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

Abstract

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.

Infrared Spectrometry as a High-Throughput Phenotyping Technology to Predict Complex Traits in Livestock Systems

“…Mastitis is the most common and costly contagious disease in dairy cattle characterized as an inflammation of the mammary gland and udder tissue. To the best of our knowledge, only Rienesl et al (2019) investigated the possibility of using milk spectra to predict mastitis, which reported satisfactory accuracy (0.68) in the validation data set ( Table 11 ). Mineur et al (2017) and Bonfatti et al (2020) investigated the ability of milk spectra data as a predictor of lameness and the predictions were poor to be employed as an on-farm tool to detect lameness in cows ( Table 11 ).…”

Prediction of pregnancy state from milk mid-infrared (MIR) spectroscopy in dairy cows

“…The 212 selected data points contain most of the usable information after removal of areas known to be nonreproducible between instruments or non-informative due to strong water absorption. According to other relevant studies (Soyeurt et al, 2011, 2012, Grelet et al, 2016, Lainé et al, 2017, Mineur et al, 2017, Ho et al, 2019, Rienesl et al, 2019, first derivatives of selected spectra values (Savitzky-Golay-Filter) were taken. All further data preparation was done in Rstudio (R Development Core Team, 2008).…”

“…The standard deviations of indicators of model fit were typically very low in approach 1 (0.001 to 0.003) and from 0.001 to 0.050 in approach 2, depending on sample size in the respective class. Data sets, data processing and methodology were very similar to a study on mastitis detection from MIR spectroscopy of Rienesl et al (2019), carried out within the framework of the same project.…”

Identifying Health Status in Grazing Dairy Cows from Milk Mid-Infrared Spectroscopy by Using Machine Learning Methods

“…Physiological and behavioural changes in cows associated with lameness are also associated with changes in milk composition [ 6 ]. Moreover, changes in milk composition are associated with the metabolic status of cows and their health [ 7 ].…”

“…Bonfatti et al [ 11 ] used PLS-DA to predict lameness and obtained 65.7% in sensitivity and 56.1% in specificity for cows which were in early stage of lactation (days in milk ≤ 120) and using the MIR spectrum and animal factors (parity number, age at calving, days in milk, breeding values and type traits). Additionally, Rienesl et al [ 7 ] predicted mastitis from MIR spectra and obtained 60.5% in sensitivity and 70.8% in specificity using PLS-DA in cows, which had the herd-test day and diagnosis of mastitis in a period of ±7 days. These studies focused on using traditional statistical methods, such as PLS-DA, which is used to predict a categorical variable.…”