Mid infrared spectroscopy and milk quality traits: a data analysis competition at the “International Workshop on Spectroscopy and Chemometrics 2021”
dc.contributor.author | Frizzarin, Maria | |
dc.contributor.author | Bevilacqua, Antonio | |
dc.contributor.author | Dhariyal, Bhaskar | |
dc.contributor.author | Domijan, Katarina | |
dc.contributor.author | Ferraccioli, Frederico | |
dc.contributor.author | Hayes, Elena | |
dc.contributor.author | Ifrim, Georgiana | |
dc.contributor.author | Konkowleska, Agnieszka | |
dc.contributor.author | Le Nguyễn, Thach | |
dc.contributor.author | Mbaka, Uche | |
dc.contributor.author | Ranzato, Giovanna | |
dc.contributor.author | Singh, Ashish | |
dc.contributor.author | Stefanucci, Marco | |
dc.contributor.author | Casa, Alessandro | |
dc.date.accessioned | 2021-09-06T13:47:58Z | |
dc.date.available | 2021-09-06T13:47:58Z | |
dc.date.issued | 2021-09-06 | |
dc.identifier.uri | http://hdl.handle.net/11019/2597 | |
dc.description | dataset | en_US |
dc.description.abstract | chemometric data analysis challenge has been arranged during the first edition of the “International Workshop on Spectroscopy and Chemometrics”, organized by the Vistamilk SFI Research Centre and held online in April 2021. The aim of the competition was to build a calibration model in order to predict milk quality traits exploiting the information contained in mid-infrared spectra only. Three different traits have been provided, presenting heterogeneous degrees of prediction complexity thus possibly requiring trait-specific modelling choices. In this paper the different approaches adopted by the participants are outlined and the insights obtained from the analyses are critically discussed. | en_US |
dc.language.iso | en | en_US |
dc.relation.ispartofseries | Chemometrics and Intelligent Laboratory Systems; | |
dc.rights | Attribution-NonCommercial-ShareAlike 4.0 International | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-sa/4.0/ | * |
dc.subject | Chemometrics | en_US |
dc.subject | Fourier transform mid-infrared spectroscopy | en_US |
dc.subject | machine learning | en_US |
dc.subject | milk quality | en_US |
dc.title | Mid infrared spectroscopy and milk quality traits: a data analysis competition at the “International Workshop on Spectroscopy and Chemometrics 2021” | en_US |
dc.type | Dataset | en_US |
dc.embargo.terms | 2021-09-06 | en_US |
dc.contributor.sponsor | Science Foundation Ireland | en_US |
dc.contributor.sponsor | Department of Agriculture, Food and the Marine | en_US |
dc.contributor.sponsor | European Union | en_US |
dc.contributor.sponsorGrantNumber | 16/RC/3835 | en_US |
dc.contributor.sponsorGrantNumber | SFI/12/RC/2289_P2) | en_US |
dc.contributor.sponsorGrantNumber | 18/SIRG/5562 | en_US |
dc.contributor.sponsorGrantNumber | 841882 | en_US |
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Livestock Systems [315]
Teagasc LIvestock Systems Department includes Dairy, Cattle and Sheep research.