Bayesian regression models outperform partial least squares methods for predicting milk components and technological properties using infrared spectral data

Journal of Dairy Science

Volumn 98, Issue 11, 2015, Pages 8133-8151

  Ferragina, A ^a   de los Campos, G ^b,c   Vazquez, A I ^b   Cecchinato, A ^a   Bittante, G ^a  

^a UNIVERSITY OF PADOVA   (Italy)

^b MICHIGAN STATE UNIVERSITY   (United States)

^c MICHIGAN STATE UNIVERSITY   (United States)

Author keywords

Bayesian method; Cheese yield; Fatty acid; Infrared spectroscopy; Milk trait

Indexed keywords

FATTY ACID; MILK PROTEIN;

ANALYSIS; ANIMAL; BAYES THEOREM; BOVINE; CALIBRATION; CHEESE; CHEMISTRY; FEMALE; INFRARED SPECTROSCOPY; LEAST SQUARE ANALYSIS; MILK; PRINCIPAL COMPONENT ANALYSIS; REGRESSION ANALYSIS; VETERINARY;

ANIMALS; BAYES THEOREM; CALIBRATION; CATTLE; CHEESE; FATTY ACIDS; FEMALE; LEAST-SQUARES ANALYSIS; MILK; MILK PROTEINS; PRINCIPAL COMPONENT ANALYSIS; REGRESSION ANALYSIS; SPECTROSCOPY, FOURIER TRANSFORM INFRARED;

EID: 84944051195     PISSN: 00220302     EISSN: 15253198     Source Type: Journal    
DOI: 10.3168/jds.2014-9143     Document Type: Article

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