Show simple item record

dc.contributor.authorFrizzarin, Maria
dc.contributor.authorGormley, Isobel Claire
dc.contributor.authorCasa, Alessandro
dc.contributor.authorMcParland, Sinéad
dc.date.accessioned2023-09-05T15:00:54Z
dc.date.available2023-09-05T15:00:54Z
dc.date.issued2021-12-11
dc.identifier.citationFrizzarin M, Gormley IC, Casa A, McParland S. Selecting Milk Spectra to Develop Equations to Predict Milk Technological Traits. Foods. 2021 Dec 11;10(12):3084. doi: 10.3390/foods10123084. PMID: 34945635; PMCID: PMC8700986.en_US
dc.identifier.urihttp://hdl.handle.net/11019/3260
dc.descriptionpeer-revieweden_US
dc.description.abstractIncluding all available data when developing equations to relate midinfrared spectra to a phenotype may be suboptimal for poorly represented spectra. Here, an alternative local changepoint approach was developed to predict six milk technological traits from midinfrared spectra. Neighbours were objectively identified for each predictand as those most similar to the predictand using the Mahalanobis distances between the spectral principal components, and subsequently used in partial least square regression (PLSR) analyses. The performance of the local changepoint approach was compared to that of PLSR using all spectra (global PLSR) and another LOCAL approach, whereby a fixed number of neighbours was used in the prediction according to the correlation between the predictand and the available spectra. Global PLSR had the lowest RMSEV for five traits. The local changepoint approach had the lowest RMSEV for one trait; however, it outperformed the LOCAL approach for four traits. When the 5% of the spectra with the greatest Mahalanobis distance from the centre of the global principal component space were analysed, the local changepoint approach outperformed the global PLSR and the LOCAL approach in two and five traits, respectively. The objective selection of neighbours improved the prediction performance compared to utilising a fixed number of neighbours; however, it generally did not outperform the global PLSR.en_US
dc.description.sponsorshipScience Foundation Ireland
dc.language.isoenen_US
dc.publisherMDPI AGen_US
dc.relation.ispartofseriesFoods;Vol 10
dc.rightsAttribution-NonCommercial-ShareAlike 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/*
dc.subjectlocal changepoint analysisen_US
dc.subjectmidinfrared spectroscopyen_US
dc.subjectneighboursen_US
dc.titleSelecting Milk Spectra to Develop Equations to Predict Milk Technological Traitsen_US
dc.typeArticleen_US
dc.identifier.doihttps://doi.org/10.3390/foods10123084
dc.contributor.sponsorScience Foundation Irelanden_US
dc.contributor.sponsorGrantNumber18/SIRG/5562en_US
dc.contributor.sponsorGrantNumber16/RC/3835 (VistaMilk)en_US
dc.source.volume10
dc.source.issue12
dc.source.beginpage3084
refterms.dateFOA2023-09-05T15:00:55Z
dc.source.journaltitleFoods


Files in this item

Thumbnail
Name:
foods-10-03084-v2.pdf
Size:
1.828Mb
Format:
PDF
Description:
main article

This item appears in the following Collection(s)

  • Livestock Systems [315]
    Teagasc LIvestock Systems Department includes Dairy, Cattle and Sheep research.

Show simple item record

Attribution-NonCommercial-ShareAlike 4.0 International
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-ShareAlike 4.0 International