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Updated: May 27, 2026

Exploring the Longissimus Muscle: Unraveling its Correlation with Meat Quality in Bos indicus and Crossbred Bulls

Exploring the Longissimus Muscle: Unraveling its Correlation with Meat Quality in Bos indicus and Crossbred Bulls

Published on: July 12, 2024

Artificial intelligence techniques point out differences in classification performance between light and standard

J Díez¹, A Bahamonde, J Alonso

¹SERIDA-CENSYRA-Somió, C/Camino de los Claveles 604, E-33203 Gijón (Asturias), Spain.

|November 9, 2011

Summary

This summary is machine-generated.

Artificial intelligence (AI) algorithms show different grading behaviors for light versus standard bovine carcasses. Reconsidering the SEUROP classification for light carcasses is recommended for standardization.

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Last Updated: May 27, 2026

Exploring the Longissimus Muscle: Unraveling its Correlation with Meat Quality in Bos indicus and Crossbred Bulls

Exploring the Longissimus Muscle: Unraveling its Correlation with Meat Quality in Bos indicus and Crossbred Bulls

Published on: July 12, 2024

Area of Science:

Animal Science
Artificial Intelligence
Agricultural Economics

Background:

The SEUROP (Standard European and Rumanian Points) system is the official bovine carcass classification method in the European Union.
Artificial Intelligence (AI) algorithms are increasingly used in agricultural applications, including carcass grading.
Understanding AI behavior in classifying different carcass types is crucial for accurate and repeatable grading.

Purpose of the Study:

To evaluate the validity of AI algorithms for grading light bovine carcasses using the SEUROP system.
To assess differences in AI classifier behavior between light and standard bovine carcasses.
To investigate the impact of AI grading on the repeatability of carcass classification.

Main Methods:

Three well-reputed Artificial Intelligence algorithms were employed for carcass grading.
Two training datasets were used: one with 65 light carcass classifications and another with 162 standard carcass classifications.
Each dataset included up to 28 attributes describing carcass conformation.

Main Results:

AI classifiers exhibited distinct grading behaviors for light versus standard carcasses.
Classifiers adhered more strictly to SEUROP rules for standard carcasses, focusing on profile and muscle development.
For light carcasses, AI classifiers incorporated attributes related to body size and skeletal development.

Conclusions:

A revision of the SEUROP classification system for light carcasses is suggested to standardize this market segment.
The differing conformation traits of light and standard carcasses may necessitate separate evaluation approaches.
Findings could influence sire evaluation programs aiming to improve carcass conformation scores using diverse market data.