Acta Univ. Agric. Silvic. Mendelianae Brun. 2019, 67, 1221-1226

https://doi.org/10.11118/actaun201967051221
Published online 2019-10-31

Mastitis Detection from Milk Mid-Infrared (MIR) Spectroscopy in Dairy Cows

Lisa Rienesl1, Negar Khayatzadeh1, Astrid Köck2, Laura Dale3, Andreas Werner3, Clément Grelet4, Nicolas Gengler5, Franz-Josef Auer6, Christa Egger-Danner2, Xavier Massart7, Johann Sölkner1

1University of Natural Resources and Life Sciences, Vienna (BOKU), Division of Livestock Sciences, Department of Sustainable Agricultural Systems, Gregor-Mendel-Strasse 33, A-1180 Vienna, Austria
2ZuchtData EDV-Dienstleistungen GmbH, Dresdner Straße 89/19, A-1200 Vienna, Austria
3Landesverband Baden-Württemberg für Leistungs- und Qualitätsprüfungen in der Tierzucht e.V. (LKV), Heinrich-Baumann Straße 1-3, 70190 Stuttgart, Germany
4Centre Wallon de Recherches Agronomiques (CRA-W), Chaussée de Namur 24, B-5030 Gembloux, Belgium
5Université de Liège (ULg), Gembloux Agro-Bio Tech, Passage des Déportés 8, B-5030 Gembloux, Belgium
6LKV Austria Gemeinnützige GmbH, Dresdner Straße 89/19, A-1200 Wien, Austria
7European Milk Recording (EMR), Rue des Champs Elysées 4, 5590 Ciney, Belgium

Received July 22, 2019
Accepted September 25, 2019

Mid-infrared (MIR) spectroscopy is the method of choice for the standard milk recording system, to determine milk components including fat, protein, lactose and urea. Since milk composition is related to health and metabolic status of a cow, MIR spectra could be potentially used for disease detection. In dairy production, mastitis is one of the most prevalent diseases. The aim of this study was to develop a calibration equation to predict mastitis events from routinely recorded MIR spectra data. A further aim was to evaluate the use of test day somatic cell score (SCS) as covariate on the accuracy of the prediction model. The data for this study is from the Austrian milk recording system and its health monitoring system (GMON). Test day data including MIR spectra data was merged with diagnosis data of Fleckvieh, Brown Swiss and Holstein Friesian cows. As prediction variables, MIR absorbance data after first derivatives and selection of wavenumbers, corrected for days in milk, were used. The data set contained roughly 600,000 records and was split into calibration and validation sets by farm. Calibration sets were made to be balanced (as many healthy as mastitis cases), while the validation set was kept large and realistic. Prediction was done with Partial Least Squares Discriminant Analysis, key indicators of model fit were sensitivity and specificity. Results were extracted for association between spectra and diagnosis with different time windows (days between diagnosis and test days) in validation. The comparison of different sets of predictor variables (MIR, SCS, MIR + SCS) showed an advantage in prediction for MIR + SCS. For this prediction model, specificity was 0.79 and sensitivity was 0.68 in time window -7 to +7 days (calibration and validation). Corresponding values for MIR were 0.71 and 0.61, for SCS they were 0.81 and 0.62. In general, prediction of mastitis performed better with a shorter distance between test day and mastitis event, yet even for time windows of -21 to +21 days, prediction accuracies were still reasonable, with sensitivities ranging from 0.50 to 0.57 and specificities remaining unchanged (0.71 to 0.85). Additional research to further improve prediction equation, and studies on genetic correlations among clinical mastitis, SCS and MIR predicted mastitis are planned.

Funding

This work was conducted within the COMET-Project D4Dairy (Digitalisation, Data integration, Detection and Decision support in Dairying). That is supported by BMVIT, BMDW and the provinces of Lower Austria and Vienna in the framework of COMET-Competence Centers of Excellent Technologies. The COMET program is handled by the FFG.

References

23 live references