Article (Scientific journals)
A comparison of 4 different machine learning algorithms to predict lactoferrin content in bovine milk from mid-infrared spectra
Soyeurt, Hélène; Grelet, Clément; McParland, Sinead et al.
2020In Journal of Dairy Science, 103, p. 11585-11596
Peer Reviewed verified by ORBi
 

Files


Full Text
article.pdf
Publisher postprint (199.62 kB)
Download

All documents in ORBi are protected by a user license.

Send to



Details



Keywords :
milk; lactoferrin; infrared; lait; lactoferrine; infrarouge
Abstract :
[en] Lactoferrin (LF) is a glycoprotein naturally present in milk. Its content varies throughout lactation, but also with mastitis; therefore it is a potential additional indicator of udder health beyond somatic cell count. Condequently, there is an interest in quantifying this biomolecule routinely. First prediction equations proposed in the literature to predict the content in milk using milk mid-infrared spectrometry were built using partial least square regression (PLSR) due to the limited size of the data set. Thanks to a large data set, the current study aimed to test 4 different machine learning algorithms using a large data set comprising 6,619 records collected across different herds, breeds, and countries. The first algorithm was a PLSR, as used in past investigations. The second and third algorithms used partial least square (PLS) factors combined with a linear and polynomial support vector regression (PLS + SVR). The fourth algorithm also used PLS factors, but included in an artificial neural network with 1 hidden layer (PLS + ANN). The training and validation sets comprised 5,541 and 836 records, respectively. Even if the calibration prediction performances were the best for PLS + polynomial SVR, their validation prediction performances were the worst. The 3 other algorithms had similar validation performances. Indeed, the validation root mean squared error (RMSE) ranged between 162.17 and 166.75 mg/L of milk. However, the lower standard deviation of cross-validation RMSE and the better normality of the residual distribution observed for PLS + ANN suggest that this modeling was more suitable to predict the LF content in milk from milk mid-infrared spectra (R2v = 0.60 and validation RMSE = 162.17 mg/L of milk). This PLS +ANN model was then applied to almost 6 million spectral records. The predicted LF showed the expected relationships with milk yield, somatic cell score, somatic cell count, and stage of lactation. The model tended to underestimate high LF values (higher than 600 mg/L of milk). However, if the prediction threshold was set to 500 mg/L, 82% of samples from the validation having a content of LF higher than 600 mg/L were detected. Future research should aim to increase the number of those extremely high LF records in the calibration set.
Disciplines :
Animal production & animal husbandry
Food science
Author, co-author :
Soyeurt, Hélène  ;  Université de Liège - ULiège > Département GxABT > Modélisation et développement
Grelet, Clément ;  CRA-W
McParland, Sinead;  Teagasc
Calmels, Marion;  Seenovia
Coffey, Mike;  SRUC
Tedde, Anthony  ;  Université de Liège - ULiège > Département GxABT > Modélisation et développement
Delhez, Pauline ;  Université de Liège - ULiège > Département GxABT > Ingénierie des productions animales et nutrition
Dehareng, Frédéric;  CRA-W
Gengler, Nicolas  ;  Université de Liège - ULiège > Département GxABT > Ingénierie des productions animales et nutrition
Language :
English
Title :
A comparison of 4 different machine learning algorithms to predict lactoferrin content in bovine milk from mid-infrared spectra
Alternative titles :
[fr] Comparison de 4 algorithmes de machine learning pour prédire le contenu en lactoferrine dans le lait de vache
Publication date :
December 2020
Journal title :
Journal of Dairy Science
ISSN :
0022-0302
eISSN :
1525-3198
Publisher :
American Dairy Science Association, United States - Illinois
Volume :
103
Pages :
11585-11596
Peer reviewed :
Peer Reviewed verified by ORBi
Available on ORBi :
since 17 November 2020

Statistics


Number of views
96 (14 by ULiège)
Number of downloads
101 (8 by ULiège)

Scopus citations®
 
26
Scopus citations®
without self-citations
18
OpenCitations
 
12

Bibliography


Similar publications



Contact ORBi