dc.contributor.advisor | | |
dc.creator | Fernández Cabanás, Víctor Manuel | es |
dc.creator | Pérez Martín, Dolores C. | es |
dc.creator | Fearn, Tom | es |
dc.creator | Gonçalves de Abreu, Joadil | es |
dc.date.accessioned | 2022-09-29T13:57:53Z | |
dc.date.available | 2022-09-29T13:57:53Z | |
dc.date.issued | 2022 | |
dc.identifier.citation | Fernández Cabanás, V.M., Pérez Martín, D.C., Fearn, T. y Gonçalves de Abreu, J. (2022). Optimisation of the predictive ability of NIR models to estimate nutritional parameters in elephant grass through LOCAL algorithms. Science Direct, 2022 (285) (2022 (121922)), 1 p.-9 p.. | |
dc.identifier.uri | https://hdl.handle.net/11441/137481 | |
dc.description.abstract | Elephant grass is a tropical forage widely used for livestock feed. The analytical techniques traditionally used for its
nutritional evaluation are costly and time consuming. Alternatively, Near Infrared Spectroscopy (NIRS) technology
has been used as a rapid analysis technique. However, in crops with high variability due to genetic improvement,
predictive models quickly lose accuracy and must be recalibrated. The use of non-linear models such as LOCAL
calibrations could mitigate these issues, although a number of parameters need to be optimized to obtain accurate
results. The objective of this work was to compare the predictive results obtained with global NIRS calibrations and
with LOCAL calibrations, paying special attention to the configuration parameters of the models.
The results obtained showed that the prediction errors with the LOCAL models were between 1.6 and 17.5 %
lower. The best results were obtained in most cases with a low number of selected samples (n = 100–250) and a
high number of PLS terms (n = 20). This configuration allows a reduced computation time with high accuracy,
becoming a valuable alternative for analytical determinations that require ruminal fluid, which would improve
the welfare of the animals by avoiding the need to surgically prepare animals to estimate the nutritional value of
the feeds. | es |
dc.format | application/pdf | es |
dc.format.extent | 9 p. | es |
dc.language.iso | eng | es |
dc.publisher | ELSEVIER | es |
dc.relation.ispartof | Science Direct, 2022 (285) (2022 (121922)), 1 p.-9 p.. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Near infrared | es |
dc.subject | LOCAL regression | es |
dc.subject | Pennisetum purpureum | es |
dc.title | Optimisation of the predictive ability of NIR models to estimate nutritional parameters in elephant grass through LOCAL algorithms | es |
dc.type | info:eu-repo/semantics/article | es |
dc.type.version | info:eu-repo/semantics/publishedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Agronomía | es |
dc.relation.publisherversion | https://www.sciencedirect.com/science/article/pii/S1386142522010708?via%3Dihub | es |
dc.identifier.doi | 10.1016/j.saa.2022.121922 | es |
dc.contributor.group | Universidad de Sevilla. AGR268: Naturación Urbana e Ingeniería de Biosistemas . | es |
dc.journaltitle | Science Direct | es |
dc.publication.volumen | 2022 (285) | es |
dc.publication.issue | 2022 (121922) | es |
dc.publication.initialPage | 1 p. | es |
dc.publication.endPage | 9 p. | es |
dc.identifier.sisius | 4233 | es |