X

Volume of the periodical

12

Issue of the periodical within the volume

Sep

Country of publishing house

CH - SWITZERLAND

Number of pages

16

Pages from-to

1-16

UT code for WoS article

000703931700001

EID of the result in the Scopus database

2-s2.0-85116437045