Infrared spectroscopy coupled with machine learning algorithms for predicting the detailed milk mineral profile in dairy cattle.
Food Chem
; 461: 140800, 2024 Dec 15.
Article
em En
| MEDLINE
| ID: mdl-39163724
ABSTRACT
Milk minerals are not only essential components for human health, but they can be informative for milk quality and cow's health. Herein, we investigated the feasibility of Fourier Transformed mid Infrared (FTIR) spectroscopy for the prediction of a detailed panel of 17 macro, trace, and environmental elements in bovine milk, using partial least squares regression (PLS) and machine learning approaches. The automatic machine learning significantly outperformed the PLS regression in terms of prediction performances of the mineral elements. For macrominerals, the R2 ranged from 0.59 to 0.78. Promising predictability was achieved for Cu and B (R2 = 0.66 and 0.74, respectively) and more moderate ones for Fe, Mn, Zn, and Al (R2 from 0.48 to 0.58). These results provide a reliable basis for a rapid and cost-effective quantification of these traits, serving as a resource for dairy farmers seeking to enhance the quality of milk production and optimize cheese properties.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Leite
/
Aprendizado de Máquina
/
Minerais
Limite:
Animals
Idioma:
En
Revista:
Food Chem
Ano de publicação:
2024
Tipo de documento:
Article