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dc.contributor.authorSoyeurt, H.
dc.contributor.authorGrelet, C.
dc.contributor.authorMcParland, Sinead
dc.contributor.authorCalmels, M.
dc.contributor.authorCoffey, M.
dc.contributor.authorTedde, A.
dc.contributor.authorDelhez, P.
dc.contributor.authorDehareng, F.
dc.contributor.authorGengler, N.
dc.date.accessioned2021-01-07T17:10:39Z
dc.date.available2021-01-07T17:10:39Z
dc.date.issued2020-10-22
dc.identifier.citationSoyeurt H, Grelet C, McParland S, Calmels M, Coffey M, Tedde A, Delhez P, Dehareng F, Gengler N. A comparison of 4 different machine learning algorithms to predict lactoferrin content in bovine milk from mid-infrared spectra. Journal of Dairy Science 2020; doi https://doi.org/10.3168/jds.2020-18870en_US
dc.identifier.issn0022-0302
dc.identifier.urihttp://hdl.handle.net/11019/2359
dc.descriptionpeer-revieweden_US
dc.description.abstractLactoferrin (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.en_US
dc.language.isoenen_US
dc.publisherAmerican Dairy Science Associationen_US
dc.relation.ispartofseriesJournal of Dairy Science;
dc.rightsAttribution-NonCommercial-ShareAlike 4.0 International*
dc.rights.urihttps://www.elsevier.com/tdm/userlicense/1.0/
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/*
dc.subjectFood Scienceen_US
dc.subjectMilken_US
dc.subjectLactoferrinen_US
dc.subjectMid infrareden_US
dc.subjectMachine learningen_US
dc.titleA comparison of 4 different machine learning algorithms to predict lactoferrin content in bovine milk from mid-infrared spectraen_US
dc.typeArticleen_US
dc.embargo.terms2021/10/22en_US
dc.identifier.doihttps://doi.org/10.3168/jds.2020-18870
dc.identifier.piiS0022030220308511
dc.contributor.sponsorEuropean Unionen_US
dc.contributor.sponsorGrantNumber211708en_US
dc.contributor.sponsorGrantNumberKBBE-2007-1en_US
dc.source.volume103
dc.source.issue12
dc.source.beginpage11585
dc.source.endpage11596
dc.source.journaltitleJournal of Dairy Science


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    Teagasc LIvestock Systems Department includes Dairy, Cattle and Sheep research.

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