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Detection of Bovine Mastitis in Raw Milk, Using a Low-Cost NIR Spectrometer and k-NN Algorithm
dc.contributor.author | Ramírez Morales, Iván | |
dc.contributor.author | Aguilar, Lenin | |
dc.contributor.author | Fernández-Blanco, Enrique | |
dc.contributor.author | Rivero, Daniel | |
dc.contributor.author | Pérez, Jhonny | |
dc.contributor.author | Pazos, A. | |
dc.date.accessioned | 2022-01-24T18:54:38Z | |
dc.date.available | 2022-01-24T18:54:38Z | |
dc.date.issued | 2021 | |
dc.identifier.citation | Ramirez-Morales, I.; Aguilar, L.; Fernandez-Blanco, E.; Rivero, D.; Perez, J.; Pazos, A. Detection of Bovine Mastitis in Raw Milk, Using a Low-Cost NIR Spectrometer and k-NN Algorithm. Appl. Sci. 2021, 11, 10751. https://doi.org/10.3390/app112210751 | es_ES |
dc.identifier.uri | http://hdl.handle.net/2183/29481 | |
dc.description | This article belongs to the Special Issue Applied Machine Learning in NIR Technology | es_ES |
dc.description.abstract | [Abstract] Among the bovine diseases, mastitis causes high economic losses in the dairy production system. Nowadays, detection under field conditions is mainly performed by the California Mastitis Test, which is considered the de facto standard. However, this method presents with problems of slowness and the expensiveness of the chemical-reactive process, which is deeply dependent on an expert’s trained eye and, consequently, is highly imprecise. The aim of this work is to propose a new method for bovine mastitis detection under field conditions. The proposed method uses a low-cost, smartphone-connected NIR spectrometer which solves the aforementioned problems of slowness, expert dependency and disposability of the chemical methods. This method uses spectra in combination with two k-Nearest Neighbors models. The first model is used to detect the presence of mastitis while the second model classifies the positive cases into weak and strong. The resulting method was validated by using a leave-one-out technique where the ground truth was obtained by the California Mastitis Test. The detection model achieved an accuracy of 92.4%, while the one classifying the severity showed an accuracy of 95%. | es_ES |
dc.description.sponsorship | This work is part of DINTA-UTMACH and RNASA-UDC research groups. This work is partially supported by Instituto de Salud Carlos III, grant number PI17/01826. It was also partially supported by different grants and projects from the Xunta de Galicia [ED431D 2017/23; ED431D 2017/16; ED431G/01; ED431C 2018/49; IN845D-2020/03]. Another source of support was the CYTED network (PCI2018\_093284) funded by the Spanish Ministry of Innovation and Science | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431D 2017/23 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431D 2017/16 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431G/01 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431C 2018/49 | es_ES |
dc.description.sponsorship | Xunta de Galicia; IN845D-2020/03 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | MDPI | es_ES |
dc.relation | info:eu-repo/grantAgreement/ISCIII/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013–2016/PI17%2F01826/ES/PROYECTO COLABORATIVO DE INTEGRACION DE DATOS GENOMICOS (CICLOGEN). TECNICAS DE DATA MINING Y DOCKING MOLECULAR PARA ANALISIS DE DATOS INTEGRATIVOS EN CANCER DE COLON/ | |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PCI2018-093284/ES/OBESIDAD Y DIABETES EN IBEROAMERICA: FACTORES DE RIESGO Y NUEVOS BIOMARCADORES PATOGENICOS Y PREDICTIVOS | |
dc.relation.uri | https://doi.org/10.3390/app112210751 | es_ES |
dc.rights | Atribución 4.0 Internacional | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | * |
dc.subject | Dairy | es_ES |
dc.subject | Health monitoring | es_ES |
dc.subject | California Mastitis Test | es_ES |
dc.subject | Machine learning | es_ES |
dc.subject | Near infrared reflected spectra | es_ES |
dc.title | Detection of Bovine Mastitis in Raw Milk, Using a Low-Cost NIR Spectrometer and k-NN Algorithm | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | Applied Sciences | es_ES |
UDC.volume | 11 | es_ES |
UDC.issue | 22 | es_ES |
UDC.startPage | 10751 | es_ES |
dc.identifier.doi | 10.3390/app112210751 |
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