Beef Tenderness Prediction by a Combination of Statistical Methods: Chemometrics and Supervised Learning to Manage Integrative Farm-To-Meat Continuum Data

Beef Tenderness Prediction by a Combination of Statistical Methods: Chemometrics and Supervised Learning to Manage Integrative Farm-To-Meat Continuum Data

This trial aimed to integrate metadata that spread over farm-to-fork continuum of 110 Protected Designation of Origin (PDO)Maine-Anjou cows and combine two statistical approaches that are chemometrics and supervised learning; to identify the potential predictors of beef tenderness analyzed using the...

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Bibliographic Details
Main Authors: Mohammed Gagaoua, Valérie Monteils, Sébastien Couvreur, Brigitte Picard
Format: Article
Language:English
Published: MDPI AG 2019-07-01
Series:Foods
Subjects:
beef tenderness
machine learning
farm-to-fork
carcass
rearing practices
decision trees
cows
Online Access:https://www.mdpi.com/2304-8158/8/7/274

Internet

https://www.mdpi.com/2304-8158/8/7/274

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