Estimation of somatic cell count levels of hard cheeses using physicochemical composition and artificial neural networks.
J Dairy Sci
; 102(2): 1014-1024, 2019 Feb.
Article
em En
| MEDLINE
| ID: mdl-30591330
ABSTRACT
This study addresses the prediction of the somatic cell counts of the milk used in the production of sheep cheese using artificial neural networks. To achieve this objective, the neural network was designed using 33 parameters of the physicochemical composition of the cheeses obtained after they have been matured for 12 mo as input data. The physicochemical analysis of the cheeses revealed that the somatic cell count level of the cheese has a significant influence on the amount of protein, fat, dry extract, and fatty acids. When properly set up, the neural network allows the correct classification of the cheeses (100% of correct results in both training and test phases) and therefore their samples in each of the 3 nominal output variables (low, average, and high somatic cell counts). The fatty composition of the cheeses, individual fatty acids, and fat acidity are the variables that most affect the correct operation of the neural network.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Contagem de Células
/
Queijo
/
Redes Neurais de Computação
/
Leite
Tipo de estudo:
Prognostic_studies
Limite:
Animals
Idioma:
En
Revista:
J Dairy Sci
Ano de publicação:
2019
Tipo de documento:
Article
País de afiliação:
Espanha